Multi-robot exploration and mapping studies have demonstrated that it is often more efficient to explore unknown areas in parallel rather than with a single agent. However, the question of how to integrate individual maps into a consistent global map remains an open research area. This problem, known as map merging, comprises the establishment of a frame of reference for multiple mobile robots, the identification of regions of map overlap, and the integration of individual maps to produce a global result. In this work, we build a hybrid map which integrates occupancy grid and feature data to solve this problem. This integrated representation permits fast and effective map merging. Experimental results are presented that demonstrate algorithm performance in a realistic scenario.
Omnidirectional vehicles have been widely applied in several areas, but most of them are designed for the case of motion on flat, smooth terrain, and are not feasible for outdoor usage. This paper presents the design and development of an omnidirectional mobile robot that possesses high mobility in rough terrain. The omnidirectional robot consists of a main body with four sets of mobility modules, called an active split offset caster (ASOC). The ASOC module has independently driven dual wheels that produce arbitrary planar translational velocity, enabling the robot to achieve its omnidirectional motion. Each module is connected to the main body via a parallel link with shock absorbers, allowing the robot to conform to uneven terrain. In this paper, the design and development of the ASOC-driven omnidirectional mobile robot for rough terrain are described. A control scheme that considers the kinematics of the omnidirectional mobile robot is presented. The mobility of the robot is also evaluated experimentally based on a metric called the ASOC mobility index. The mobility evaluation test clarifies a design tradeoff between terrain adaptability and omnidirectional mobility due to the shock absorbers. In addition, an odometry improvement technique that can reduce position estimation error due to wheel slippage is proposed. Experimental odometry tests confirmed that the proposed technique is able to improve the odometry accuracy for sharp-turning maneuvers. C 2014 Wiley Periodicals, Inc.
A local positioning system (LPS) for tracking mobile robots using a newly developed uItra-wideband (UWB) ranging radio technology is presented in this paper. However, measured ranges from these radios often have uncertain biases and large sporadic errors due to multipath and attenuation effect. A fuzzy neighborhood tracking filtering technique was developed to deal with the range outlier problems. A progressive update trilateration filter technique is introduced. The paper then describes a UWB LPS based on triangulation or trilateration of the ranges, which is fused with other vehicle kinematics/dynamics sensors including a compass, rate gyros and wheel speed sensors.The UWB LPS was applied to navigation and guidance of an experimental autonomous mobile robot. Simulation theoretical and real-time experimental results were validated against the true results of the experiment recorded by video.
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