NTNU 0 share This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. learning-based navigation system,, , Land: Learning to navigate from disengagements,, D.Hoeller, L.Wellhausen, F.Farshidian, and M.Hutter, Learning a state A page with further details is maintained at https://s.ntnu.no/oracle. feasible motions for high performance mobile robot navigation in complex The neural network is tasked to predict the collision cost of each. This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. In the UT, for a k-dimensional robot state, N=2k+1 sigma points, and their associated weights, are computed based on the mean value st and covariance t, . A set of contributions in a) planning with neural networks, b) planning under uncertainty and modeling uncertainty in deep neural networks, and c) planning with motion primitives relate to this work. The number of data points created by augmenting the remaining actions is such that the number of data points with no collision label and the number of data points with at least one collision label are almost equal. Figure7.1 demonstrates the effect of the filtering process presented in[41], which reduces the sim-to-real gap and also removes false obstacles. Assuming NMC forward passes through the ensemble of CNNs, and NMCN forward passes through the Combiner network can be run in parallel on the GPU, their complexities are O(1). Notably, a particular limitation of the depth sensor used in real-life was taken into account. mapping in real-time,, L.Han, F.Gao, B.Zhou, and S.Shen, Fiesta: Fast incremental euclidean The method is built upon the basis of the RRT* algorithm and takes advantages of fast motion primitive generation and collision checking for multicopters. 2011 IEEE International Conference on Robotics and Automation. octrees,, H.Oleynikova, Z.Taylor, M.Fehr, R.Siegwart, and J.Nieto, Voxblox: 2011 IEEE International Conference on Robotics and Automation. An uncertainty-aware model-based learning algorithm that estimates the probability of collision together with a statistical estimate of uncertainty is presented, and it is shown that the algorithm naturally chooses to proceed cautiously in unfamiliar environments, and increases the velocity of the robot in settings where it has high confidence. Since real-life depth images are often subject to several shortcomings compared to simulated data, including a) missing information, b) loss of detail, and c) depth noise[18], we perform an additional filtering step using the IP-Basic algorithm[41] to refine the depth frame and thus reduce the mismatch between the real and simulated depth images. system identification, and control for a cost effective open-source vtol Motion Primitives-based Navigation Planning using Deep Collision Prediction Huan Nguyen 0003 , Sondre Holm Fyhn , Paolo De Petris , Kostas Alexis . distance fields for online motion planning of aerial robots, in, M.Burri, H.Oleynikova, M.W. Achtelik, and R.Siegwart, Real-time Two real-life experiments were conducted using the LMF platform to evaluate the performance of ORACLE. The best action sequence is chosen and its first step is executed, while the whole procedure is repeated in a receding horizon fashion. Mantel and R. Siegwart, Three-dimensional A learning-based pipeline to realise local navigation with a quadrupedal robot in cluttered environments with static and dynamic obstacles is presented, which is trained to not only estimate the hidden state of the world in an unsupervised fashion, but also helps bridging the reality gap, enabling successful sim-to-real transfer. For efficient neural network forward pass and uncertainty estimation, we only perform MC dropout in the CNN branch and split the neural network shown in Figure3 into 3 parts, namely the CNN, Combiner, and Prediction networks. The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated linear and angular velocities of the robot. collision clearance estimator for fast robot motion planning., F.Furrer, M.Burri, M.Achtelik, and R.Siegwart, Rotors-a modular gazebo In 2022 International Conference on Robotics and Automation, ICRA 2022, Philadelphia, PA, USA, May 23-27, 2022. This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. Learning to fly by driving,, F.Sadeghi and S.Levine, CAD2RL: real single-image flight without a single F.Ruess, M.Suppa, and D.Burschka, Toward a fully autonomous uav: The neural network is 2022 International Conference on Robotics and Automation (ICRA) Motion Primitives-based Navigation Planning using Deep Collision Prediction Pages 9660-9667 PreviousChapterNextChapter ABSTRACT This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. Agha-mohammadi, Locus: This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. Both planners perform well in environment A with the average flight times almost equal to the timeout period. The ADS is operated by the Smithsonian Astrophysical Observatory under NASA Cooperative The algorithm incrementally constructs a graph of trajectories through state space, while efficiently searching over candidate paths through the graph at each iteration results in a search tree in belief space that provably converges to the optimal path. in, J.Ku, A.Harakeh, and S.L. Waslander, In defense of classical image Motion Primitives-based Navigation Planning using Deep Collision Prediction - Autonomous Robots Lab Motion Primitives-based Navigation Planning using Deep Collision Prediction This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. Specifically, to tackle the problem that depth cameras often present gaps in depth data when facing a reflective surface[45], we designed artificial obstacles having holes with sizes that could just fit our flying robot, or smaller, to enhance the trained networks tendency to avoid such extremely narrow or erroneously detected passages. Proceedings of Machine aerial robots,, T.Tomic, K.Schmid, P.Lutz, A.Domel, M.Kassecker, E.Mair, I.L. Grixa, primitive for quadrocopter trajectory generation,, B.T. Lopez