Research on Trajectory Decision-Making Methods for Intelligent Robots Integrating LiDAR and Multimodal Perception such as Image

Abstract

In complex scenarios, single-sensor perception is unstable, trajectory planning lacks safety constraints, and there are problems with multi-source coordination. To address these issues, this paper proposes an intelligent robot trajectory decision-making method combining LiDAR and image processing, forming a multimodal perception system. This system requires a robot platform, proper extrinsic parameter calibration, and time synchronization. A LiDAR-Image feature acquisition and attention fusion network is designed from a unified BEV perspective to generate an environmental cost map that considers both geometry and semantics. Based on this cost map, an RL/MPC trajectory decision-making model is constructed, introducing chance constraints and dynamic boundaries to ensure safety margins. Simulations and real-world experiments included indoor corridors, office areas, and crowded places. Results show that the multimodal approach outperforms DWA and single-modal RL in terms of mAP, distance RMSE, minimum obstacle distance, and task completion rate. Furthermore, it can run continuously on embedded platforms, demonstrating the effectiveness of the proposed method and its value for engineering applications.