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This paper introduces a novel control barrier function (CBF) based safety filter for robotic manipulators that ensures full-body collision avoidance in dynamic environments. The key innovation is the use of 3D Poisson Safety Functions (PSFs) to synthesize a globally smooth CBF from environmental occupancy data, effectively reducing the computational burden of enforcing numerous safety constraints. Experimental validation on a 7-DoF manipulator demonstrates the framework's ability to maintain safety in dynamic scenarios.
Guaranteeing full-body collision avoidance for robot manipulators in dynamic environments is now computationally tractable thanks to a novel application of 3D Poisson Safety Functions.
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.
