Leap-Frog Path Design for Multi-Robot Cooperative Localization

Tully, Stephen; Kantor, George; Choset, Howie

doi:10.1007/978-3-642-13408-1_28

Cited by 33 publications

(27 citation statements)

References 11 publications

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“…[ Tully et al, 2010] used a fleet of three robots, each one of them equipped with a red ball (easily detectable using standard computer vision techniques) to follow a strategic path in formation to minimize the localization error. The mentioned robot fleet is shown in Fig.…”

Section: Coverage Under Uncertaintymentioning

confidence: 99%

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A survey on coverage path planning for robotics

Galceran

Carreras

2013

Robotics and Autonomous Systems

1,192

602

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Coverage Path Planning (CPP) is the task of determining a path that passes over all points of an area or volume of interest while avoiding obstacles. This task is integral to many robotic applications, such as vacuum cleaning robots, painter robots, autonomous underwater vehicles creating image mosaics, demining robots, lawn mowers, automated harvesters, window cleaners and inspection of complex structures, just to name a few. A considerable body of research has addressed the CPP problem. However, no updated surveys on CPP reflecting recent advances in the field have been presented in the past ten years. In this paper, we present a review of the most successful CPP methods, focusing in the achievements made in the past decade. Furthermore, we discuss reported field applications of the described CPP methods. This work aims to become a starting point for researchers who are initiating their endeavors in CPP. Likewise, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works.

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Section: Coverage Under Uncertaintymentioning

confidence: 99%

“…Leap-frog strategy [Tully et al, 2010] On-line Obstacles not considered Robots in a team use each other as beacons alternatively.…”

Section: Coverage Under Uncertaintymentioning

confidence: 99%

A survey on coverage path planning for robotics

Galceran

Carreras

2013

Robotics and Autonomous Systems

1,192

602

View full text Add to dashboard Cite

show abstract

“…Fusing the leap-frog method [19] and the signal strength of a ZigBee network [20] as the measuring tool and also as communication media for the robots in the swarm; a cooperative localization and communication system is developed.…”

Section: A Localizationmentioning

confidence: 99%

“…There are many situations where an external positioning system, such as GPS, is unavailable to the robots. A team of robots can employ cooperative localization to incorporate relative sensor measurements into a Kalman filter framework that estimates the pose of the robots [19]. Many of the cooperative localization systems, using infrared, ultrasound and Wi-Fi, have been developed for indoor localization after GPS but each of them shows its merits and weaknesses at the same time.…”

Section: Introductionmentioning

confidence: 99%

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An olfactory-based robot swarm navigation method

Marjovi

Nunes

Sousa

et al. 2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-This paper presents a novel robot swarming navigation algorithm in order to find the odor sources in an unknown environment, based on the ability of each swarm member to sense the odor. Each robot in the swarm has a cooperative localization system which uses wireless network as a mean of measuring the distance from the other robots. In this method, at least three robots act as stationary measurement beacons while the other robots of the swarm navigate in the environment towards the odor source. In the next step, the roles of the robots will be switched and some other robots will act as beacons. The experimental tests report a good result in finding the odor source and also the accuracy of localization system 1 .

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Coverage Path Planning with Real-time Replanning and Surface Reconstruction for Inspection of Three-dimensional Underwater Structures using Autonomous Underwater Vehicles

et al. 2014

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We present a novel method for planning coverage paths for inspecting complex structures on the ocean floor using an autonomous underwater vehicle (AUV). Our method initially uses a 2.5-dimensional (2.5D) prior bathymetric map to plan a nominal coverage path that allows the AUV to pass its sensors over all points on the target area. The nominal path uses a standard mowing-the-lawn pattern in effectively planar regions, while in regions with substantial 3D relief it follows horizontal contours of the terrain at a given offset distance. We then go beyond previous approaches in the literature by considering the vehicle's state uncertainty rather than relying on the unrealistic assumption of an idealized path execution. Toward that end, we present a replanning algorithm based on a stochastic trajectory optimization that reshapes the nominal path to cope with the actual target structure perceived in situ. The replanning algorithm runs onboard the AUV in real time during the inspection mission, adapting the path according to the measurements provided by the vehicle's range-sensing sonars. Furthermore, we propose a pipeline of state-of-the-art surface reconstruction techniques we apply to the data acquired by the AUV to obtain 3D models of the inspected structures that show the benefits of our planning method for 3D mapping. We demonstrate the efficacy of our method in experiments at sea using the GIRONA 500 AUV, where we cover part of a breakwater structure in a harbor and an underwater boulder rising from 40 m up to 27 m depthThis research has been sponsored by the Government of Spain (COMAROB Project, DPI2011-27977-C03-02), the MORPH EU FP7-Project (grant agreement FP7-ICT-2011-7-288704), and the Eurofleets2 EU FP7-Project (grant agreement FP7-INF-2012-312762). The authors are grateful to Lluis Magi, Carles Candela, and Arnau Carrera for helping with the GIRONA 500 operation

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Leap-Frog Path Design for Multi-Robot Cooperative Localization

Cited by 33 publications

References 11 publications

A survey on coverage path planning for robotics

A survey on coverage path planning for robotics

An olfactory-based robot swarm navigation method

Coverage Path Planning with Real-time Replanning and Surface Reconstruction for Inspection of Three-dimensional Underwater Structures using Autonomous Underwater Vehicles

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