IEEE Trans Robot 28(1):158–171ĭai J, Benini A, Lin H, Antsaklis PJ, Rutherford MJ, Valavanis KP (2016) Learning-based formal synthesis of cooperative multi-agent systems with an application to robotic coordination. IEEE, pp 2718–2723Ĭhen Y, Ding XC, Stefanescu A, Belta C (2012) Formal approach to the deployment of distributed robotic teams. In: Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on. IEEE Robot Autom Mag 14(1):61–70Ĭhen Y, Ding XC, Belta C (2011) Synthesis of distributed control and communication schemes from global LTL specifications. IEEE, pp 7712–7718īelta C, Bicchi A, Egerstedt M, Frazzoli E, Klavins E, Pappas GJ (2007) Symbolic planning and control of robot motion. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). MIT Press, Cambridgeīanks C, Wilson S, Coogan S, Egerstedt M (2020) Multi-agent task allocation using cross-entropy temporal logic optimization. Extensive numerical simulations are also performed to evaluate the scalability, computational complexity, and execution efficiency of the proposed framework and show its advantages over the centralized task and motion planning strategy.īaier C, Katoen JP (2008) Principles of model checking. The proposed framework is demonstrated with a multi-robot experiment for manufacturing tasks in a lab setting. The robots then plan motions to execute the tasks associated with the minimal cost task plans. Each SPA can generate a minimal cost task plan by taking into account the costs of multi-robot tasking. The overall robot assignments and SPA can guarantee the MRS to satisfy all the subtask automata. The capability transition system of the assigned robots and these subtask automata synthesize a corresponding set of subtask planning automata (SPA), each of which is either an independent satisfaction of an individual subtask automaton or a concurrent satisfaction of multiple subtask automata. Robots are assigned to the smallest task component in each subtask automaton. A parallel decomposition algorithm is developed to iteratively decompose a global task specification into a set of smaller subtask automata. This paper presents an automaton-based task and motion planning framework for multi-robot systems (MRS) to satisfy finite words of linear temporal logic (LTL) task specifications in parallel and concurrently.
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