Real-Time Terrain-Aware Path Optimization for Off-Road Autonomous Vehicles

Navigating off-road terrains is crucial for military, agricultural, and rescue operations. Existing algorithms for off-road path planning offer limited adaptability to complex terrains and often lack the computational efficiency required for real-time applications. This is largely due to the nonconvex and nonsmooth characteristics of terrain geometry. Our research introduces an innovative terrain representation technique that streamlines the complexity of the terrain into a manageable path optimization problem, focusing on optimizing vehicle attitude concerning the path. By employing discrete curves to represent lateral terrain elevation changes, our method facilitates the direct integration of vehicle attitude into the optimization framework, thereby diminishing the need for computationally intensive traversability maps typical of traditional approaches. We tackle the resulting nonlinear optimization problem with a constrained iterative linear quadratic regulator (iLQR), achieving real-time path planning capabilities. The proposed method demonstrates improved computational efficiency and enhanced path quality, demonstrating significant time savings in planning while ensuring high-quality outcomes.