Research Project

Multi-Agent Multi-Task Planner

Description

The current approach in the IT method finds the best plan through the combined roadmap while assuming that a robot of the underlying robot-type roadmap is always available. When planning for a sequence of tasks this is not always the case. There are scenarios in which the optimal plan requires waiting for robots to be able to complete certain subtasks along the way, but a different suboptimal plan does not require waiting and thus becomes the more efficient plan give the constraint of robot availability.

Objective

The goal for this summer is to find an optimal plan for a sequence of tasks using a multi-agent/robot team that accounts for shared and limited resources by dynamically updating the calculated path as tasks and resources are modified.

Deliverables




Mentor

Nancy Amato

Email: namato@illinois.edu

School: University of Illinois Urbana-Champaign

Department: Computer Science

Area of Research: Motion Planning Algorithms

Website: https://parasol.tamu.edu/~amato/

James Motes

Email: jmotes@tamu.edu

School: Texas A&M University

Department: Computer Science

Area of Research: Motion Planning Algorithms

Website: https://parasol.tamu.edu/people/jmotes/