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

2011 ◽  
Vol 28 (3) ◽  
pp. 373-387 ◽  
Author(s):  
Keiji Nagatani ◽  
Yoshito Okada ◽  
Naoki Tokunaga ◽  
Seiga Kiribayashi ◽  
Kazuya Yoshida ◽  
...  
2008 ◽  
Vol 20 (1) ◽  
pp. 24-37 ◽  
Author(s):  
Chomchana Trevai ◽  
◽  
Norisuke Fujii ◽  
Jun Ota ◽  
Tamio Arai

In this paper, we propose a search and surveillance with mobile robots to collect information while minimizing repeated coverage to maximize efficiency. The problem of search and surveillance is defined as one having a mobile robot or covering a working area with sensor footprints. The problem is applicable to tasks such as floor cleaning, map building, surveillance, security patrols, and search and rescue operations. We use a reaction-diffusion equation on a graph (RDEG), we make and remake plans online base on incoming environmental information. The strategy is applicable to patrolling tasks after an environment has been completely explorated. Tasks are allocated to multiple mobile robots, among which a temporary leader, i.e., the robot detecting a drastic change in the environment, plans a strategy for other mobile robots on the team. Sensing and positioning data for each robot is broadcast and shared among robots. Simulation in different scenarios using one to three robots demonstrated the feasibility of increasing the number of robots on a team.


Robotica ◽  
2019 ◽  
Vol 38 (2) ◽  
pp. 350-373 ◽  
Author(s):  
Hongling Wang ◽  
Chengjin Zhang ◽  
Yong Song ◽  
Bao Pang ◽  
Guangyuan Zhang

SummaryConventional simultaneous localization and mapping (SLAM) has concentrated on two-dimensional (2D) map building. To adapt it to urgent search and rescue (SAR) environments, it is necessary to combine the fast and simple global 2D SLAM and three-dimensional (3D) objects of interest (OOIs) local sub-maps. The main novelty of the present work is a method for 3D OOI reconstruction based on a 2D map, thereby retaining the fast performances of the latter. A theory is established that is adapted to a SAR environment, including the object identification, exploration area coverage (AC), and loop closure detection of revisited spots. Proposed for the first is image optical flow calculation with a 2D/3D fusion method and RGB-D (red, green, blue + depth) transformation based on Joblove–Greenberg mathematics and OpenCV processing. The mathematical theories of optical flow calculation and wavelet transformation are used for the first time to solve the robotic SAR SLAM problem. The present contributions indicate two aspects: (i) mobile robots depend on planar distance estimation to build 2D maps quickly and to provide SAR exploration AC; (ii) 3D OOIs are reconstructed using the proposed innovative methods of RGB-D iterative closest points (RGB-ICPs) and 2D/3D principle of wavelet transformation. Different mobile robots are used to conduct indoor and outdoor SAR SLAM. Both the SLAM and the SAR OOIs detection are implemented by simulations and ground-truth experiments, which provide strong evidence for the proposed 2D/3D reconstruction SAR SLAM approaches adapted to post-disaster environments.


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