Reachable Sets for Safe, Real-Time Manipulator Trajectory Design
Patrick Holmes,Shreyas Kousik,Bohao Zhang,Daphna Raz,Corina Barbalata,Matthew Johnson-Roberson,Ram Vasudevan +6 more
- Vol. 16
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TLDR
In this paper, an autonomous reachability-based Manipulator Trajectory Design (ARMTD) algorithm is proposed, which computes a reachable set of parameterized trajectories for each joint of an arm.Abstract:
For robotic arms to operate in arbitrary environments, especially near people, it is critical to certify the safety of their motion planning algorithms. However, there is often a trade-off between safety and real-time performance; one can either carefully design safe plans, or rapidly generate potentially-unsafe plans. This work presents a receding-horizon, real-time trajectory planner with safety guarantees, called ARMTD (Autonomous Reachability-based Manipulator Trajectory Design). The method first computes (offline) a reachable set of parameterized trajectories for each joint of an arm. Each trajectory includes a fail-safe maneuver (braking to a stop). At runtime, in each receding-horizon planning iteration, ARMTD constructs a parameterized reachable set of the full arm in workspace and intersects it with obstacles to generate sub-differentiable, provably-conservative collision-avoidance constraints on the trajectory parameters. ARMTD then performs trajectory optimization over the parameters, subject to these constraints. On a 6 degree-of-freedom arm, ARMTD outperforms CHOMP in simulation, never crashes, and completes a variety of real-time planning tasks on hardware.read more
Citations
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Proceedings ArticleDOI
DeepReach: A Deep Learning Approach to High-Dimensional Reachability
Somil Bansal,Claire J. Tomlin +1 more
TL;DR: DeepReach as mentioned in this paper leverages new developments in sinusoidal networks to develop a neural PDE solver for high-dimensional reachability problems, which achieves comparable results to the state-of-the-art reachability methods, does not require any explicit supervision for the PDE solution, and also provides a safety controller for the system.
Posted Content
Reachability-based Trajectory Safeguard (RTS): A Safe and Fast Reinforcement Learning Safety Layer for Continuous Control
TL;DR: A Reachability-based Trajectory Safeguard (RTS), which leverages reachability analysis to ensure safety during training and operation and in comparison with state-of-the-art safe motion planning methods.
Journal ArticleDOI
Reachability-Based Trajectory Safeguard (RTS): A Safe and Fast Reinforcement Learning Safety Layer for Continuous Control
TL;DR: In this paper, a Reachability-based Trajectory Safeguard (RTS) algorithm is proposed to ensure safety during training and operation of a robot in a safety critical environment.
Posted Content
Sampling-based Reachability Analysis: A Random Set Theory Approach with Adversarial Sampling
Thomas Lew,Marco Pavone +1 more
TL;DR: A simple yet effective sampling-based approach to perform reachability analysis for arbitrary dynamical systems by using random set theory to give a rigorous interpretation of the method, and proving that it returns sets which are guaranteed to converge to the convex hull of the true reachable sets.
Proceedings ArticleDOI
SaRA: A Tool for Safe Human-Robot Coexistence and Collaboration through Reachability Analysis
TL;DR: The experimental results show that the set-based prediction of a human can be computed in a few microseconds, using SaRA, allowing for real-time consideration of many surrounding humans in an environment.
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