and J.P. How, Aggressive 3-d collision avoidance for high-speed completion for robotic navigation,, P.DePetris, H.Nguyen, T.Dang, F.Mascarich, and K.Alexis, The final uncertainty-aware collision cost for an action sequence is given as: Specifically, we denote ^cuack as the uncertainty-aware collision cost of the kth action sequence akt:t+H in the MPL (k=1,,NMP). LMF inherited the collision-tolerant design of the Resilient Micro Flyer[46], yet with an increased diameter at 0.43m and a mass of 1.2kg. Astrophysical Observatory. Two environments are created to verify the methods performance, namely a) environment A (Figure4.ii) that contains obstacles seen during the training phase and the robot is given perfect state estimation, while b) environment B (Figure4.iii) contains unseen obstacles and the robot is given largely deteriorated state estimation sdett=[vdett,dett]T (specifically vdett=vt1.0m/s,dett=t+0.1rad/s). The primary contribution of this work is a new pyramid-based spatial partitioning method that enables rapid collision detection between candidate trajectories and the environment, and how a local planning algorithm can be run at high rates on computationally constrained hardware. This paper contributes a method to design a novel navigation planner learning for computer vision? in, C.Richter and N.Roy, Safe visual navigation via deep learning and novelty By splitting the prediction network into 3 parts, we can perform inference on the ensemble of CNNs, the Combiner and Prediction networks with different batch sizes of 1,NMCN, and NMCNNMP, respectively, avoiding the need to run the forward passes through the whole CPN NMCNNMP times. with optimal control as a supervisor,, G.Kahn, P.Abbeel, and S.Levine, Badgr: An autonomous self-supervised Graph-based subterranean exploration path planning using aerial and legged An uncertainty-aware model-based learning algorithm that estimates the probability of collision together with a statistical estimate of uncertainty is presented, and it is shown that the algorithm naturally chooses to proceed cautiously in unfamiliar environments, and increases the velocity of the robot in settings where it has high confidence. , ser. thermalinertial odometry, in, S.Zhang, Y.Wu, T.Che, Z.Lin, R.Memisevic, R.Salakhutdinov, and Y.Bengio, multimodal sensor fusion for resilient robot pose estimation in The developed planner is structured around motion primitives that search for admissible paths, taking advantage of efficient volumetric mapping with collision checks and future-safe path. environments, in, D.Gandhi, L.Pinto, and A.Gupta, Learning to fly by crashing, in, A.Loquercio, A.I. Maqueda, C.R. del Blanco, and D.Scaramuzza, Dronet: The RotorS simulator[43] is used to collect data for training the collision predictor network. Second, I perform path planning / local collision avoidance. By clicking accept or continuing to use the site, you agree to the terms outlined in our. A motion primitives library[29] can be generated by either sampling the vehicles configuration space or its control space[30]. Motion Primitives-based Navigation Planning using Deep Collision Prediction - YouTube This work contributes a method to design a novel navigation planner exploiting a learning-based. Figure2). Motion Primitives-based Navigation Planning using Deep Collision Prediction Tailored to the combined need for large-scale exploration of challenging and confined environments, despite the limited . Relevant methods for dynamic system identification of MAVs are presented in[44]. By clicking accept or continuing to use the site, you agree to the terms outlined in our. integrated depth sensors (rappids): A fast planner for multicopter model uncertainty in deep learning, in. model uncertainty estimates, in, A.Loquercio, M.Segu, and D.Scaramuzza, A general framework for uncertainty To investigate the dynamic behavior of a mooring buoy installed in an open shore, a simplified simulation model is developed, which consists of the surging and heaving motions of a mooring buoy. 2020 IEEE International Conference on Robotics and Automation (ICRA). mav, in. Let a) NMC,NMP be the number of dropout masks and action sequences in the MPL, respectively, and b) coln,coln(n=1,,NMC) the variance and mean of the predicted collision cost of at:t+H, estimated by the UT with different dropout masks of the CNN part. On the other hand, the inference time of the Prediction network is dominated by the inference time of the LSTM network and has the complexity of O(H) [48], assuming that NMCNNMP forward passes through it can be run in parallel on the GPU. Based on the collision probability of each action sequence, the safest motion primitives are chosen and the corresponding goal-reaching costs are calculated to determine the best action in a receding horizon fashion. A fast neural network collision checking heuristic, ClearanceNet, is presented and incorporated within a planning algorithm, CN-RRT, which achieves speedups of more than two orders of magnitude over traditional collision detection methods. In the second experiment, LMF traveled a distance of 110m in a narrow underground environment, which is particularly challenging since the depth image data is not perfect, and the uncertainty in the estimated partial state of the robot is not negligible due to the fairly dark conditions which lead to relatively weak features. At any time, the goal direction is commanded to be the current heading of the robot which in turn allows the system to pick the safe direction that deviates the least from the current heading direction of the robot in this unknown environment. order to determine the best action sequence to execute in a receding horizon This work contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. Huan Nguyen 0003, Sondre Holm Fyhn, Paolo De Petris, Kostas Alexis. B. Loje, D. H. Nguyen, N. reinforcement learning for collision avoidance,, B.Ltjens, M.Everett, and J.P. How, Safe reinforcement learning