Distributed information gathering policies under temporal logic constraints

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.