Optimized motion planning for mobile robots in dynamic construction environments with low-feature mapping and pose-based positioning

Abstract

Optimizing autonomous motion planning for robots in dynamic and uncertain construction environments is crucial. Real-time planning is challenged by the complexity of map-building data processing and path optimization. This paper introduced a dynamic motion planning approach utilizing low-feature data, multi-constraint path planning, and flexible positioning. A multi-sensor data fusion method generates grid-based 2D dynamic maps for efficient data processing and real-time perception. The approach incorporates multiple constraints, including safety, stability, and energy consumption, to optimize path planning. Flexible destination positioning is achieved through pose recognition in changing construction scenarios. Real-time experiments demonstrate that the proposed method reduces CPU usage by 19 %, memory usage by 8 %, and energy consumption by 9.5 % compared to traditional methods using LIO-SAM mapping and RRT path planning. This paper provided an efficient and safe motion planning approach for mobile robots in dynamic environments, achieving low energy consumption and enhanced operational efficiency.