Robot Planning in Uncertain, Dynamic Environments

Many real-world applications require robots to operate in dynamic environments characterized by moving objects or agents whose trajectories are unpredictable. This thesis addresses the challenges posed by such environments by introducing Relative Temporal Probabilistic Roadmaps (Rel-T-PRM), a novel motion planning algorithm that builds upon the Temporal Probabilistic Roadmap (T-PRM) algorithm. The Rel-T-PRM allows for variable dynamic obstacle size, enables robustness with respect to minor changes in time and position and and introduces the concept of waiting until obstacles clear. Furthermore, we leverage Rel-T-PRM’s strengths to propose two replanning strategies. The first attempts to rapidly replan on-the-fly by using waiting to modify the trajectory without needing to modify the path. The second proposed replanning strategy identifies and plans to safe locations, where the robot can safely replan under a longer time horizon. We demonstrate Rel-T-PRM through a variety of simulation experiments on a fixed-base robotic manipulator.

Name thesis:degree_name

Master

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

2024

Author dc:creator

• Cheerla, Anika

Advisor dc:contributor.advisor

• Lozano-Perez, Tomás

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156644

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156644