We present an algorithm for synthesizing distributed control policies for networks of mobile robots such that they gather the maximum amount of information about some a priori unknown feature of the environment, e.g. hydration levels of crops or a lost person adrift at sea. Natural motion and communication constraints such as “Avoid obstacles and periodically communicate with all other agents”, are formulated as temporal logic formulae, a richer set of constraints than has been previously considered for this application. Mission constraints are distributed automatically among sub-groups of the agents. Each sub-group independently executes a receding horizon planner that locally optimizes information gathering and is guaranteed to satisfy the assigned mission specification. This approach allows the agents to disperse beyond inter-agent communication ranges while ensuring global team constraints are met. We evaluate our novel paradigm via simulation.
['Kevin Leahy', 'Austin Jones', 'Mac Schwager', 'Calin Belta']
Distributed information gathering policies under temporal logic constraints