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 model. Additionally, there are many techniques for exploring environments that do not consider how to prioritize regions when resources, in terms of robots to deploy and deployment durations, are limited. Here, we consider an environment model consisting of a series of HMMs that evolve over time independently and can be directly observed. At each iteration, we must determine which HMMs to observe in order to maximize the gain in model accuracy. We present a utility measure that balances a Pearson's χ2goodness-of-fit of the dynamics model with Mutual Information (MI) to ensure that observations are allocated to maximize the convergence rate of all HMMs, resulting in a faster convergence to higher steady-state model confidence and accuracy than either χ2or MI alone.

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 cells and promotes revisitation. Further, sections of the map whose occupancy differs between observations are visited more frequently, while unchanging areas are scheduled less frequently. While naive planning is intractable through the entire space of multi-agent spatio-temporal states, the proposed algorithm decouples planning such that constraints are resolved separately by solving tracTable subproblems. We evaluate this approach in simulation and show how the uncertainty of our world model is maintained below an acceptable threshold while the algorithm retains a tractable computation time.

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 collision-free exchange, even where congestion is a concern. The quality of the resulting trajectories is determined by the amount of time spent deviating from the original plan while maintaining collision-free, speed-limited polynomial splines. The algorithm is shown through simulation and experiments to be viable with average deviation time gaps of less than 16 seconds and average computation times of under 3 minutes for the presented scenarios with varying numbers of robots and deployment specifications.

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 refueling at depots. We also present a heuristic method to allow us to compute bounded suboptimal results in real time. The results produced by our algorithm will allow a team of robots to efficiently cover a given set of targets or tasks persistently over long periods of time, even when the cost to transition between tasks is dynamic.

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 preserving energy reserves via recharging depots. The approach enables online modeling of uncertain task costs and yields a schedule that adapts according to an evolving energy expenditure model. A fast heuristic method is formulated that enables online generation of a schedule that concurrently maximizes task completion frequency and avoids the risk of individual robot energy-depletion and consequential platform failure. Failure mitigation is introduced through a recourse strategy that routes robots based on acceptable levels of risk. Simulation and experimental results evaluate the efficacy of the proposed methodology and demonstrate online system-level adaptation due to increasingly certain costs models acquired during the deployment execution.