Coverage & cooperation: Completing complex tasks as quickly as possible using teams of robots

As the robotics industry grows and robots enter our homes and public spaces, they are increasingly expected to work in cooperation with each other. My thesis focuses on multirobot planning, specifically in the context of coverage robots, such as robotic lawnmowers and vacuum cleaners. Two problems unique to multirobot teams are task allocation and search. I present a task allocation algorithm which balances the workload amongst all robots in the team with the objective of minimizing the overall mission time. I also present a search algorithm which robots can use to find lost teammates. It uses a probabilistic belief of a target robot’s position to create a planning tree and then searches by following the best path in the tree. For robust multirobot coverage, I use both the task allocation and search algorithms. First the coverage region is divided into a set of small coverage tasks which minimize the number of turns the robots will need to take. These tasks are then allocated to individual robots. During the mission, robots replan with nearby robots to rebalance the workload and, once a robot has finished its tasks, it searches for teammates to help them finish their tasks faster

Establishing connection between disconnected robots using a semi-Markov model

A team of 5 robots starts in different locations in an environment and must find each other so that they can establish a fully connected communication network amongst the team. In this video, the environment is represented by a graph with the robots able to stay at a vertex or move along an edge. Robots are only able to communicate if they are located at vertices which share an edge (or edges whose endpoints share an edge). Since the robots are not located near each other initially, they cannot communicate and must search based on their belief of the other robots' positions. The beliefs are updated based on a semi-Markov model and the robots plan their paths to maximize the probability of establishing connection with some other robot given their belief. In the videos, the translucent circles represent the red robot's belief of the other robots' positions. When two robots come within communication range, they combine their beliefs and begin planning their paths together to maximize their joint probability of finding another robot (given their shared belief) while satisfying the constraint that they remain connected to each other. These videos represent the fastest, median, and slowest times it took for the team of 5 robots to become fully connected out of the 50 simulations I performed with different random environments. The robots start with exact knowledge of each other's positions but then move randomly for the first 50 time steps so that when they begin searching their beliefs of each other's positions are not entirely certain