Visibility-Aware Motion Planning

The motion planning problem, of deciding how to move to achieve a goal, is ubiquitous in robotics. In many robotics applications, there is a map of the environment that is generally useful, but typically outdated as it does not include information about unknown obstacles, such as clutter. This thesis addresses the problem of planning for a robot with an onboard obstacle-detection sensor. The planning objective is to remain safe with respect to unknown obstacles by guaranteeing that the robot will not move into any region of the workspace before observing it. Although much work has addressed a version of this problem in which the field of view of the sensor is a sphere around the robot, we address robots with a limited field of view, which may arise from sensor limitations or self-occlusions in the case of mobile manipulation robots. We provide a formal definition of the problem, which we call Visibility-Aware Motion Planning (VAMP), and several solution methods with different computational trade-offs. We demonstrate the behavior of these planning algorithms in illustrative planar domains. The key to an efficient solution is to aggressively prune paths, while ensuring that the overall search strategy is sound and complete. We demonstrate that motion planning problems like VAMP benefit from a path-dependent formulation, in which the state at a search node is represented implicitly by the path to that node. The straightforward approach to computing the feasibility of a successor node in such a path-dependent formulation takes time linear in the path length to the node, in contrast to a (possibly very large) constant time for a more typical search formulation. For long-horizon plans, this linear-time computation for each node becomes prohibitive. To improve upon this, we introduce the use of a fully persistent spatial data structure (FPSDS). We apply a FPSDS to VAMP search, by using a nearest-neighbor data structure to perform bounding-volume queries. We demonstrate an asymptotic and practical improvement in the runtime of finding VAMP solutions in large domains. To the best of our knowledge, this is the first use of a fully persistent data structure for accelerating motion planning

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Goretkin, Gustavo Nunes

Advisors dc:contributor.advisor

• Kaelbling, Leslie Pack
• Lozano-Pérez, Tomás

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/143248

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/143248