Energy-efficient human-like trajectory planning for wheeled robots in unstructured environments based on the RCSM-PL network

Abstract

Reducing energy consumption of wheeled robots in urban inspection and unstructured environments is a pressing challenge. This study proposes a human-like trajectory planning method based on deep learning to address energy inefficiency. A convolutional neural network (CNN) with multi-dimensional attention extracts spatial features from driving scenes and radar maps of hazardous areas. Temporal dependencies are captured using an improved long short-term memory (LSTM), where state information is added to the gate update module. Power, speed, and angular velocity are incorporated as constraints to enhance trajectory mapping accuracy. Experimental results show that, compared with traditional and state-of-the-art methods, the proposed approach significantly reduces cumulative power consumption and improves accuracy in predicting future trajectories. The model effectively learns human manipulation behaviors and demonstrates superior energy-saving performance in complex driving scenarios.