Self-Assessment of Evidential Grid Map Fusion for Robust Motion Planning

Conflicting sensor measurements pose a huge problem for the environment representation of an autonomous robot. Therefore, in this paper, we address the self-assessment of an evidential grid map in which data from conflicting LiDAR sensor measurements are fused, followed by methods for robust motion planning under these circumstances. First, conflicting measurements aggregated in Subjective-Logic-based evidential grid maps are classified. Then, a self-assessment framework evaluates these conflicts and estimates their severity for the overall system by calculating a degradation score. This enables the detection of calibration errors and insufficient sensor setups. In contrast to other motion planning approaches, the information gained from the evidential grid maps is further used inside our proposed path-planning algorithm. Here, the impact of conflicting measurements on the current motion plan is evaluated, and a robust and curious path-planning strategy is derived to plan paths under the influence of conflicting data. This ensures that the system integrity is maintained in severely degraded environment representations which can prevent the unnecessary abortion of planning tasks.

Efficient Path Planning in Large Unknown Environments with Switchable System Models for Automated Vehicles

Large environments are challenging for path planning algorithms as the size of the configuration space increases. Furthermore, if the environment is mainly unexplored, large amounts of the path are planned through unknown areas. Hence, a complete replanning of the entire path occurs whenever the path collides with newly discovered obstacles. We propose a novel method that stops the path planning algorithm after a certain distance. It is used to navigate the algorithm in large environments and is not prone to problems of existing navigation approaches. Furthermore, we developed a method to detect significant environment changes to allow a more efficient replanning. At last, we extend the path planner to be used in the U-Shift concept vehicle. It can switch to another system model and rotate around the center of its rear axis. The results show that the proposed methods generate nearly identical paths compared to the standard Hybrid A* while drastically reducing the execution time. Furthermore, we show that the extended path planning algorithm enables the efficient use of the maneuvering capabilities of the concept vehicle to plan concise paths in narrow environments.