Efficient Informative Sensing using Multiple Robots

Abstract

The need for efficient monitoring of spatio-temporal dynamics in large environmental applications, such as the water quality monitoring in rivers and lakes, motivates the use of robotic sensors in order to achieve sufficient spatial coverage. Typically, these robots have bounded resources, such as limited battery or limited amounts of time to obtain measurements. Thus, careful coordination of their paths is required in order to maximize the amount of information collected, while respecting the resource constraints. In this paper, we present an efficient approach for near-optimally solving the NP-hard optimization problem of planning such informative paths. In particular, we first develop eSIP (efficient Single-robot Informative Path planning), an approximation algorithm for optimizing the path of a single robot. Hereby, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to quantify the amount of information collected. We prove that the mutual information collected using paths obtained by using eSIP is close to the information obtained by an optimal solution. We then provide a general technique, sequential allocation, which can be used to extend any single robot planning algorithm, such as eSIP, for the multi-robot problem. This procedure approximately generalizes any guarantees for the single-robot problem to the multi-robot case. We extensively evaluate the effectiveness of our approach on several experiments performed in-field for two important environmental sensing applications, lake and river monitoring, and simulation experiments performed using several real world sensor network data sets.

Adaptive Informative Path Planning in Metric Spaces

“…If we focus on information gathering only and ignore robot movement cost, IPP becomes sensor placement, view planning, or ODT, which admits efficient solutions through, e.g., submodular optimization, in both nonadaptive [15] and adaptive settings [7,13]. If we account for movement cost, there are several nonadaptive algorithms with performance guarantee (e.g., [12,19]). …”

“…It is probably unsurprising that the robot actually does not need to return to the start position, line [18][19] in each recursive step (Algorithm 1). This is mainly to simplify For comparison, we also implemented two greedy algorithms.…”

Branch and bound for informative path planning

“…The mixed integer approach in [10] allows for a large number of constraints to be handled, but requires linear objective functions. A more realistic objective function is used in [11], and the recursive-greedy algorithm for submodular orienteering from [12] is used to solve it. Our prior work in [13] is also based on this submodular orienteering approach, and considers extensions specific to AUVs.…”

“…A branch and bound extension to the recursive-greedy algorithm is also presented in [11], using the greedy sensor placement algorithm as an upper bound on informative path planning. Although they show that this can speed up the recursive-greedy algorithm, the solutions found by recursivegreedy are not necessarily optimal to begin with.…”

On active target tracking and cooperative localization for multiple aerial vehicles

“…Decentralized controllers can be obtained from (8), by replacing any unavailable global quantity with local estimates [5], [14].…”