Performance Optimization of Autonomous Platforms in Unstructured Outdoor Environments Using a Novel Constrained Planning Approach

We propose a novel constrained planning approach for autonomous vehicles in unstructured outdoor environments. Our method enables autonomous off-road platforms to keep a predetermined track accurately, but in the same time allows the avoidance of static obstacles and dynamic objects. Two application scenarios are presented according to common transport behaviors with obstacle avoidance: Mule and Convoying. Our method is real-time integrated in a typical processing pipeline for autonomous driving in unstructured outdoor environments. It provides a constrained planning area, subsequently designated cost valley. Our cost valley keeps the trajectory of the vehicle both smooth in difficult passages and close to the desired track. The efficiency of our method is demonstrated on two autonomous platforms with a huge difference in kinematics, weight and size - a small electric platform and an off-road truck. It directly improves the behavior of autonomous vehicles, especially in critical passages.