Task allocation is an important aspect of many multi-robot systems. The features and complexity of multi-robot task allocation (MRTA) problems are dictated by the requirements of the particular domain under consideration. These problems can range from those involving instantaneous distribution of simple, independent tasks among members of a homogenous team, to those requiring the time-extended scheduling of complex interrelated multi-step tasks for members of a heterogenous team related by several constraints. The existing widely used taxonomy for task allocation in multi-robot systems was designed for problems with independent tasks and does not deal with problems with interrelated utilities and constraints. While that taxonomy was a groundbreaking contribution to the MRTA literature, a survey of recent work in MRTA reveals that it is no longer a sufficient taxonomy, due to the increasing importance of interrelated utilities and constraints in realistic MRTA problems under consideration. Thus, in this paper, we present a new, comprehensive taxonomy, iTax, that explicitly takes into consideration the issues of interrelated utilities and constraints. Our taxonomy maps categories of MRTA problems to existing mathematical models from combinatorial optimization and operations research, and hence draws important parallels between robotics and these fields.
In this report, we present an approach to optimal planning and flexible execution for a set of spatially distributed tasks related by temporal ordering constraints such as precedence, synchronization, or non-overlapping constraints. We integrate an optimal planner for task allocation and scheduling with cross-schedule dependencies with a flexible, distributed plan execution strategy. The integrated system performs optimal task allocation and scheduling for tasks related by temporal constraints, and ensures that plans are executed smoothly in the face of real-world variations in operation speed and task execution time. It also ensures that plan execution degrades gracefully in the event of task failure. We demonstrate the capabilities of our approach on a team of three pioneer robots operating in an indoor environment. Experimental results focus on the flexible execution strategy and illustrate that it effectively enables execution of the optimal plan and prevents constraint violations. The overall approach is thus demonstrated to be effective for constrained planning and execution in the face of realworld variations.
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