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

2025-02-18

AI Summary

Addressing the challenge of online robotic arm trajectory planning in dynamic environments—where kinematic constraints, real-time performance, and collision avoidance must be simultaneously satisfied—this paper proposes the BoundPlanner–BoundMPC cooperative framework. First, it introduces a novel convex-set-based representation of obstacle-free space and path boundaries, enabling obstacle-count-independent convex collision-avoidance constraints. Second, it designs a BoundMPC tracking controller incorporating convex path-deviation constraints, facilitating real-time bounded-trajectory optimization and execution. The method integrates convex geometric modeling, distance-field-based collision checking, and efficient nonlinear model predictive control (MPC). Evaluated on both simulation and physical 7-DoF robotic arms, it significantly improves planning success rate and response speed over state-of-the-art approaches, especially under high-constraint dynamic conditions. The source code and experimental videos are publicly available.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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/Thieso/BoundPlanner and videos of the experiments can be found at www.acin.tuwien.ac.at/42d4

Problem

• Online trajectory planning for manipulators
• Handles robot limits and collisions
• Uses convex sets for path optimization

Innovation

• Convex-set-based Cartesian path planner
• Online trajectory planner with convex sets
• Collision avoidance using convex sets