Full-Body Dynamic Safety for Robot Manipulators: 3D Poisson Safety Functions for CBF-Based Safety Filters

2026-04-22

AI Summary

This work addresses the challenges of multi-constraint handling and computational complexity in whole-body collision avoidance for robots operating in high-dimensional configuration spaces. The authors propose a real-time obstacle avoidance framework based on a 3D Poisson safety function. By leveraging occupancy data of the environment, surface points on the robot arm are sampled, and a buffered free space is constructed via the Pontryagin difference. Within this region, a Poisson equation is solved to generate a globally smooth safety function. Integrating control barrier functions with a multi-constraint quadratic program, the approach reduces the intricate whole-body collision avoidance problem to a single smooth constraint. Theoretical analysis demonstrates that ensuring safety at the sampled surface points guarantees collision-free motion for the entire continuous manipulator. Experiments on a 7-DOF robotic arm validate the method’s efficacy and reliability in achieving real-time whole-body obstacle avoidance in dynamic environments.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements

Abstract

Collision avoidance for robotic manipulators requires enforcing full-body safety constraints in high-dimensional configuration spaces. Control Barrier Function (CBF) based safety filters have proven effective in enabling safe behaviors, but enforcing the high number of constraints needed for safe manipulation leads to theoretic and computational challenges. This work presents a framework for full-body collision avoidance for manipulators in dynamic environments by leveraging 3D Poisson Safety Functions (PSFs). In particular, given environmental occupancy data, we sample the manipulator surface at a prescribed resolution and shrink free space via a Pontryagin difference according to this resolution. On this buffered domain, we synthesize a globally smooth CBF by solving Poisson's equation, yielding a single safety function for the entire environment. This safety function, evaluated at each sampled point, yields task-space CBF constraints enforced by a real-time safety filter via a multi-constraint quadratic program. We prove that keeping the sample points safe in the buffered region guarantees collision avoidance for the entire continuous robot surface. The framework is validated on a 7-degree-of-freedom manipulator in dynamic environments.

Problem

• full-body safety
• collision avoidance
• robotic manipulators
• high-dimensional configuration spaces
• safety constraints

Innovation

• Poisson Safety Functions
• Control Barrier Functions
• Full-body Collision Avoidance
• Pontryagin Difference
• Real-time Safety Filter