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Reachable Sets for Safe, Real-Time Manipulator Trajectory Design

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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.

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Proceedings ArticleDOI

DeepReach: A Deep Learning Approach to High-Dimensional Reachability

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.
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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.
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Sampling-based Reachability Analysis: A Random Set Theory Approach with Adversarial Sampling

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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