Epistemic Prediction and Planning with Implicit Coordination for Multi-Robot Teams in Communication Restricted Environments

Thus, we introduce Multi-robot systems (MRS) have the potential to assist a coordinated epistemic prediction and planning method in many safety-critical applications such as search and rescue, in which a robot propagates a finite set of belief states military intelligence and surveillance, and inspection representing possible states of other agents in the system and operations where it may be hazardous and costly to deploy empathy states representing a finite set of possible states from humans. Looking to the state-of-the-art, we note that most other agents' perspectives. Subsequently, using epistemic MRS research assumes constant communication between planning, we can formulate a consensus strategy such that robots [1]-[3]. However, within the aforementioned application every distributed belief in the system achieves consensus. For space, long-range communication is often unreliable example, consider Figure 1 where two robots are canvassing or unavailable. Humans adequately cope with such problems, an environment. During disconnection, Robot 1 maintains a performing these tasks collaboratively by extrapolating and set of possible (belief) states for Robot 2 and also a set of empathizing with what other actors might believe if the local (empathy) states that Robot 2 might believe about Robot 1. plan must change at run-time. This subconscious process can Once Robot 2 experiences a failure, it tracks another state be modally represented as epistemic planning, computing in its empathy set. We reason that though Robot 1 holds a and reasoning about multiple predictions and actions while false belief about Robot 2's state, there exists an epistemic accounting for a priori beliefs, current observations, and strategy that can allow robot 1 to find robot 2 (i.e., updating other actors' sensing and mobility capabilities.