Fast and Bounded Probabilistic Collision Detection for High-DOF Trajectory Planning in Dynamic Environments

Park, Chonhyon; Park, Jae S.; Manocha, Dinesh

doi:10.1109/tase.2018.2801279

Cited by 41 publications

(62 citation statements)

References 45 publications

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“…In this way, p-Chekov will not need to waste a long time trying to search for feasible solutions for infeasible cases. Another potential future work direction is to incorporate online obstacle avoidance [36,49,55] into p-Chekov, so that it can handle dynamic obstacles in the execution environment. Additionally, real robot experiments with raw sensor data are also necessary before p-Chekov can be deployed in real-world applications.…”

Section: Discussionmentioning

confidence: 99%

Fast-Reactive Probabilistic Motion Planning for High-Dimensional Robots

2021

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Many real-world robotic operations that involve high-dimensional humanoid robots require fast reaction to plan disturbances and probabilistic guarantees over collision risks, whereas most probabilistic motion planning approaches developed for carlike robots cannot be directly applied to high-dimensional robots. In this paper, we present probabilistic Chekov (p-Chekov), a fast-reactive motion planning system that can provide safety guarantees for high-dimensional robots suffering from process noises and observation noises. Leveraging recent advances in machine learning as well as our previous work in deterministic motion planning that integrated trajectory optimization into a sparse roadmap framework, p-Chekov demonstrates its superiority in terms of collision avoidance ability and planning speed in high-dimensional robotic motion planning tasks in complex environments without the convexification of obstacles. Comprehensive theoretical and empirical analysis provided in this paper shows that p-Chekov can effectively satisfy user-specified chance constraints over collision risk in practical robotic manipulation tasks.

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Section: Discussionmentioning

confidence: 99%

Fast-Reactive Probabilistic Motion Planning for High-Dimensional Robots

2021

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“…Monte-Carlo methods can evaluate the probability of collision by sampling, but can be computationally expensive when the likelihood of failure is very small [6]. When the uncertainty is restricted to Gaussian uncertainty on the robot's pose, probabilistic collision checking can yield notable performance improvements [17][13] [12].…”

Section: Related Workmentioning

confidence: 99%

Provably Safe Robot Navigation with Obstacle Uncertainty

Axelrod

Kaelbling

Lozano-Pérez

2017

Robotics: Science and Systems XIII

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Abstract-As drones and autonomous cars become more widespread it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy is safe with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information. We present efficient algorithms that can prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our method's ability to evaluate if a trajectory or policy is safe. We then use these safety checking methods to design a safe variant of the RRT planning algorithm.

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“…We also continuously track the human pose and update the predicted future motion to re-plan safe robot motions. Our approach uses the notion of online probabilistic collision detection [23,26,27] between the robot and the point-cloud data corresponding to human obstacles, to compute reactive costs and integrate them with our optimization-based planner.…”

Section: Online Motion Planningmentioning

confidence: 99%

Intention-Aware Motion Planning Using Learning Based Human Motion Prediction

Park¹,

Park²,

Manocha

2017

Robotics: Science and Systems XIII

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Abstract-We present a motion planning algorithm to compute collision-free and smooth trajectories for high-DOF robots interacting with humans in a shared workspace. Our approach uses offline learning of human actions along with temporal coherence to predict the human actions. Our intention-aware online planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We highlight the performance of our planning algorithm in complex simulated scenarios and real world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments.

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Fast and Bounded Probabilistic Collision Detection for High-DOF Trajectory Planning in Dynamic Environments

Cited by 41 publications

References 45 publications

Fast-Reactive Probabilistic Motion Planning for High-Dimensional Robots

Fast-Reactive Probabilistic Motion Planning for High-Dimensional Robots

Provably Safe Robot Navigation with Obstacle Uncertainty

Intention-Aware Motion Planning Using Learning Based Human Motion Prediction

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