with Dear community members, The depicted work (video with explanation is provided in the link) contributes a method to design a novel navigation planner Press J to jump to the feed. Specifically, the sooner the collision event is predicted to happen, the more it will contribute to the final collision cost: To account for the uncertainty of st, which cannot be negligible - especially in fast flight or within perceptually degraded environments - we utilize the Unscented Transform (UT)[25] to approximately propagate the uncertainty in st to the predicted collision cost ^ccol of an action sequence at:t+H. A multi-sensor lidar-centric solution for high-precision odometry and 3d 2017 IEEE International Conference on Robotics and Automation (ICRA). In further detail, the filtered depth image ot of size 270480, is processed by a Convolutional Neural Network (CNN) branch, while the estimated partial state of the robot is processed by a Fully-Connected (FC) network before concatenating it with the output feature vector from the CNN branch. After calculating the uncertainty-aware predicted collision cost for each action sequence in the motion primitives library, as described in sectionIV-C, the minimum collision cost ^cuacmin of all action sequences is calculated, and all the action sequences having collision cost greater than ^cuacmin+cth, where cth is a set positive threshold, are discarded. The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated linear and angular velocities of the robot. The concatenated vector is then fed to another FC network before being used as the initial hidden state of a Long Short-Term Memory (LSTM) network. In this context, ensuring collision-free navigation can be a gruelling task and remains an open problem. This paper presents a sampling-based method that can exploit collisions for better motion planning. Additionally, the model uncertainty - which can be significant in novel environments - can be captured by performing MC dropout for the network as in[28]. is the function that wraps an angle in radians to [,]. About Press Copyright Contact us Creators Advertise Developers Terms Privacy Policy & Safety How YouTube works Test new features Press Copyright Contact us Creators . Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. Uncertainty propagation through deep neural 2016, pp. S.Khattak, H.Nguyen, F.Mascarich, T.Dang, and K.Alexis, Complementary The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the . The input to the LSTM cells is generated by the velocity-steering angle action sequence provided by the motion primitives library, while the outputs of the LSTM cells are passed through a FC network to predict the collision label at each time step. To collect data for predicting collision probabilities at the future time steps, an action sequence with random vr and r within the sensors FOV is drawn and is fully executed. Khedekar, and K. Alexis, Motion primitives-based path planning for fast exploiting a learning-based collision prediction network. representation and navigation in cluttered and dynamic environments,, A.Bry and N.Roy, Rapidly-exploring random belief trees for motion planning It integrates a Realsense D455 to obtain depth data, a nadir-facing TFmini 1D range sensor, a PixRacer PX4-based autopilot delivering attitude and thrust control, and a Realsense T265 fused with the IMU of the autopilot allowing it to estimate the speed, orientation and angular rate of the robot. cost is then combined with the goal direction given by a global planner in environments,, A.Hornung, K.M. Wurm, M.Bennewitz, C.Stachniss, and W.Burgard, Motion Primitives-based Navigation Planning using Deep Collision Prediction. nonlinear systems, in. To demonstrate the method, we develop a resilient small flying robot 48. The total number of collisions, and the average flight time before a collision are reported in TableI. variational inference, 2016. 10 PDF View 1 excerpt, references methods Search-Based Motion Planning for Aggressive Flight in SE (3) Energy Approximations Considering Angle Difference for Heuristic Function, Learning to Navigate: Exploiting Deep Networks to Inform Sample-Based In the first experiment, illustrated in Figure6, LMF is tasked to follow the goal directions given by a global planner, while navigating safely in a corridor filled with various types of obstacles that were not seen during training. S.Leutenegger, Volumetric occupancy mapping with probabilistic depth state by utilizing the Unscented Transform and the uncertainty of the neural the robot. One training data point d is recorded every time the robot moves more than th meters, having the format d=(ot,st,at:t+H,ccolt+1:t+H+1) where ccolt+1:t+H+1=[ccolt+1,ccolt+2,,ccolt+H],ccolt+i denotes the collision label at time step t+i,i=1,,H (equal to 1 for collision and 0 for non-collision status). Moreover, when using deep neural networks for making predictions, there are two kinds of uncertainty that need to be considered: aleatoric uncertainty which captures noise inherent in the observations, and epistemic uncertainty which accounts for model uncertainty[20], . Employing a data-driven approach to allow collision-free navigation without building the map explicitly and without assuming that the robot has access to an accurate estimate of its position, we propose a local mOtion pRimitives-bAsed navigation planner using a deep CoLlision prEdictor (ORACLE) that further interfaces and accounts for the goal direction of the robot as provided by a global planner. real image, in, V.Tolani, S.Bansal, A.Faust, and C.Tomlin, Visual navigation among humans subterranean environments, in, L.Tai, J.Zhang, M.Liu, and W.Burgard, Socially compliant navigation Recent advances in the field of aerial robotics have enabled their utilization in various applications including industrial inspection[1, 2], search and rescue[3], surveillance[4], subterranean exploration[5], and more. aggressive flight in se(3),, M. R. Dharmadhikari, T. Dang, L. Solanka, J. perceptually-challenging environments is conducted to evaluate the quality of To demonstrate the method, we develop a resilient small flying robot integrating lightweight sensing and computing resources. vehicles by trajectory smoothing using motion primitives,, T.Howard, C.Green, A.Kelly, and D.Ferguson, State space sampling of , Bayesian convolutional neural networks with bernoulli approximate The uncertainty-aware collision 2020 IEEE International Conference on Robotics and Automation (ICRA). through raw depth inputs with generative adversarial imitation learning, in, A.Kendall and Y.Gal, What uncertainties do we need in bayesian deep C.Kanellakis, L.Carlone, C.Guaragnella, and A.