A Constraint Based Approach to Interleaving Planning and Execution for Multirobot Coordination

Enabling multiple robots to work together as a team is a difficult problem. Robots must decide amongst themselves who should work on which goals and at what time each goal should be achieved. Since the team is situated in some physical environment, the robots must consider travel time in these decisions. This is particularly challenging in time critical domains where goal rewards decrease over time and for tightly coupled coordination where multiple robots must work together on each goal. Further complications arise when the system is subjected to additional constraints on the ordering of the goals, the use of resources, or the allocation of robots to goals. Optimal team behavior can only be achieved when robots simultaneously consider path planning, task allocation, scheduling, and these additional system constraints. In dynamic and uncertain environments, robots need to reevaluate these decisions as they discover new information. Communication failures may mean that robots are unable to consult as a whole team while replanning. The proposed thesis addresses these challenges with four main points. First, we propose modeling the problem facing the robot team, a time critical tight coordination team planning problem, using a mathematical programming model, or, more specifically, as a mixed integer linear programming problem. We describe algorithms for combining traditional solution techniques for mathematical programming models with domain specific heuristics resulting in a system that is declarative, transparent to humans, efficient, progressive, and capable of providing error bounds on solution quality. Second, we show that it is possible to reason about a variety of constraints on the goals, robots, and resources of the system within the proposed framework. We describe a general first order logic constraint language for multirobot coordination which we incorporate into our system. Third, the proposed thesis provides a framework for interleaving planning and execution in a dynamic environment. By adopting a novel model for communication failure in robot teams, robots can explicitly reason about the effects on team performance of changes in the team plan when some of the team is out of communication and unaware of these changes. The tradeoff between adapting the plan to optimally respond to new information and preserving the original plan for consistency can be incorporated into our proposed mixed integer linear programming model in a principled manner, thus improving team performance. Similarly, a number of various tradeoffs can be represented in the declarative mathematical programming model. We propose addressing one more such tradeoff and reasoning about the robustness of team plans to certain types of failure or new information. Preliminary results in an abstract simulator indicate the feasibility of this approach and the benefits of some of these extensions. The proposed thesis will further investigate the effects of reasoning about constraints, communication failures, and plan robustness on a variety of benchmarks and on realistic robots in a high fidelity simulator.