Bird’s-Eye-View Trajectory Planning of Multiple Robots using Continuous Deep Reinforcement Learning and Model Predictive Control

Abstract

Efficient motion planning and control for multiple mobile robots in industrial automation and indoor logistics face challenges such as trajectory generation and collision avoidance in complex environments. We propose a hybrid, sequential method combining Bird’s-Eye-View vision-based continuous Deep Reinforcement Learning (DRL) with Model Predictive Control (MPC). DRL generates candidate trajectories in complex environments, while MPC refines these trajectories to ensure adherence to kinematic and dynamic constraints of the robot, as well as constraints modeling humans’ current and predicted future positions. In this study, the DRL utilizes a Deep Deterministic Policy Gradient model for trajectory generation, demonstrating its capability to navigate non-convex obstacles, a task that might pose challenges for MPC. We demonstrate that the proposed hybrid DRL-MPC model performs favorably in handling new scenarios, computational efficiency, time to destination, and adaptability to complex multi-robot situations when compared to pure DRL or pure MPC approaches.