We propose a new formulation for the multi-robot task planning and allocation problem that incorporates (a) precedence relationships between tasks; (b) coordination for tasks allowing multiple robots to achieve increased efficiency; and (c) cooperation through the formation of robot coalitions for tasks that cannot be performed by individual robots alone. In our formulation, the tasks and the relationships between the tasks are specified by a task graph. We define a set of reward functions over the task graph's nodes and edges. These functions model the effect of robot coalition size on task performance while incorporating the influence of one task's performance on a dependent task. Solving this problem optimally is NP-hard. However, using the task graph formulation allows us to leverage min-cost network flow approaches to obtain approximate solutions efficiently. Additionally, we explore a mixed integer programming approach, which gives optimal solutions for small instances of the problem but is computationally expensive. We also develop a greedy heuristic algorithm as a baseline. Our modeling and solution approaches result in task plans that leverage task precedence relationships and robot coordination and cooperation to achieve high mission performance, even in large missions with many agents.

We investigate and develop algorithms for social fairness in coverage control problems. Existing coverage control methods are efficient, optimizing the average expected distance from any event to the nearest robot. However, in societal applications like disaster response or transportation, these conventional objectives lead to disparate coverage costs with respect to different groups within a population. We formulate social fairness for coverage control as the minimization of the maximum coverage cost among a set of groups within a population. Our approach uses Voronoi iteration to solve this novel problem by approximating the non-differentiable objective with the log-sum-exp and defining a gradient based controller that prioritizes fairness while also optimizing average performance when disparities between groups are low. We show convergence properties of this proposed control law and demonstrate the approach in simulations of randomly generated population densities as well as environments generated from U.S. census data on population rates and demographics. Our approach provides greater fairness than existing methods while maintaining similar computational time and convergence properties.

In this paper, we present an online adaptive planning strategy for a team of robots with heterogeneous sensors to sample from a latent spatial field using a learned model for decision making. Current robotic sampling methods seek to gather information about an observable spatial field. However, many applications, such as environmental monitoring and precision agriculture, involve phenomena that are not directly observable or are costly to measure, called latent phenomena. In our approach, we seek to reason about the latent phenomenon in real-time by effectively sampling the observable spatial fields using a team of robots with heterogeneous sensors, where each robot has a distinct sensor to measure a different observable field. The information gain is estimated using a learned model that maps from the observable spatial fields to the latent phenomenon. This model captures aleatoric uncertainty in the relationship to allow for information theoretic measures. Additionally, we explicitly consider the correlations among the observable spatial fields, capturing the relationship between sensor types whose observations are not independent. We show it is possible to learn these correlations, and investigate the impact of the learned correlation models on the performance of our sampling approach. Through our qualitative and quantitative results, we illustrate that empirically learned correlations improve the overall sampling efficiency of the team. We simulate our approach using a data set of sensor measurements collected on Lac Hertel, in Quebec, which we make publicly available.

This paper proposes a new reactive mission planning algorithm for multiple robots that operate in unknown environments. The robots are equipped with individual sensors that allow them to collectively learn and continuously update a map of the unknown environment. The goal of the robots is to accomplish complex tasks, captured by global co-safe Linear Temporal Logic (LTL) formulas. The majority of existing temporal logic planning approaches rely on discrete abstractions of the robot dynamics operating in known environments and, as a result, they cannot be applied to the more realistic scenarios where the environment is initially unknown. In this paper, we address this novel challenge by proposing the first reactive, and abstraction-free LTL planning algorithm that can be applied for complex mission planning of multiple robots operating in unknown environments. Our algorithm is reactive in the sense that temporal logic planning is adapting to the updated map of the environment and abstraction-free as it does not rely on designing abstractions of robot dynamics. Our proposed algorithm is complete under mild assumptions on the structure of the environment and the sensor models. Our paper provides extensive numerical simulations and hardware experiments that illustrate the theoretical analysis and show that the proposed algorithm can address complex planning tasks in unknown environments.