-a. (or is it just me), Smithsonian Privacy However, the method may not be applicable for propagating aleatoric uncertainty through recurrent neural networks. motion primitives library in the robot's velocity-steering angle space, given The total variance can then be expressed as: where col=1NMCNMCn=1coln. Available: I.Sa, M.Kamel, R.Khanna, M.Popovic, J.Nieto, and R.Siegwart, Dynamic A set of works have applied deep learning to the problem of autonomous robot navigation. The remainder of this paper is organized as follows: SectionII presents related work, followed by the problem statement in SectionIII. [Online]. In this work, a navigation planner based on a learning-based collision prediction network was proposed and experimentally verified using a custom-designed resilient micro flyer. A. This paper presents a novel path planning strategy for fast and agile exploration using aerial robots that provides fast collision-free and future-safe paths that maximize the expected exploration gain and ensure continuous fast navigation through the unknown environment. An effective algorithm for state space sampling utilizing a modelbased trajectory generation approach is presented that enables highspeed navigation in highly constrained and/or partially known environments such as trails, roadways, and dense offroad obstacle fields. under uncertainty, in, G.Kahn, A.Villaflor, V.Pong, P.Abbeel, and S.Levine, Uncertainty-aware Research platform for indoor and outdoor urban search and rescue,, B.Grocholsky, J.Keller, V.Kumar, and G.Pappas, Cooperative air and ground This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. Furthermore, we account for the uncertainty of the robot's partial state by utilizing the Unscented Transform and the uncertainty of the neural network model by using Monte Carlo dropout. In this work, we approximately propagate the robots state uncertainty through the prediction network using the Unscented Transform (UT). It generates collision Figure2 provides an overview of the methods architecture. Moreover, given that the robot dynamics are holonomic and effectively invariant to its heading orientation, we also perform horizontal flip data augmentation, specifically by adding the augmented data point dflip=(oflipt,sflipt,aflipt:t+H,ccolt+1:t+H+1), where oflipt is the horizontally flipped image of ot, sflipt and aflipt:t+H are created by changing the sign of t and rt+i(i=0,,H1) in st and at:t+H, respectively. Architectural complexity measures of recurrent neural networks, in, Exploiting collisions for sampling-based multicopter motion planning, A Receding Horizon Multi-Objective Planner for Autonomous Surface As a testbed for our learning-based navigation policy, we developed a new collision-tolerant aerial robot, called Learning-based Micro Flyer (LMF). A motion primitive-based, receding-horizon planning approach that maximizes information gain, accounts for platform dynamics, and ensures safe operation for rapid exploration of unknown environments using aerial robots is presented. As mentioned, ORACLE further considers the uncertainty of the robots partial state and the model uncertainty of the collision prediction network. A search-based trajectory planning algorithm that exploits the quadrotor maneuverability to generate sequences of motion primitives in cluttered environments and analyzes critical discretization parameters of motion primitive planning. Once I predict the position and orientation of the robot for the immediate step, I . Thus, ORACLE avoids the need for hand-engineered collision checking algorithms such as[34, 38] or access to a reconstructed map of the environment[39, 40]. Algorithm1 outlines ORACLEs key steps. Assuming only access to estimates of the robots linear and angular velocities, alongside the depth image, ORACLE identifies the next best sequence of actions (velocity-steering commands) that ensure that the system is navigating towards a goal heading direction, while avoiding the obstacles in its environment. C.L. Bottasso, D.Leonello, and B.Savini, Path planning for autonomous and agile exploration using aerial robots, in, N.Bucki, J.Lee, and M.W. Mueller, Rectangular pyramid partitioning using Motion Primitives-based Navigation Planning using #Deep Collision Prediction #AI #cognaize #datavisualization #software #neuralnetworks #robots. A motion primitive-based, receding-horizon planning approach that maximizes information gain, accounts for platform dynamics, and ensures safe operation for rapid exploration of unknown environments using aerial robots is presented. Siegwart, Structural inspection path planning via iterative viewpoint The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated linear and angular velocities of the robot. The inference time of the CPN includes the inference times of the ensemble of CNNs, the Combiner and Prediction networks. 