OpenStreetMap (OSM) is widely used in outdoor navigation research recently, which is publicly available and can provide a wide range of road information for outdoor robot navigation. In this article, aiming at the problem that the map error of OSM will cause the global path to be inconsistent with the real environment, we propose an OSM-based robot navigation method that combines road network information and local perception information. As a global map, OSM provides road network information to obtain the global path by the Dijkstra algorithm. Multisensor (including 3D-LiDAR and Charge-coupled Device (CCD) camera) information fusion offers local information to detect local road information and obstacles for local path planning. We filter local road information and then extract useful road features to optimize the local path. Finally, this local path is used for robot path tracking to complete navigation tasks. The experimental results show that the average error between the trajectory of the robot and the road center is 0.18 m. This reveals that our method has high navigation accuracy and strong robustness in the real complex environment.

In complex real-world scenarios, wheel-legged robots with maneuverability, stability, and reliability have addressed growing research attention, especially in material transportation, emergency rescue, as well as the exploration of unknown environments. How to achieve stable high-level movement with payload delivery simultaneously is the main challenge for the wheel-legged robot. In this article, a novel hierarchical framework for the flexible motion of the six wheel-legged robot is considered in experimental results. First, for the wheeled motion, the speed consensus algorithm is implemented to the six-wheeled cooperative control; for the legged motion, three gait sequences, and foot-end trajectory based on the Bezier function are designed. Furthermore, a whole-body control architecture includes the attitude controller, impedance controller, and center height controller is developed for obstacle avoidance, which can ensure the horizontal stability of the body of the robot when it passes through obstacles in different terrain. Finally, extensive experimental demonstrations using the six wheel-legged robot (BIT-6NAZA) are dedicated to the effectiveness and robustness of the developed framework, indicating that it is a superior case of a selectable flexible motion with satisfactory stable performance under the field world environment.

Lane detection and tracking is the basic component of many intelligent vehicle systems. In this study, a robust multilane detection and tracking method is proposed. Using the measurements provided by an in‐vehicle mono‐camera and a forward‐looking LIDAR, this algorithm can address challenging scenarios in real urban driving situations. The proposed approach makes use of steerable filters for lane feature detection, LIDAR‐based image drivable space segmentation for lane marking points validations and the RANdom SAmple Consensus technique for robust lane model fitting. To improve the robustness of the fitting further, the parallel lanes hypothesis is introduced. The detected lanes initialise particle filters for tracking, without knowing the ego‐motion information. The image processing procedures are carried out in inverse perspective mapping image, because of its convenience for multilane detection. Experimental results indicate that the algorithm in this study has robustness against various driving situations.

In order to realise autonomous navigation of unmanned platforms in urban or off‐road environments, it is crucial to study accurate, versatile and real‐time road detection methods. This study proposes an adaptive road detection method that combines lane lines and obstacle boundaries, which can be applied to a variety of driving environments. Combining multi‐channel threshold processing, it is robust to lane feature detection under various complex situations. Obstacle information extracted from the grid image constructed by 3D LIDAR point cloud is used for lane feature selection to avoid interference from pedestrians and vehicles. The proposed method makes use of adaptive sliding window for feature selection, and piecewise least squares method for road line fitting. Experimental results on dataset and in real‐world environments show that the proposed method can overcome illumination changes, shadow occlusion, pedestrian, vehicle interference and so on in a variety of scenes. The proposed method has good enough efficiency, robustness and real‐time performance.

Designers specify design requirements and determine the interconnections between the design requirements and main components of the system architecture during the conceptual design phase. To define the architecture of a mechatronic system, two resear