This paper proposes a heterogeneous teaming solution to the problem of target discovery and monitoring in unknown, non-convex environments. The team consists of two types of agents: agile agents with sensors capable of mapping their surroundings and slower agents that are capable of monitoring or servicing discovered targets. We propose an exploration algorithm that utilizes the IRIS algorithm to generate a graph decomposition from collision free ellipses contained within the environment. This graph is passed to the monitoring agents who execute polynomial complexity assignment and touring algorithms to generate high quality path plans which service all discovered targets. Our algorithmic structure allows the team to solve the problems of exploration, target discovery, assignment, and monitoring within unknown, non-convex environments efficiently using limited information. The performance of our proposed method is verified through batch simulations and complexity analysis.

This paper proposes a formulation for the risk-aware path planning problem which utilizes multi-objective optimization to dynamically plan trajectories that satisfy multiple complex mission specifications. In the setting of persistent monitoring, we develop a method for representing environmental information and risk in a way that allows for local sampling to generate Pareto-dominant solutions over a receding horizon. We propose two algorithms capable of solving these problems: a dense sampling approach and an improved method utilizing noisy gradient descent. Simulation results demonstrate the efficacy of our methods at persistently gathering information while avoiding risk, robust to randomly-generated environments.

This paper proposes an algorithm for generating Pareto-optimal privacy-aware trajectories for multi-robot coverage. Our approach utilizes a genetic algorithm to generate a set of modified trajectories for a team of robots that wishes to obscure its goal from an observer. A novel velocity-constrained crossover algorithm ensures all child trajectories are feasible for a holonomic vehicle. The Pareto front of generated trajectories allows a team to select an allowable trade-off between privacy and coverage cost given within their task. Simulation results demonstrate the performance of our algorithm in Voronoi-based coverage control. We show our approach successfully obscures the objective from our proposed observer.