2022 International Conference on Robotics and Automation (ICRA), This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. Motion Primitives-based Navigation Planning using Deep Collision Prediction | DeepAI Motion Primitives-based Navigation Planning using Deep Collision Prediction 01/10/2022 by Huan Nguyen, et al. The authors in[34] use configuration space primitives proposed in[35] to generate minimum-jerk primitives online, however, position feedback is required and not all generated trajectories are guaranteed to lie within the sensors FOV. networks,, Y.Gal and Z.Ghahramani, Dropout as a bayesian approximation: Representing Vehicles in Urban Waterways, Potential Gap: Using Reactive Policies to Guarantee Safe Navigation, Exact and Bounded Collision Probability for Motion Planning under 10501059. Accordingly, the naive planner is not using the UT samples and the ensemble of NNs (cf. Gaussian Uncertainty, Motion Primitives based Path Planning with Rapidly-exploring Random Tree, Real time A* Adaptive Action Set Footstep Planning with Human Locomotion The proposed mOtion pRimitives-bAsed navigation planner using a deep CoLlision prEdictor (ORACLE) is detailed in this section. In 2022 International Conference on Robotics and Automation, ICRA 2022, Philadelphia, PA, USA, May 23-27, 2022 . Learning Research, M.F. Balcan and K.Q. Weinberger, Eds., vol. task dataset model metric name metric value global rank remove We assume that there is a global planner providing goal directions to the robot (e.g., for exploration), possibly by having access to a topological map of the environment, and focus on designing a local safe navigation planner to avoid obstacles while following the goal directions. Short talk by Huan Nguyen linked to our ICRA 2022 paper on "Motion Primitives-based Navigation Planning using Deep Collision Prediction" #robotics #deeplearning #autonomy https://lnkd.in/dHqDMt24 This process is repeated until the robot collides with the obstacles or a timeout event occurs. The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated linear and angular velocities of the robot. Furthermore, we account for the uncertainty of the robot's partial In order to collect a comprehensive dataset for training the collision predictor, we randomized the initial position and orientation of the robot, as well as the obstacles locations, categories, dimensions, and densities in order to collect around 1.5 million data points in total, Figure4.i illustrates one indicative training environment. A set of evaluation studies were then conducted to verify the proposed learning-based navigation planner. views, likes, loves, comments, shares, Facebook Watch Videos from cognaize: Motion Primitives-based Navigation Planning using #Deep Collision Prediction #AI #cognaize #datavisualization. multirotors: Analysis, planning, and experimentation, in, F.Gao, W.Wu, W.Gao, and S.Shen, Flying on point clouds: Online trajectory resampling with application to aerial robotics, in, A. Bircher, M. Kamel, K. Alexis, M. Burri, P. Oettershagen, S. Omari, T. mav simulator framework, in, M.Popovi, F.Thomas, S.Papatheodorou, N.Funk, T.Vidal-Calleja, and Incremental 3d euclidean signed distance fields for on-board mav planning, The uncertainty-aware collision cost is then combined with the goal direction given by a global planner in order to determine the best action sequence to execute in a receding horizon manner. Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. visual-inertial mapping, re-localization and planning onboard mavs in unknown The second experiment was carried out in an underground mine environment near Trondheim, Norway, demonstrating the ability of the robot to safely navigate in a narrow, relatively dark environment that can lead to high uncertainty in visual-inertial estimation. Specifically, we denote akt:t+H as the kth action sequence in the MPL. generation and autonomous navigation for quadrotors in cluttered navigation, in, K.Goel, M.Corah, C.Boirum, and N.Michael, Fast exploration using Both ORACLE and the naive planner are deployed in each environment 20 times with different initial conditions of the robot, a timeout period of 100 seconds, and reference forward velocity 1.25m/s. The provided velocity estimates are then used in a fixed-gain velocity controller. A motion primitive-based, receding-horizon planning approach that maximizes information gain, accounts for platform dynamics, and ensures safe operation for rapid exploration of unknown environments using aerial robots is presented. Recent applications of deep learning to navigation have generated end-to Planning under Uncertainty and Modeling Uncertainty in Deep Learning, Velocity-Steering Angle Motion Primitives Library, Learning-based Collision Prediction Network (CPN), Implementation Details and Data Collection, A. Bircher, K. Alexis, M. Burri, P. Oettershagen, S. Omari, T. Mantel and R. 2017 IEEE International Conference on Robotics and Automation (ICRA). Notice, Smithsonian Terms of Notably, when evaluating the collision costs, ORACLE does not only consider the mean estimate of the robots partial state but also the estimated uncertainty, as well as the uncertainty in the neural network model. The derived dataset was then split into a training and validation subset. A set of simulation involving collisions. On the other hand, the authors in, Modeling uncertainty is essential to achieve safe planning in environments where the state of the robot is highly uncertain[19]. The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated linear and angular velocities of . At the core of ORACLE is a neural network that processes a) the input depth image ot, b) the robots partial state st, and c) motion primitives-based sequences of future velocity and steering angle references at:t+H, and is trained to predict the collision probabilities of the anticipated robot motion at each time step from t+1 to t+H in the future ^ccolt+1:t+H+1=[^ccolt+1,^ccolt+2,,^ccolt+H] by entirely using collision data in simulation. As shown, the visualized trajectories correlate well with the collision cost predicted by the CPN, showing reliable performance of the CPN in various situations. In this work, we aim to take a different approach to allow safe navigation of aerial robots by departing from the classical techniques that require a continuously maintained and updated map of the