5 accepted papers
This work addresses the problem of learning a model of a dynamic environment using many independent Hidden Markov Models (HMMs) with a limited number of observations available per iteration. Many techniques exist to model dynamic environments, but do not consider how to deploy robots to build this m…
This paper proposes a method to deploy teams of robots with constrained energy capacities to persistently maintain a map of an uncertain environment. Typical occupancy map approaches assume a static world; however, we introduce a decay in confidence that degrades the occupancy probability of grid ce…
This paper presents an online optimization-based approach to compute trajectories to enable substitution of robots in formation-based deployments with durations that exceed the energy capacity of individual systems. The proposed algorithm computes trajectories in a multi-robot context to ensure a co…
We propose the Stochastic Multi-Robot Persistent Coverage Problem (SMRPCP) and correspondant methodology to compute an optimal schedule that enables a fleet of energy-constrained unmanned aerial vehicles to repeatedly perform a set of tasks while maximizing the frequency of task completion and prese…
In this paper, we present an algorithm to solve the Multi-Robot Persistent Coverage Problem (MRPCP). Here, we seek to compute a schedule that will allow a fleet of agents to visit all targets of a given set while maximizing the frequency of visitation and maintaining a sufficient fuel capacity by re…