Informed Asymptotically Near-Optimal Planning for Field Robots with Dynamics
|Title||Informed Asymptotically Near-Optimal Planning for Field Robots with Dynamics|
|Publication Type||Conference Paper|
|Year of Publication||2017|
|Authors||Littlefield, Z, Bekris, KE|
|Conference Name||11th Conference on Field and Service Robotics (FSR) 2017|
|Conference Location||Zurich, Switzerland|
Recent progress in sampling-based planning has provided performance guarantees in terms of optimizing trajectory cost even in the presence of significant dynamics. The STABLE SPARSE RRT (SST) algorithm has these desirable path quality properties and achieves computational efficiency by maintaining a sparse set of state-space samples. The current paper focuses on field robotics, where workspace information can be used to effectively guide the search process of a planner, and improves the computational performance of SST by appropriately utilizing such information in the form of heuristics. The workspace information guides the exploration process of the planner and focuses it on the useful subset of the state space. The resulting Informed-SST is evaluated in several scenarios involving either ground vehicles or quadrotors. This includes testing for a physically-simulated vehicle over uneven terrain, which is a computationally expensive challenge.