Multi-robot Exploration and Mapping Using Self Biddings and Stop Signals

Elizondo-Leal, Juan Carlos; Torres, Gabriel Ramírez; Toscano-Pulido, Gregorio

doi:10.1007/978-3-540-88636-5_59

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…In [12], the authors introduced a problem, where it may happen that when the number of frontiers become less than the number of robots. In that case, some robots may come to a halt as they may believe that coverage is already completed.…”

Section: Buddy Approachmentioning

confidence: 99%

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Remember-All Based Frontier Allocation for Multi-Robot Coverage in Unknown Environments

Parti¹,

Wu²

2016

Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

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Robots are being increasingly used for coverage tasks which were earlier considered too dangerous or monotonous to be performed by humans such as interplanetary exploration, search & rescue missions, etc. Out of all the multi-robot coverage approaches, the frontier based approach is one of the most widely used. Most of the coverage approaches developed so far face the issue of frontier duplication and require access to maps of the environments prior to coverage. In this work, a new frontier based strategy for multi-robot coverage in unknown environments is developed. This new strategy tries to remember and manage all the frontiers discovered so far. It is scalable to multiple robots and does not require prior access to the maps. It also uses a new frontier allocation and coordination strategy, which reduces the frontier duplication and improves the efficiency of coverage.

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Section: Buddy Approachmentioning

confidence: 99%

“…In that case, some robots may come to a halt as they may believe that coverage is already completed. In this case, we adopt the Buddy approach [12] to address this problem.…”

Section: Buddy Approachmentioning

confidence: 99%

Remember-All Based Frontier Allocation for Multi-Robot Coverage in Unknown Environments

Parti¹,

Wu²

2016

Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

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“…Applications include search and rescue, hazardous material handling, planetary exploration, etc. A specific application of path planning is exploration and mapping [1][2][3], where the planner is responsible for efficiently reaching the given objectives. The distance given by the planner to each goal is taken as a utility measure in some exploration algorithms; therefore it is necessary to have an algorithm that provides accurate distances towards the goal.…”

Section: Introductionmentioning

confidence: 99%

The Exact Euclidean Distance Transform: A New Algorithm for Universal Path Planning

Elizondo-Leal¹,

Parra-González²,

Ramírez-Torres³

2013

International Journal of Advanced Robotic Systems

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The Path-Planning problem is a basic issue in mobile robotics, in order to allow the robots to solve more complex tasks, for example, an exploration assignment in which the distance given by the planner is taken as a utility measure. Among the different proposed approaches, algorithms based on an exact cell decomposition of the environment are very popular. In this paper, we present a new algorithm for universal path planning in cell decomposition, using a raster scan method for computing the Exact Euclidean Distance Transform (EEDT) for each cell in the map. Our algorithm computes, for every cell in the map, the point sequence to the goal. For each sequence, the sub-goals are selected near to the vertices of the obstacles, reducing the total distance to the goal without post processing. At the end, we obtain a smooth path up to the goal without the need for post-processing. The paths are computed by visibility verification among the cells, exploiting the processing performed in the neighbouring cells.

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“…In this paper authors are mainly concerned with avoiding interference between agents (avoiding blocking paths) in indoor environments and use Markov hidden model to classify the objects during exploration. In [7], [8] authors use bidding algorithms found in economy for exploration and mapping of unknown indoor environments. Another interesting approach for exploration of indoor environments was proposed in [9], where large number of flying robots deploy over unknown environments and create sensor nodes to navigate other flying or ground robots.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Swarm Based Localization of Hazardous Events

Varga¹,

Piskovic²,

Bogdan³

et al. 2012

Control and Intelligent Systems

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Swarm robotic concept has become a topic of extensive research in hazardous environments where fault tolerant, robust and energyefficient approach is needed. In this paper we present behavioural based algorithm for outdoor environment exploration. This work is inspired by ant colony behaviour models. Our goal is to control agent's motion using simple rules and not to rely on wireless communication. Agents perform random walk combined with simple heuristic pheromone based rules which prevent frequent visits of previously visited locations. To determine heuristic parameters which maximize area coverage, we optimize parameters by simulating swarm behaviour in Matlab. Furthermore, we take into account the agent limitations, such as battery lifetime, and we model a probability success of a mission with respect to that parameter.

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Multi-robot Exploration and Mapping Using Self Biddings and Stop Signals

Cited by 4 publications

References 8 publications

Remember-All Based Frontier Allocation for Multi-Robot Coverage in Unknown Environments

Remember-All Based Frontier Allocation for Multi-Robot Coverage in Unknown Environments

The Exact Euclidean Distance Transform: A New Algorithm for Universal Path Planning

Multi-Agent Swarm Based Localization of Hazardous Events

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