Overview: A Hierarchical Framework for Plan Generation and Execution in Multi-Robot Systems

March 30, 2018 Β· Declared Dead Β· πŸ› IEEE Intelligent Systems

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Authors Hang Ma, Wolfgang HΓΆnig, Liron Cohen, Tansel Uras, Hong Xu, T. K. Satish Kumar, Nora Ayanian, Sven Koenig arXiv ID 1804.00038 Category cs.AI: Artificial Intelligence Cross-listed cs.MA, cs.RO Citations 28 Venue IEEE Intelligent Systems Last Checked 4 months ago
Abstract
The authors present an overview of a hierarchical framework for coordinating task- and motion-level operations in multirobot systems. Their framework is based on the idea of using simple temporal networks to simultaneously reason about precedence/causal constraints required for task-level coordination and simple temporal constraints required to take some kinematic constraints of robots into account. In the plan-generation phase, the framework provides a computationally scalable method for generating plans that achieve high-level tasks for groups of robots and take some of their kinematic constraints into account. In the plan-execution phase, the framework provides a method for absorbing an imperfect plan execution to avoid time-consuming re-planning in many cases. The authors use the multirobot path-planning problem as a case study to present the key ideas behind their framework for the long-term autonomy of multirobot systems.
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