Topics in Multi - Robot Teamwork

In recent years there is a growing interest in multi-robots systems, where a group of N robots are working collaboratively in order to execute a given task. This thesis addresses two open challenges in multi-robot systems. The first is the challenge of deciding on which robot, out of a group of robots, should travel to a goal location, to carry out a task there. The second is the challenge of integrating multiple and different multi-robot controllers into a robust system. A common decision problem in multi-robot applications involves deciding on which robot, out of a group of N robots, should travel to a goal location, to carry out a task there. Trivially, this decision problem can be solved greedily, by selecting the robot with the shortest expected travel time. However, this ignores the inherent uncertainty in path traversal times; we may prefer a robot that is slower (but always takes the same time), over a robot that is expected to reach the goal faster, but on occasion takes a very long time to arrive. In the first part of this thesis we make several contributions that address this challenge. First, we bring to bear economic decision-making theory, to distinguish between different selection policies, based on risk (risk averse, risk seeking, etc.). Second, we introduce social regret (the difference between the actual travel time by the selected robot, and the hypothetical time of other robots) to augment decision-making in practice. Then, we carry out experiments in simulation and with real robots, to demonstrate the usefulness of the selection procedures under real-world settings, and find that travel-time distributions have repeating characteristics. In the second part, we address the challenge of integrating multiple and different multi-robot controllers into a robust system. Multi-robot formations are of increasing interest to robotics researchers (as a canonical research problem), and to robot system builders (e.g.,for unmanned convoys). Indeed, there exists vast literature on various techniques for maintaining formations in a variety of settings, and for a variety of robots. However, little attention has been given to the possibility of using multiple formation controllers, all integrated together for greater formation robustness. In this part, we make two contributions. First, we address a key challenge in integration, that of joint distributed selection and execution of the correct controller, at the same time. We demonstrate how to utilize a teamwork software engine to automate this joint selection. Second, we describe one such integrated system, which uses several different formation-maintenance controllers for greater robustness.