Navigation is necessary for autonomous mobile robots that need to track the roads in outdoor environments. These functions could be achieved by fusing data from costly sensors, such as GPS/IMU, lasers and cameras. In this paper, we propose a novel method for road detection and road following without prior knowledge, which is more suitable with small single lane roads. The proposed system consists of a road detection system and road tracking system. A color-based road detector and a texture line detector are designed separately and fused to track the target in the road detection system. The top middle area of the road detection result is regarded as the road-following target and is delivered to the road tracking system for the robot. The road tracking system maps the tracking position in camera coordinates to position in world coordinates, which is used to calculate the control commands by the traditional tracking controllers. The robustness of the system is enhanced with the development of an Unscented Kalman Filter (UKF). The UKF estimates the best road borders from the measurement and presents a smooth road transition between frame to frame, especially in situations such as occlusion or discontinuous roads. The system is tested to achieve a recognition rate of about 98.7% under regular illumination conditions and with minimal road-following error within a variety of environments under various lighting conditions.
A multi-robot system is used to explore an environment and create a map based on market approach. Data fusion is performed using Bayes theorem and then the local maps are updated. A diffusivity concept is defined to describe the robots' extent apart from one another. The immune optimizing strategy is introduced to select goal points since it is a problem of optimized combination. In order to minimize repeated coverage and improve the exploration efficiency, the evaluation function considers the cost, revenue and diffusivity. Simulation examples show that the proposed method is effective for the stated problem and the immune optimizing strategy is more efficient than other strategies.
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