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.
Highlights
Information gathering (IG) is a key task in many robotic applications such as, e.g., magnetic field mapping [1], environmental monitoring [2], or wind field mapping [3]
A common approach often used in the literature to solve multi-robot information gathering (MR-IG) tasks is to use an underlying model of the physical process under study that, together with an information-theoretic metric, is employed to predict the impact of certain robot actions and states
We evaluate our approach in simulations where multiple aerial vehicles, which are subject to collision avoidance and communication constraints, cooperate to map a wind field
Summary
Information gathering (IG) is a key task in many robotic applications such as, e.g., magnetic field mapping [1], environmental monitoring [2], or wind field mapping [3]. A common approach often used in the literature to solve multi-robot information gathering (MR-IG) tasks is to use an underlying model of the physical process under study that, together with an information-theoretic metric, is employed to predict the impact of certain robot actions and states (see, e.g., [1,4]). We use the squared exponential (SE) [5] covariance function due to its capacity to model smooth processes. This function is determined by hyperparameters θ = [σ2f , l, σn2 ] T , being l the characteristic length-scale (informally, "how close" two positions x and x0 have to be to influence each other significantly); σ2f represents the maximum allowable covariance; and σn is the variance of the noise fluctuations [5]
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