Occlusion-Aware Consistent Model Predictive Control for Robot Navigation in Occluded Obstacle-Dense Environments
Minzhe Zheng, Lei Zheng, Lei Zhu, Jun Ma
AI summary
Problem
Mobile robots struggle to navigate safely and smoothly in obstacle-dense environments with partial visibility, as existing methods are either overly conservative, lack motion consistency, or are too computationally heavy for real-time use.
Approach
The authors introduce an occlusion-aware Consistent MPC that dynamically models potential obstacle locations as adjustable risk regions, generates multiple trajectory branches sharing a common initial segment for smooth transitions, and decomposes the optimization into parallel sub-problems using ADMM.
Key results
- Develops a parallel ADMM-based CMPC framework for real-time trajectory planning
- Introduces adjustable risk regions to balance safety and performance in occluded areas
- Ensures motion consistency through a shared consensus segment across trajectory branches
- Validates superior safety and smoothness over baselines in simulations and real-world experiments
Why it matters
Enables reliable, real-time autonomous navigation for mobile robots in complex, partially visible environments where safety and smooth motion are critical.
Abstract
Ensuring safety and motion consistency for robot navigation in occluded, obstacle-dense environments is a critical challenge. In this context, this study presents an occlusion- aware Consistent Model Predictive Control (CMPC) strategy. To account for the occluded obstacles, it incorporates adjustable risk regions that represent their potential future locations. Subsequently, dynamic risk boundary constraints are developed online to enhance safety. Based on these constraints, the CMPC constructs multiple locally optimal trajectory branches (each tailored to different risk regions) to strike a balance between safety and performance. A shared consensus segment is generated to ensure smooth transitions between branches without significant velocity fluctuations, preserving motion con- sistency. To facilitate high computational efficiency and ensure coordination across local trajectories, we use the alternating direction method of multipliers (ADMM) to decompose the CMPC into manageable sub-problems for parallel solving. The proposed strategy is validated through simulations and real- world experiments on an Ackermann-steering robot platform. The results demonstrate the effectiveness of the proposed CMPC strategy through comparisons with baseline approaches in occluded, obstacle-dense environments.