ParaMaP: Real-time Robot Manipulation with Parallel Mapping and Planning
Published:Dec 27, 2025 12:24
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of real-time, collision-free motion planning for robotic manipulation in dynamic environments. It proposes a novel framework, ParaMaP, that integrates GPU-accelerated Euclidean Distance Transform (EDT) for environment representation with a sampling-based Model Predictive Control (SMPC) planner. The key innovation lies in the parallel execution of mapping and planning, enabling high-frequency replanning and reactive behavior. The use of a robot-masked update mechanism and a geometrically consistent pose tracking metric further enhances the system's performance. The paper's significance lies in its potential to improve the responsiveness and adaptability of robots in complex and uncertain environments.
Key Takeaways
- •Proposes ParaMaP, a parallel mapping and motion planning framework.
- •Integrates EDT-based environment representation with SMPC planning.
- •Employs GPU acceleration for high-frequency replanning.
- •Includes a robot-masked update mechanism and a geometrically consistent pose tracking metric.
- •Validated through simulations and real-world experiments.
Reference
“The paper highlights the use of a GPU-based EDT and SMPC for high-frequency replanning and reactive manipulation.”