Distributed Sensing Subject to Temporal Logic Constraints

This paper considers the combination of temporal logic (TL) specifications and local objective functions to create online, multiagent, motion plans. These plans are guaranteed to satisfy a persistent mission TL specification and locally optimize an objective function (e. g. in this paper, a cost based on information entropy). The presented approach decouples the two tasks by assigning sub-teams of agents to fulfill the TL specification, while unassigned agents optimize the objective function locally. This paper also presents a novel decoupling of the classic product automaton based approach while maintaining satisfaction guarantees. We also qualitatively show that optimality loss in the local greedy minimization due to the TL constraints can be approximated based on specification complexity. This approach is evaluated with a set of simulations and an experiment of 6 robots with real sensors.

Authors

• Zachary T. Serlin
• Kevin Leahy 0001
• Roberto Tron
• Calin Belta

Keywords

• Robot sensing systems
• Linear programming
• Task analysis
• Planning
• Automata
• Temporal Constraints
• Temporal Logic
• Temporal Logic Constraints
• Objective Function
• Local Minima
• Multi-agent
• Path Planning
• Information Entropy
• Time Step
• Infinity
• Secondary Objective
• Undirected
• Local Optimum
• Projector
• Information Gathering
• Sensor Measurements
• Multiple Agents
• Optimal Objective Function
• Position Of Agent
• RGB Values
• Entropy Reduction
• Disaster Area
• Swarm Robotics
• Sampling-based Methods
• Ground Robots