Multi-Agent Task Allocation using Cross-Entropy Temporal Logic Optimization

In this paper, we propose a graph-based search method to optimally allocate tasks to a team of robots given a global task specification. In particular, we define these agents as discrete transition systems. In order to allocate tasks to the team of robots, we decompose finite linear temporal logic (LTL) specifications and consider agent specific cost functions. We propose to use the stochastic optimization technique, cross entropy, to optimize over this cost function. The multi-agent task allocation cross-entropy (MTAC-E) algorithm is developed to determine both when it is optimal to switch to a new agent to complete a task and minimize the costs associated with individual agent trajectories. The proposed algorithm is verified in simulation and experimental results are included.

Authors

• Christopher Banks
• Sean Wilson
• Samuel Coogan 0001
• Magnus Egerstedt

Keywords

• Task analysis
• Automata
• Cost function
• Planning
• Resource management
• Switches
• Task Allocation
• Temporal Logic
• Logic Optimization
• Multiagent Task Allocation
• Individual Agency
• Individual Trajectories
• Transit System
• Discrete System
• Swarm Robotics
• Linear Logic
• Local Environment
• State Space
• Ellipsoid
• Control Input
• Actual Cost
• Number Of Agents
• Multiple Agents
• Multi-agent Systems
• Optimal Cost
• Set Of Propositions
• Sample Trajectories
• Finite Sequence
• Decomposition Framework
• System Constraints
• Set Of Agents
• Switching Transition
• Trajectory Length
• Global Goals