A spatio-temporal trajectory planning framework for AGVs based on motion primitive and dynamic programming in off-road environments

Abstract

Generating safe, smooth, and dynamically feasible trajectories remains a critical yet challenging task for autonomous ground vehicles (AGVs) operating in off-road environments. This paper proposes a spatio-temporal trajectory planning framework to systematically address the challenges of off-road environments and dynamic obstacles for AGVs. The proposed framework consists of reference path planning based on motion primitives (MP) and spatio-temporal trajectory planning based on dynamic programming (DP). At the reference path level, a hierarchical map model is constructed to store terrain elevation information, traversability information, and static obstacle information separately. Based on the hierarchical map data, we establish a vehicle static stability model and a safe feasible region generation model, and employ the MP algorithm to generate the optimal reference path. At the spatio-temporal trajectory planning level, a spatio-temporal sampling space model is constructed to search for reference trajectories, and a DP-based method is designed to select the optimal trajectory. Based on the reference trajectory, a spatio-temporal safety corridor generation method is proposed to iteratively optimize the trajectory solution. Finally, the proposed framework is validated in both simulations and real-vehicle, and experimental results demonstrate that the proposed system can plan a feasible trajectory fast with the constraints from vehicle kinematics, obstacle avoidance and off-road terrains.