G-MAPP: GPU-accelerated Multi-Agent Planning and Perception for Reactive Motion Generation
Bishnoi, Tanmay Laha, Riddhiman Low, Tobias Chandy, Jose Alex Figueredo, Luis FC Haddadin, Sami
Bishnoi, Tanmay
Laha, Riddhiman
Low, Tobias
Chandy, Jose Alex
Figueredo, Luis FC
Haddadin, Sami
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Robotics
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Journal article
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English
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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 quasiglobal trajectory planning, and tighter coupling of the perceptionaction 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. The implementation is available at: https://github.com/chart-research/g-mapp
Citation
T. Bishnoi, R. Laha, T. Low, J.A. Chandy, L.F.C. Figueredo, S. Haddadin, "G-MAPP: GPU-accelerated Multi-Agent Planning and Perception for Reactive Motion Generation," IEEE Robotics and Automation Letters, vol. PP, no. 99, pp. 1-8, 2026, https://doi.org/10.1109/lra.2026.3678839.
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IEEE Robotics and Automation Letters
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Keywords
46 Information and Computing Sciences, 4602 Artificial Intelligence, 4605 Data Management and Data Science
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IEEE