Semantically-informed coordinated multirobot exploration of relevant areas in search and rescue settings

Cipolleschi, Riccardo; Giusto, Michele; Li, Alberto Quattrini; Amigoni, Francesco

doi:10.1109/ecmr.2013.6698845

Cited by 9 publications

(11 citation statements)

References 17 publications

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“…Specifically, we propose a method for evaluating candidate locations, which is a variant of that presented by Basilico and Amigoni (2011), and a method for allocating robots to candidate locations. The contributions of this paper significantly extend the preliminary results of Cipolleschi et al (2013) and include a more complete experimental analysis of the behavior and benefits of the proposed multirobot system, also involving additional experiments using stateof-the-art approaches for comparison and the deployment of more robots.…”

Section: Introductionmentioning

confidence: 88%

A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

Cipolleschi

Giusto

et al. 2015

Auton Robot

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Coordinated multirobot exploration involves autonomous discovering and mapping of the features of initially unknown environments by using multiple robots. Autonomously exploring mobile robots are usually driven, both in selecting locations to visit and in assigning them to robots, by knowledge of the already explored portions of the environment, often represented in a metric map. In the literature, some works addressed the use of semantic knowledge in exploration, which, embedded in a semantic map, associates spatial concepts (like 'rooms' and 'corridors') with metric entities, showing its effectiveness in improving the total area explored by robots. In this paper, we build on these results and propose a system that exploits semantic information to push robots to explore relevant areas of initially unknown environments, according to a priori information provided by human users. Discovery of relevant areas is significant in some search and rescue settings, in which human rescuers can instruct robots to search for victims in specific areas, for example in cubicles if a disaster happened in an office building during working hours. We propose to speed up the exploration of specific areas by using semantic information both to select locations to visit and to determine the number of robots to allocate to those locations. In this way, for exam-B Alberto Quattrini Li ple, more robots could be assigned to a candidate location in a corridor, so the attached rooms can be explored faster. We tested our semantic-based multirobot exploration system within a reliable robot simulator and we evaluated its performance in realistic search and rescue indoor settings with respect to state-of-the-art approaches.

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Section: Introductionmentioning

confidence: 88%

A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

Cipolleschi

Giusto

et al. 2015

Auton Robot

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“…One of my contributions (Cipolleschi, Giusto, Quattrini Li, and Amigoni, 2013; Quattrini Li, Cipolleschi, Giusto, and Amigoni, 2014) lies on defining exploration strategies and coordination methods that embed information coming from semantic maps in order to significantly improve the exploration of relevant areas of indoor environments besides that of the total area. Imagine for example the following search and rescue scenario: a disaster happens in a building during lunch time, so rescuers believe that victims are most likely located in the canteens.…”

Section: To Improve Exploration Strategies and Coordination Methodsmentioning

confidence: 99%

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

2015

AI Matters

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“…For instance, the edge of the area in [220] was known and in [128,133], the equation to describe the covered area was given. [46] provided semantic information to assist the exploration. In many path planning and navigation problems like [51], the limited knowledge was given which was the direction from the starting point to the target.…”

Section: Collision-free Navigationmentioning

confidence: 99%

Decentralized Collision-Free Control of Multiple Robots in 2D and 3D Spaces

Yang

2017

Preprint

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Decentralized control of robots has attracted huge research interests. However, some of the research used unrealistic assumptions without collision avoidance. This report focuses on the collision-free control for multiple robots in both complete coverage and search tasks in 2D and 3D areas which are arbitrary unknown. All algorithms are decentralized as robots have limited abilities and they are mathematically proved.The report starts with the grid selection in the two tasks. Grid patterns simplify the representation of the area and robots only need to move straightly between neighbor vertices. For the 100% complete 2D coverage, the equilateral triangular grid is proposed. For the complete coverage ignoring the boundary effect, the grid with the fewest vertices is calculated in every situation for both 2D and 3D areas.The second part is for the complete coverage in 2D and 3D areas. A decentralized collision-free algorithm with the above selected grid is presented driving robots to sections which are furthest from the reference point. The area can be static or expanding, and the algorithm is simulated in MATLAB.Thirdly, three grid-based decentralized random algorithms with collision avoidance are provided to search targets in 2D or 3D areas. The number of targets can be known or unknown. In the first algorithm, robots choose vacant neighbors randomly with priorities on unvisited ones while the second one adds the repulsive force to disperse robots if they are close. In the third algorithm, if surrounded by visited vertices, the robot will use the breadth-first search algorithm to go to one of the nearest unvisited vertices via the grid. The second search algorithm is verified on Pioneer 3-DX robots. The general way to generate the formula to estimate the search time is demonstrated. Algorithms are compared with five other algorithms in MATLAB to show their effectiveness.

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Semantically-informed coordinated multirobot exploration of relevant areas in search and rescue settings

Cited by 9 publications

References 17 publications

A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

A semantically-informed multirobot system for exploration of relevant areas in search and rescue settings

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

Decentralized Collision-Free Control of Multiple Robots in 2D and 3D Spaces

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