Multirobot exploration for search and rescue missions: A report on map building in RoboCupRescue 2009

Nagatani, Keiji; Okada, Yoshito; Tokunaga, Naoki; Kiribayashi, Seiga; Yoshida, Kazuya; Ohno, Kazunori; Takeuchi, Eijiro; Tadokoro, Satoshı; Akiyama, Hidehisa; Noda, Itsuki; Yoshida, Tomoaki; Koyanagi, Eiji

doi:10.1002/rob.20389

Cited by 40 publications

(18 citation statements)

References 27 publications

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“…The team "Pelican United," comprised of students and faculty from Chiba Institute of Technology, the National Institute of Advanced Industrial Science and Technology (AIST), and Tohoku University (including one of the authors), participated in the competition in 2009. Details about the implementation are described in [3]. Figure 2(a) shows a teleoperated robot and (b) an autonomous robot.…”

Section: Implementation Examplementioning

confidence: 99%

Sensor Information Processing in Robot Competitions and Real World Robotic Challenges

2012

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In this paper, we present an overview of technical approaches and algorithms used by university researchers in several recent international robot competitions and challenges, which include RoboCupRescue, Tsukuba Challenge, Defense Advanced Research Projects Agency Urban Challenge, and Multi Autonomous Ground-robotic International Challenge. All robot systems display the latest hardware and software modules needed for accurate localization, navigation, and exploration in difficult environments. In each case, key robotic research problems addressed by each challenge are presented, and a representative implementation of a robot system is provided. Finally, individual results from the fielded robots are presented, and opportunities for future work are discussed.

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Section: Implementation Examplementioning

confidence: 99%

Sensor Information Processing in Robot Competitions and Real World Robotic Challenges

2012

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“…This, however, requires the computational resources of a GPU even when limiting the search space to a discretization of

R^{3}

R^{2}

, respectively. Nagatani et al () use an iterative closest point (ICP) algorithm to match LIDAR‐based 2.5D maps created by multiple robots. Similar to our approach, they match submaps of limited size, restrict the matching to neighboring submaps and use the resulting transformations as constraints for graph optimization.…”

Section: Related Workmentioning

confidence: 99%

Distributed stereo vision‐based 6D localization and mapping for multi‐robot teams

Schuster

Schmid²,

Brand

et al. 2018

Journal of Field Robotics

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Joint simultaneous localization and mapping (SLAM) constitutes the basis for cooperative action in multi-robot teams. We designed a stereo vision-based 6D SLAM system combining local and global methods to benefit from their particular advantages: (1) Decoupled local reference filters on each robot for real-time, longterm stable state estimation required for stabilization, control and fast obstacle avoidance; (2) Online graph optimization with a novel graph topology and intra-as well as inter-robot loop closures through an improved submap matching method to provide global multi-robot pose and map estimates; (3) Distribution of the processing of high-frequency and high-bandwidth measurements enabling the exchange of aggregated and thus compacted map data. As a result, we gain robustness with respect to communication losses between robots. We evaluated our improved map matcher on simulated and real-world datasets and present our full system in five realworld multi-robot experiments in areas of up 3,000 m 2 (bounding box), including visual robot detections and submap matches as loop-closure constraints. Further, we demonstrate its application to autonomous multi-robot exploration in a challenging rough-terrain environment at a Moon-analogue site located on a volcano. K E Y W O R D S graph SLAM, map matching, mobile robots, multi-robot, navigation filter 1 | INTRODUCTION The exploration of moons and foreign planets is an important current and future application for mobile robots as their surfaces are difficult to reach and hard to access for humans. The application of huge and complex robot systems such as Curiosity, landed on Mars in 2012, creates many single points of failure for a mission. As a consequence, these rovers have to move very slowly and carefully to avoid getting stuck, as the Mars rover Spirit did in 2009 (Wolchover, 2011). The future deployment of teams of multiple robots can avoid these single points of failure by gaining robustness through redundancy and, in addition, can improve efficiency through parallelization. The robots have to travel through previously unknown unstructured rough terrain, operating in areas where external methods for localization like global navigation satellite systems (GNSS) are not available or expensive to set up. Communication links to the robots are limited and heavily delayed, featuring for example 8-40 min round trip time between Earth andMars. Furthermore, communication between the robots cannot be guaranteed at all times, in particular at scientifically interesting places such as craters, canyons, or caves. As teleoperation therefore becomes inefficient or infeasible, robot autonomy is a key aspect for future planetary exploration missions. Any coordinated (semi-)autonomous operation in such challenging environments requires up-to-date localization estimates for all robots in a team as well as a joint map to operate on.

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“…Such systems have found many applications in diverse areas of science and engineering such as rescue operations [16], distributed arrays of sensors [17], or networked vehicles [18].…”

Section: Example: Positioning Of Robot Teamsmentioning

confidence: 99%

Distance Bound Smoothing under orientation constraints

Rull

Porta

Thomas

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Distance Bound Smoothing (DBS) is a basic operation originally developed in Computational Chemistry to determine point configurations that are within certain pairwise ranges of distances. This operation consist in the iterative application of filtering processes that reduce the given ranges using triangular and tetrangular inequalities.Standard DBS has a limited range of applications because it does not take into account constraints on the orientations of simplices (triangles or tetrahedra, depending on the dimension of the problem). This paper discuses an extension of DBS that permits incorporating these constraints. This paves the way for the application of DBS techniques to a broad range of problems in Robotics.

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Multirobot exploration for search and rescue missions: A report on map building in RoboCupRescue 2009

Cited by 40 publications

References 27 publications

Sensor Information Processing in Robot Competitions and Real World Robotic Challenges

Sensor Information Processing in Robot Competitions and Real World Robotic Challenges

Distributed stereo vision‐based 6D localization and mapping for multi‐robot teams

Distance Bound Smoothing under orientation constraints

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