Can Not Touch This: Real-Time, Safe Motion Planning and Control for Manipulators Under Uncertainty

Engineering notes

Key topics: autonomous driving, motion planning, planning, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Ensuring safe, real-time motion planning in arbitrary environments requires a robotic manipulator to avoid collisions, obey joint limits, and account for uncertainties in the mass and inertia of objects and the robot itself. This article proposes autonomous robust manipulation via optimization with uncertainty-aware reachability (ARMOUR), a provably-safe, receding-horizon trajectory planner and tracking controller framework for robotic manipulators to address these challenges. ARMOUR first constructs a robust controller that tracks desired trajectories with bounded error despite uncertain dynamics. ARMOUR then uses a novel recursive Newton–Euler method to compute all inputs required to track any trajectory within a continuum of desired trajectories. Finally, ARMOUR overapproximates the swept volume of the manipulator; this enables one to formulate an optimization problem that can be solved in real time to synthesize provably-safe motions. This article compares ARMOUR to state of the art methods on a set of challenging manipulation examples in simulation and demonstrates its ability to ensure safety on real hardware in the presence of model uncertainty without sacrificing performance.

Links and sources

• Official / arXiv page

Comments