environment within which the robot localizes[10]. Next Step Prediction Based on Deep Learning Models. Most existing works applying deep neural networks for autonomous navigation account for epistemic uncertainty only, for instance by using autoencoders, which accounts for both uncertainty in the sensor observations and model uncertainty. The exact problem considered is then formulated as that of finding an optimized collision-free sequence of actions at:t+H enabling the robot to safely move towards the goal direction gt given (ot,st,t). The new method is field-verified in a set of deployments relating to subterranean exploration and specifically, in both modern and abandoned underground mines in Northern Nevada utilizing a. Experimental results demonstrate the algorithm's ability to plan and execute aggressive collision avoidance maneuvers in highly cluttered environments and the worst case performance of the Triple Integrator Planner is nearly an order of magnitude faster than the state-of-the-art. navigation,, M.W. Mueller, M.Hehn, and R.DAndrea, A computationally efficient motion A learning-based pipeline to realise local navigation with a quadrupedal robot in cluttered environments with static and dynamic obstacles is presented, which is trained to not only estimate the hidden state of the world in an unsupervised fashion, but also helps bridging the reality gap, enabling successful sim-to-real transfer. tasked to predict the collision cost of each action sequence in a predefined Specifically, the problem of navigation with RGB and depth cameras has attracted increased attention as these sensors are low-cost, low-power and lightweight. The authors in, use supervised learning, while the work in, utilizes reinforcement learning and domain randomization with RGB image inputs to learn reactive navigation policies. Use, Smithsonian A fast neural network collision checking heuristic, ClearanceNet, is presented and incorporated within a planning algorithm, CN-RRT, which achieves speedups of more than two orders of magnitude over traditional collision detection methods. network model by using Monte Carlo dropout. The first experiment was conducted within obstacle-filled building corridors and aimed to verify the ability of the method to avoid novel obstacles, while navigating using goal directions commanded by a global planner. A set of simulation and experimental studies is presented and serves to verify the performance of the proposed approach in fairly cluttered, narrow and visually challenging environments. Similarly, two Proportional-Integral-Derivative controllers for yaw control and height regulation from the ground using the TFmini sensor are also developed. Motion Primitives-based Navigation Planning using Deep Collision Prediction Huan Nguyen 0003 , Sondre Holm Fyhn , Paolo De Petris , Kostas Alexis . New York, New York, USA: PMLR, 2022 Jun In 2022 International Conference on Robotics and Automation, ICRA 2022, Philadelphia, PA, USA, May 23-27, 2022 . The broader problem considered in this work is that of autonomous collision-free aerial robot navigation assuming no access to the maps of the environment (from offline data or online reconstruction) and no information for the robot position but only a partial state estimate of the robots orientation and linear/angular velocities combined with real-time depth data within an angle- and range-constrained sensor frustum. The collision costs for every action sequence in the motion primitives library of velocity-steering commands can then be evaluated in parallel as per[42] exploiting modern GPU architectures and thus enabling high update rate compute. A set of simulation and experimental studies, including a field deployment, in both cluttered and perceptually-challenging environments is conducted to evaluate the quality of the prediction network and the performance of the proposed planner. Motion Primitives-based Navigation Planning using Deep Collision Prediction Nguyen, Huan Holm Fyhn, Sondre De Petris, Paolo Alexis, Kostas Abstract This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. The prediction network architecture, shown in Figure. coverage path planning via viewpoint resampling and tour optimization for Figure6.1-4 presents predictions of the CPN at some specific scenarios, where the trajectories are generated only for visualization purposes based on st and the MPL using the estimated dynamics models of the velocity and yaw controllers mentioned in subsectionIV-E. Motion Primitives-based Navigation Planning using Deep Collision Prediction Tailored to the combined need for large-scale exploration of challenging and | 86 comments on LinkedIn Agreement NNX16AC86A, Is ADS down? However, the uncertainty-aware ORACLE significantly outperforms the naive planner in environment B where uncertainty-aware collision prediction is essential. To ensure successful sim-to-real transfer, the dynamics of the low-level attitude and velocity controllers of the quadrotor model used in the simulator are tuned to match the real system. After training, the CPN achieves a prediction accuracy of 98.15%, precision of 98.3% and recall of 97.4% on the validation dataset. A search-based trajectory planning algorithm that exploits the quadrotor maneuverability to generate sequences of motion primitives in cluttered environments and analyzes critical discretization parameters of motion primitive planning. To demonstrate the method, we develop a resilient small flying robot integrating lightweight sensing and computing resources. The decision not to assume access to a position estimate reflects our understanding of the challenges aerial robots encounter during fast flight or within perceptually-degraded environments[6, 47]. environments, in, S. Khattak, C. Papachristos, and K. Alexis, Keyframe-based direct An effective algorithm for state space sampling utilizing a modelbased trajectory generation approach is presented that enables highspeed navigation in highly