Communication is crucial for human-robot collaborative tasks. In this context, legibility studies movement as the means of implicit communication between robotic systems and a human observer. This concept has been explored mostly for manipulators and humanoid robots. In contrast, little information is available in the literature about legibility of multi-robot systems or swarms, where simplicity and non-anthropomorphism of robots, along with the complexity of their interactions and aggregated behavior impose different challenges that are not encountered in single-robot scenarios. This article investigates legibility of multi-robot systems. Hence, we extend the definition of legibility, incorporating information about high-level goals in terms of the coordination objective of the group of robots, to previous results that focused solely on the legibility of spatial goals. A set of standard multi-robot algorithms corresponding to different coordination objectives are implemented and their legibility is evaluated in a user study, where participants observe the behavior of the multi-robot system in a virtual reality setup and are asked to identify the system’s spatial goal and coordination objective. The results of the study confirmed that coordination objectives are discernible by the users, hence multi-robot systems can be controlled to be legible, in terms of spatial goal and coordination objective.

This paper proposes a distributed coverage control strategy for quadcopters equipped with downward-facing cameras that prevents the appearance of unmonitored areas in between the quadcopters' fields of view (FOVs). We derive a necessary and sufficient condition for eliminating any unsurveilled area that may arise in between the FOVs among a trio of quadcopters by utilizing a power diagram, i.e. a weighted Voronoi diagram defined by radii of FOVs. Because this condition can be described as logically combined constraints, we leverage nonsmooth control barrier functions (NCBFs) to prevent the appearance of unmonitored areas among a team's FOV. We then investigate the symmetric properties of the proposed NCBFs to develop a distributed algorithm. The proposed algorithm can support the switching of the NCBFs caused by changes of the quadcopters composing trios. The existence of the control input satisfying NCBF conditions is analyzed by employing the characteristics of the power diagram. The proposed framework is synthesized with a coverage control law that maximizes the monitoring quality while reducing overlaps of FOVs. The proposed method is demonstrated in simulation and experiment.

We propose, analyze, and experimentally verify a new proactive approach for robot social navigation driven by the robot's “opinion” for which way and by how much to pass human movers crossing its path. The robot forms an opinion over time according to nonlinear dynamics that depend on the robot's observations of human movers and its level of attention to these social cues. For these dynamics, it is guaranteed that when the robot's attention is greater than a critical value, deadlock in decision making is broken, and the robot rapidly forms a strong opinion, passing each human mover even if the robot has no bias nor evidence for which way to pass. We enable proactive rapid and reliable social navigation by having the robot grow its attention across the critical value when a human mover approaches. With human-robot experiments we demonstrate the flexibility of our approach and validate our analytical results on deadlock-breaking. We also show that a single design parameter can tune the trade-off between efficiency and reliability in human-robot passing. The new approach has the additional advantage that it does not rely on a predictive model of human behavior.

This paper presents an algorithm for a team of mobile robots to simultaneously learn a spatial field over a domain and spatially distribute themselves to optimally cover it. Drawing from previous approaches that estimate the spatial field through a centralized Gaussian process, this work leverages the spatial structure of the coverage problem and presents a decentralized strategy where samples are aggregated locally by establishing communications through the boundaries of a Voronoi partition. We present an algorithm whereby each robot runs a local Gaussian process calculated from its own measurements and those provided by its Voronoi neighbors, which are incorporated into the individual robot's Gaussian process only if they provide sufficiently novel information. The performance of the algorithm is evaluated in simulation and compared with centralized approaches.

We present an algorithm for multi-robot coverage of an initially unknown spatial scalar field characterized by a density function, whereby a team of robots simultaneously estimates and optimizes its coverage of the density function over the domain. The proposed algorithm borrows powerful concepts from Bayesian Optimization with Gaussian Processes that, when combined with control laws to achieve centroidal Voronoi tessellation, give rise to an adaptive sequential sampling method to explore and cover the domain. The crux of the approach is to apply a control law using a surrogate function of the true density function, which is then successively refined as robots gather more samples for estimation. The performance of the algorithm is justified theoretically under slightly idealized assumptions, by demonstrating asymptotic no-regret with respect to the coverage obtained with a known density function. The performance is also evaluated in simulation and on the Robotarium with small teams of robots, confirming the good performance suggested by the theoretical analysis.

This paper presents a coverage control algorithm for teams of quadcopters with downward facing visual sensors that prevents the appearance of coverage holes in-between the monitored areas while maximizing the coverage quality as much as possible. We derive necessary and sufficient conditions for preventing the appearance of holes in-between the fields of views among trios of robots. Because this condition can be expressed as logically combined constraints, control nonsmooth barrier functions are implemented to enforce it. An algorithm which extends control nonsmooth barrier functions to hybrid systems is implemented to manage the switching among barrier functions caused by the changes of the robots composing trio. The performance and validity of the proposed algorithm are evaluated in simulation as well as on a team of quadcopters.

Executing multiple tasks concurrently is important in many robotic applications. Moreover, the prioritization of tasks is essential in applications where safety-critical tasks need to precede application-related objectives, in order to protect both the robot from its surroundings and vice versa. Furthermore, the possibility of switching the priority of tasks during their execution gives the robotic system the flexibility of changing its objectives over time. In this paper, we present an optimization-based task execution and prioritization framework that lends itself to the case of time-varying priorities as well as variable number of tasks. We introduce the concept of extended set-based tasks, encode them using control barrier functions, and execute them by means of a constrained-optimization problem, which can be efficiently solved in an online fashion. Finally, we show the application of the proposed approach to the case of a redundant robotic manipulator.

This paper presents a coverage control strategy for teams of quadcopters that ensures that no area is left unsurveyed in between the fields of view of the visual sensors mounted on the quadcopters. We present a locational cost that quantifies the team's coverage performance according to the sensors' performance function. Moreover, the cost function penalizes overlaps between the fields of view of the different sensors, with the objective of increasing the area covered by the team. A distributed control law is derived for the quadcopters so that they adjust their position and zoom according to the direction of ascent of the cost. Control barrier functions are implemented to ensure that, while executing the gradient ascent control law, no holes appear in between the fields of view of neighboring robots. The performance of the algorithm is evaluated in simulated experiments.

In this paper we consider a constrained coverage control problem for a team of mobile robots. The robots are asked to provide sensor coverage over a two-dimensional domain, while being constrained to only move on a curve. The unconstrained coverage problem can be effectively solved by defining a locational cost to be minimized by the robots, in a decentralized fashion, using gradient descent. However, a direct projection of the solution to the unconstrained problem onto the curve may result in a very poor spatial allocation of the team within the two-dimensional domain. Therefore, we propose a modification to the locational cost, which incorporates the constraints, and a convex relaxation that allows us to efficiently minimize a convex approximation of the cost using a decentralized strategy. The resulting algorithm is implemented on a team of mobile robots.

This paper presents a coverage algorithm for multi-robot systems where the robots are equipped with qualitatively different sensing modalities. Unlike previous approaches to the problem of coverage for teams with heterogeneous sensing capabilities, in this paper the robots have access to information about their neighbors' specific sensor modalities. This knowledge affords the ability of ensuring that no robot is tasked with covering features in a region without the required sensing modalities. With this information, a robot can determine which of its neighbors it should coordinate with to cover the environmental features in a region while ignoring robots that are not equipped with that particular sensory capability. We derive a distributed control algorithm that allows the robots to move in a direction of descent relative to a novel locational cost, in order to minimize it. The performance of the algorithm is evaluated on a real robotic platform.