Reachable Sets for Safe, Real-Time Manipulator Trajectory Design
This addresses safety-critical motion planning for robotic arms operating near people, though it is incremental as it builds on existing reachability and optimization methods.
The paper tackles the trade-off between safety and real-time performance in robotic arm motion planning by introducing ARMTD, a receding-horizon planner with safety guarantees that uses reachable sets and fail-safe maneuvers; it outperforms CHOMP in simulation, avoids crashes, and handles real-time tasks on hardware.
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.