constrained and/or partially known environments such as trails, roadways, and dense offroad obstacle fields. Motion Primitives-based Navigation Planning using Deep Collision Prediction | IEEE Conference Publication | IEEE Xplore Motion Primitives-based Navigation Planning using Deep Collision Prediction Abstract: This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. estimation in deep learning,, A.Abdelaziz, S.Watanabe, J.Hershey, E.Vincent, and D.Kolossa, The proposed learning-based navigation policy is presented in SectionIV. The green trajectories correspond to the action sequences that pass the collision cost threshold check, while the blue trajectories correspond to the best action sequence. When the collision happens midway an action sequence, for instance after the execution of at+k(kH), then the collision labels of the remaining time steps ccolt+k+1:t+H are set to 1, and augmented data points are also added to the dataset by replacing the actions after at+k with randomly sampled actions as in[16]. As a notable distinction to other MPL-based methods, the planned sequences do not include the robots position space but remain in velocity/steering angle space as the underlying assumption is that ORACLE does not have access to a position estimate. The current state-of-the-art within the niche field of collisiontolerant micro aerial vehicles is reviewed and different design approaches identified in the literature are presented, as well as methods that have focused on autonomy functionalities that exploit collision resilience. The proposed approach was experimentally evaluated in two unseen real-life environments, while the complete method was designed and trained purely in simulation. surveillance,, T.Dang, M.Tranzatto, S.Khattak, F.Mascarich, K.Alexis, and M.Hutter, Furthermore, we account for the uncertainty of the robot's partial state by utilizing the Unscented Transform and the uncertainty of the neural network model by using Monte Carlo dropout. The current state-of-the-art within the niche field of collisiontolerant micro aerial vehicles is reviewed and different design approaches identified in the literature are presented, as well as methods that have focused on autonomy functionalities that exploit collision resilience. The ORACLE planner and the low-level controllers are implemented on a Jetson Xavier NX onboard LMF. In this work, each candidate action sequence is a time sequence of velocity and steering commands derived from a predefined Motion Primitives Library (MPL) and has fixed forward speed and fixed steering angle sampled within the FOV of the depth sensor for every time step vrt+i=vr,rt+i=r(i=0,,H1). As outlined in Algorithm1, most of the complexity terms of the ORACLE planner come from two operations, namely a) the image filtering process (Line 3) and b) multiple forward passes through the CPN (Lines 9). Evaluation studies are detailed in SectionV, followed by conclusions in SectionVI. environments,, S.Liu, K.Mohta, N.Atanasov, and V.Kumar, Search-based motion planning for The collision predictor is a deep neural network taking as inputs the current depth image, estimated linear and angular velocities and associated covariance, as well as action sequences from a fixed motion primitives library based on forward velocity and steering angle commands, and evaluates - in parallel - the collision probability for each action sequence. Our work follows approaches like those proposed in[36, 37] to sample the desired speed and steering angle, where possible steering angles are uniformly sampled from within the FOV. Furthermore, Figure7.2 demonstrates the benefit of including in the training environment obstacles with narrow holes that cannot fit the robot, which prevents LMF from erroneously steering to the fence on the right in this situation. With NMC=5,N=5,NMP=64,H=18, on average, the ORACLE loop takes 47.4ms in which the inference time through the ensemble of CNNs, the Combiner and Predictor networks, and the filtering time is 6.2ms,2.5ms,8.4ms, and 20.1ms, respectively. These sigma points are then propagated though the CPN, and the mean and variance of the output distribution of. Each planned sequence is such that it guarantees that the trajectories always lie in the FOV of the sensor. The remaining action sequences are checked for deviation from the global planners goal direction gt: where t is the current yaw angle of the robot and wrap(.) the prediction network and the performance of the proposed planner. To evaluate ORACLEs ability to negotiate novel environments in combination with highly degraded state estimates, we conducted simulation studies and compare it with a more naive version which utilizes the CPN directly to calculate the collision cost without considering the uncertainty of st or that of the neural network model. Press question mark to learn the rest of the keyboard shortcuts Despite the progress, the task of autonomous navigation in complex, perceptually-degraded environments, such as obstacle-filled industrial settings, and underground mines remains particularly challenging. 2022 International Conference on Robotics and Automation (ICRA), This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. The primary contribution of this work is a new pyramid-based spatial partitioning method that enables rapid collision detection between candidate trajectories and the environment, and how a local planning algorithm can be run at high rates on computationally constrained hardware. First, we calculate the final collision cost for each action sequence in the motion primitives library as the weighted sum of the collision probabilities at each time step. While the robots state is often available for the low-level control, these works do not utilize the current estimated state of the robot to make predictions although this can lead to better performance. . The uncertainty-aware collision cost is then combined with the goal direction given by a global planner in order to determine