BoundPlanner: A convex-set-based approach to bounded manipulator trajectory planning

TL;DR

BoundPlanner introduces a convex-set-based framework for fast, collision-aware online trajectory planning of robot manipulators, enabling real-time responses in dynamic environments with improved safety and efficiency.

Contribution

This work presents a novel convex-set-based Cartesian path planner and an extended online trajectory planner that together improve collision avoidance and path following in real-time.

Findings

Outperforms state-of-the-art methods in simulations and experiments

Handles collision avoidance independently of obstacle number

Enables real-time trajectory planning for 7-DoF manipulators

Abstract

Online trajectory planning enables robot manipulators to react quickly to changing environments or tasks. Many robot trajectory planners exist for known environments but are often too slow for online computations. Current methods in online trajectory planning do not find suitable trajectories in challenging scenarios that respect the limits of the robot and account for collisions. This work proposes a trajectory planning framework consisting of the novel Cartesian path planner based on convex sets, called BoundPlanner, and the online trajectory planner BoundMPC. BoundPlanner explores and maps the collision-free space using convex sets to compute a reference path with bounds. BoundMPC is extended in this work to handle convex sets for path deviations, which allows the robot to optimally follow the path within the bounds while accounting for the robot's kinematics. Collisions of the robot's kinematic chain are considered by a novel convex-set-based collision avoidance formulation independent on the number of obstacles. Simulations and experiments with a 7-DoF manipulator show the performance of the proposed planner compared to state-of-the-art methods. The source code is available at github.com/TU-Wien-ACIN-CDS/BoundPlanner and videos of the experiments can be found at www.acin.tuwien.ac.at/42d4.