Study, design, and evaluation of exploration strategies for autonomous mobile robots

Li, Alberto Quattrini

doi:10.1145/2757001.2757005

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…More recently repartitioning the area was proposed for covering the space by a team of robots [19]. It is worth noting that, the problem of coverage is similar to exploration, especially when considering a much larger-footprint sensor; see for example the work by Quattrini Li et al [20], [21]. Most of the existing studies rely heavily on the existence of communication between robots, however, in some domains it is infeasible to maintain communications.…”

Section: Related Workmentioning

confidence: 99%

Efficient multi-robot coverage of a known environment

Karapetyan

Benson

McKinney

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper addresses the complete area coverage problem of a known environment by multiple-robots. Complete area coverage is the problem of moving an end-effector over all available space while avoiding existing obstacles. In such tasks, using multiple robots can increase the efficiency of the area coverage in terms of minimizing the operational time and increase the robustness in the face of robot attrition. Unfortunately, the problem of finding an optimal solution for such an area coverage problem with multiple robots is known to be NPcomplete. In this paper we present two approximation heuristics for solving the multi-robot coverage problem. The first solution presented is a direct extension of an efficient single robot area coverage algorithm, based on an exact cellular decomposition. The second algorithm is a greedy approach that divides the area into equal regions and applies an efficient single-robot coverage algorithm to each region. We present experimental results for two algorithms. Results indicate that our approaches provide good coverage distribution between robots and minimize the workload per robot, meanwhile ensuring complete coverage of the area.Index Terms-Multiple and distributed robots, path planning, coverage.

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Section: Related Workmentioning

confidence: 99%

Efficient multi-robot coverage of a known environment

Karapetyan

Benson

McKinney

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…This problem is dealt with in Basilico and Amigoni (2011), which presents a multi-criteria method for searching an environment without any a priori information about structure and target locations. There, the authors combine a set of criteria (amount of anticipated free area, probability of a robot being able to send back information, distance between the current position and the place to explore) in order to produce an assessment of the global utility for a candidate point for exploration; the point which maximizes this utility is visited next (see also Li (2015) for a survey on related problems). Contrasting to this problem, in our setting the aim is to use previously available information and improve it in an optimal way.…”

Section: Related Problemsmentioning

confidence: 99%

The Sea Exploration Problem: Data-driven Orienteering on a Continuous Surface

Pedroso¹,

Alpar-Vajk²,

Zhang³

2018

Preprint

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This paper describes a problem arising in sea exploration, where the aim is to schedule the expedition of a ship for collecting information about the resources on the seafloor. The aim is to collect data by probing on a set of carefully chosen locations, so that the information available is optimally enriched. This problem has similarities with the orienteering problem, where the aim is to plan a time-limited trip for visiting a set of vertices, collecting a prize at each of them, in such a way that the total value collected is maximum. In our problem, the score at each vertex is associated with an estimation of the level of the resource on the given surface, which is done by regression using Gaussian processes. Hence, there is a correlation among scores on the selected vertices; this is a first difference with respect to the standard orienteering problem. The second difference is the location of each vertex, which in our problem is a freely chosen point on a given surface.

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