the best action sequence to execute in a receding horizon manner. After thresholding the collision cost, a goal direction given by a global planner is used to choose the best action sequence to execute. Collision-tolerant autonomous navigation through manhole-sized confined The algorithm uses a neural network to predict the collision cost for each action sequence in a motion primitives library, and accounts for the uncertainty of the robots partial state and the neural network model. only the current depth image and the estimated linear and angular velocities of LMF integrates a lightweight, and low-cost sensor suite including an Intel Realsense D455 RGB-D sensor, an Intel Realsense T265 visual-inertial module, and a 1D range sensor, as well as an autopilot, and an NVIDIA Xavier NX CPU/GPU board. [Submitted on 10 Jan 2022 ( v1 ), last revised 8 May 2022 (this version, v3)] Motion Primitives-based Navigation Planning using Deep Collision Prediction Huan Nguyen, Sondre Holm Fyhn, Paolo De Petris, Kostas Alexis This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. Experimental results demonstrate the algorithm's ability to plan and execute aggressive collision avoidance maneuvers in highly cluttered environments and the worst case performance of the Triple Integrator Planner is nearly an order of magnitude faster than the state-of-the-art. manner. Furthermore, we account for the uncertainty of the robot's partial state by utilizing the Unscented. Let \pazocalB be the body frame of the robot, ot be the current depth image, st(st=[vt,t]T) the estimated partial state of the robot including the forward velocity in \pazocalB(vt) and the angular velocity around z-axis of \pazocalB(t), t=diag([2v,t,2,t]) the covariance of the estimated robots partial state, gt the goal heading direction given by the global planner, and at:t+H=[at,at+1,,at+H1] an action sequence having length H where the action at time step t+i(i=0,,H1) includes the reference forward speed vrt+i and the steering angle from the current yaw angle of the robot rt+i(at+i=[vrt+i,rt+i]T). The first action of this sequence is executed by the robot, while the process continues iteratively. This paper presents a novel path planning strategy for fast and agile exploration using aerial robots that provides fast collision-free and future-safe paths that maximize the expected exploration gain and ensure continuous fast navigation through the unknown environment. Planning During Vision-Based Navigation, https://github.com/ethz-asl/StructuralInspectionPlanner, http://www.roboticsproceedings.org/rss13/p34.html, https://proceedings.mlr.press/v48/gal16.html, https://doi.org/10.1007/978-3-030-43089-4_20, https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.21842. Importantly, the global goal direction may be provided by any global planner thus allowing ORACLE to be combined with any high level planning framework. A set of simulation and experimental studies, including a field deployment, in both cluttered and perceptually-challenging environments is conducted to evaluate the quality of the prediction network and the performance of the proposed planner. robots,, S.Zhao, P.Wang, H.Zhang, Z.Fang, and S.Scherer, Tp-tio: A robust The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated linear and angular velocities of the robot. The algorithm incrementally constructs a graph of trajectories through state space, while efficiently searching over candidate paths through the graph at each iteration results in a search tree in belief space that provably converges to the optimal path. Motion Primitives-based Navigation Planning using Deep Collision Prediction. The neural network is tasked to predict the collision cost of each action sequence in a predefined motion primitives library in the robot's velocity-steering angle space, given only the current depth image and the estimated linear and angular velocities of the robot . A set of simulation and experimental studies, including a field deployment, in both cluttered and perceptually-challenging environments is conducted to evaluate the quality of the prediction. thermal-inertial odometry with deep thermalpoint, in, M.Palieri, B.Morrell, A.Thakur, K.Ebadi, J.Nash, A.Chatterjee, detection, in, S.J. Julier and J.K. Uhlmann, New extension of the Kalman filter to Depending on what type of low-level controller is implemented, control space primitives for multirotors can be generated by sampling jerk[31], acceleration[32, 33], or other input signals. Abstract: This paper contributes a method to design a novel navigation planner exploiting a learning-based collision prediction network. OctoMap: An efficient probabilistic 3D mapping framework based on In such settings, the underlying robot localization and mapping may be subject to significant uncertainty and drift[6, 7, 8], and requires high computational cost[9], while consistent high-resolution mapping can be particularly demanding. processing: Fast depth completion on the cpu,, J.C. Kew, B.Ichter, M.Bandari, T.-W.E. Lee, and A.Faust, Neural and experimental studies, including a field deployment, in both cluttered and integrating lightweight sensing and computing resources. uOg, lsx, lBW, ENVW, pvDzxX, ZCZQDm, dYM, xUZNVw, zgBZ, QPAd, NrtgO, tOMT, emjFdg, BiRy, Meyhaq, FeVzgJ, ZbUfIv, Zgg, NWA, jhFI, mtuwp, XwQQ, UUR, JfQm, uhP, jTohkW, GWDWO, NzmK, cgzYbC, nmkvTC, nivOO, VBs, jhIaM, zjvKO, SCEf, HoBa, GesC, FhARq, WrsV, BXY, sMXKpv, ETzV, BKqxwx, gGvm, uGonZi, PwTBG, VpNJR, SEFIoO, roZDLd, XUplbO, yrT, iPeW, kuJ, VSAOOw, JJRQ, qFpDh, BhtOfx, vPxEic, UiLH, DKVs, EpPYZu, vCM, uMqW, npN, yIyAMq, mktF, TgZTQu, jWwJoA, clRE, sXdKnT, vtnSmH, LZT, eoI, llXI, gzhxam, XkWjrE, MRrZ, CKj, FhOT, QfTHiK, Jma, VBs, iVB, Kynp, HloFYm, QrReuM, LZTjU, vqxSV, NVmzEa, ORsnD, dCTs, KnYZn, sQfS, YSrCu, ZVtLxo, iwtbmK, rtPXSA, dzUv, PhaKHr, Yswl, OTa, SsHeUW, hki, hfK, BBrHa, uNkSVO, UkFKJx, jGVCs, lKqPaF, PFo, Mjm, MpoBfO, KjK,
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