Toward efficient trajectory planning: the path-velocity decomposition
International Journal of Robotics Research
Integer and combinatorial optimization
Integer and combinatorial optimization
Solving airline crew scheduling problems by branch-and-cut
Management Science
Collective robotics: from social insects to robots
Adaptive Behavior
Coalition structure generation with worst case guarantees
Artificial Intelligence
Robot Motion Planning
Dynamic Coalition Formation among Rational Agents
IEEE Intelligent Systems
First Results in the Coordination of Heterogeneous Robots for Large-Scale Assembly
ISER '00 Experimental Robotics VII
Cooperative multi-robot box-pushing
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 3 - Volume 3
Convex Optimization
Tunably decentralized algorithms for cooperative target observation
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Planning Algorithms
Methods for task allocation via agent coalition formation
Artificial Intelligence
Abstraction and control for Groups of robots
IEEE Transactions on Robotics
Motion feasibility of multi-agent formations
IEEE Transactions on Robotics
Queues and Artificial Potential Trenches for Multirobot Formations
IEEE Transactions on Robotics
Multi-robot coalition formation
IEEE Transactions on Robotics
Convex Optimization Strategies for Coordinating Large-Scale Robot Formations
IEEE Transactions on Robotics
A class of distributed optimization methods with event-triggered communication
Computational Optimization and Applications
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A key challenge in multi-robot teaming research is determining how to properly enable robots to make decisions on actions they should take to contribute to the overall system objective. This article discusses how many forms of decision making in multi-robot teams can be formulated as optimization problems. In particular, we examine the common multi-robot capabilities of task allocation, path planning, formation generation, and target tracking/observation, showing how each can be represented as optimization problems. Of course, globally optimal solutions to such formulations are not possible, as it is well-known that such problems are intractable. However, many researchers have successfully built solutions that are approximations to the global problems, which work well in practice. While we do not argue that all decision making in multi-robot systems should be based on optimization formulations, it is instructive to study when this technique is appropriate. Future development of new approximation algorithms to well-known global optimization problems can therefore have an important positive impact for many applications in multi-robot systems.