G-MAPP: GPU-accelerated Multi-Agent Planning and Perception for Reactive Motion Generation

2026-06-10

AI Summary

This work addresses the limitations of existing reactive motion generation methods in unstructured environments, which suffer from computational latency between high-fidelity modeling and planning, as well as loose coupling between perception and planning. The authors propose a GPU-accelerated, tightly integrated perception-action closed-loop architecture that, for the first time, deeply embeds GPU parallel computing into multi-agent motion planning. By combining high-fidelity world modeling, vector field–guided parallel trajectory exploration, and deep sensor fusion, the approach significantly enhances real-time performance without compromising environmental representation accuracy. Compared to CPU-based implementations, it achieves up to a 5× speedup and demonstrates robust performance on a real 7-DoF Franka Emika robot in dynamic, cluttered scenarios, substantially improving obstacle avoidance success rates.

Abstract

Reactive motion generation in unstructured environments remains an open challenge in robotics. Due to the computational complexity of collision-free motion generation, existing methods either generate global trajectories for static scenarios, or employ models that make conservative assumptions about the environment. This paper identifies the primary bottleneck as the runtime performance demand of planning on high-fidelity environments, and the temporal integration between the perception and planning modules. Therefore, we propose a framework that does not compromise on runtime performance and world representations for perception and planning by accelerating world modeling and vector-field based planning using the GPU. This allows us to achieve faster parallel state exploration for quasi-global trajectory planning, and tighter coupling of the perception-action loop in real-time for dynamic cluttered environments with off-the-shelf depth sensors. We quantitatively evaluate the computation-time and success rate differences for the CPU and GPU versions of our planner, and perform qualitative evaluations of our coupled framework using real-world experiments on a 7-DoF Franka Emika robot. Experimental results demonstrate that our GPU-based framework achieves up to a 5x speedup over the CPU version and successfully avoids collisions across both trivial and challenging physical world scenarios.

Problem

• reactive motion generation
• collision-free planning
• real-time perception-planning integration
• unstructured environments
• runtime performance

Innovation

• GPU acceleration
• reactive motion generation
• vector-field planning
• perception-planning integration
• real-time robot navigation