Communication constraints dictated by hardware often require a multi-robot system to make decisions and take actions locally. Unfortunately, local knowledge may impose limits that ultimately impede global optimality in a decentralized optimization problem. This paper enhances a recent anytime optimal assignment method based on a task-swap mechanism, redesigning the algorithm to address task allocation problems in a decentralized fashion. We propose a fully decentralized approach that allows local search processes to execute concurrently while minimizing interactions amongst the processes, needing neither global broadcast nor a multi-hop communication protocol. The formulation is analyzed in a novel way using tools from group theory and optimization duality theory to show that the convergence of local searching processes is related to a shortest path routing problem on a graph subject to the network topology. Simulation results show that this fully decentralized method converges quickly while sacrificing little optimality.
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The main algorithm was first presented at the 2014 Robotics: Science and Systems conference (Liu et al. 2014).
Note that this notation represents a change from our conference paper (Liu et al. 2014). The arrow direction in the present work better illustrates the underlying task-exchange operator.
The symbols u and v will be used as vertices of graphs in the remainder of the paper. They are not to be confused with their use earlier as dual variables.
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This is one of several papers published in Autonomous Robots comprising the “Special Issue on Robotics Science and Systems”.
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Liu, L., Michael, N. & Shell, D.A. Communication constrained task allocation with optimized local task swaps. Auton Robot 39, 429–444 (2015). https://doi.org/10.1007/s10514-015-9481-9
- Decentralized task allocation
- Communication constraint
- Task swaps
- Permutation group