Distributed Multi-Robot Information Gathering under Spatio-Temporal Inter-Robot Constraints

Abstract

Information gathering (IG) algorithms aim to intelligently select the mobile robotic sensor actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, a wind field, or a magnetic field. Recently, multiple IG algorithms that benefit from multi-robot cooperation have been proposed in the literature. Most of these algorithms employ discretization of the state and action spaces, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, they cannot deal with inter-robot restrictions such as collision avoidance or communication constraints. This paper presents a novel approach for multi-robot information gathering (MR-IG) that tackles the two aforementioned restrictions: (i) discretization of robot’s state space, and (ii) dealing with inter-robot constraints. Here we propose an algorithm that employs: (i) an underlying model of the physical process of interest, (ii) sampling-based planners to plan paths in a continuous domain, and (iii) a distributed decision-making algorithm to enable multi-robot coordination. In particular, we use the max-sum algorithm for distributed decision-making by defining an information-theoretic utility function. This function maximizes IG, while fulfilling inter-robot communication and collision avoidance constraints. We validate our proposed approach in simulations, and in a field experiment where three quadcopters explore a simulated wind field. Results demonstrate the effectiveness and scalability with respect to the number of robots of our approach.

Adaptive Visual Information Gathering for Autonomous Exploration of Underwater Environments

“…Information Gathering (IG) algorithms guide such exploration using an information metric which represents the informativeness of the environment variable under study in particular locations, and this metric is used to drive the data recording process towards the more informative spots whilst minimizing a cost, such as the number of measurements, the navigation distance or the mission time. IG algorithms have been used for different types of exploration, for instance; (a) goal-based, where the objective is traveling from an initial location to a goal location with a given cost budget [8], [9], [10], (b) front-based, for traversing a given threshold area [11], [12], (c) frontier-based, usually for indoor environment mapping [13], [14], [15], [16], (d) multimodal, using different data sources [17], [18], (e) multirobot, using multiple robots [19], [20], [21], (f) hotspot-based, to find environmental variable hotspots [22], [23], and (g) coveragebased, for environmental variables dense estimation [24], [25], which is, in fact, the focus of this work.…”

“…The information gain I is computed from the prediction obtained with the GP model at the node location. We have considered two options to compute such information, either 19) and (20), respectively.…”

Design and Verification of Multi-Agent Systems with the Use of Bigraphs

“…In this work, we will focus on multi-agent robotic systems (MARS). The literature [12][13][14][15][16] is replete with examples of various applications of multi-agent robotic systems. There are also methodologies and tools [10,17] to design such systems.…”

“…There is no consensus on how to design such systems in general and current solutions come from different areas of science. The most common paradigms used to design MARS include software design patterns [16], control theory [12,13], optimization theory or combinations of the above [15]. Some examples are utilizing mathematical logic in MARS design [18], but they are much less common.…”