In large-scale structured scenes, efficient and reliable global localization is hard to achieve for mobile robots due to the substantial increase in the search space. This article addresses this issue by proposing an optimized branch and bound (BnB) based global localization using multiscale/resolution maps, which can efficiently find the global optimal initial pose with a high success rate. Multiscale feature maps (MSFMs) are first constructed to reflect the explicit point/line information of the structured scene. Then, to narrow the position space, robust features are extracted from LiDAR data at different scales and matched with MSFMs instantaneously using the hash function. Utilizing the attitude and heading reference system, a yaw angle estimation considering environmental interference is achieved to reduce the orientation space and improve the localization reliability in ambiguous environments. Besides, with the optimal resolution level and sparse point cloud sampled from interval filtering, an optimized BnB-based search is presented by using multiresolution maps. Finally, the effectiveness of the proposed method is verified by mobile robot real-world and simulated experiments in large-scale structured environments.

Four-wheel redundantly-actuated mobile robot (FRMR) offers high controllability and maneuverability for automation applications. However, the robot dynamics and actuator failures are normally omitted in the existing kinematic control schemes, which may lead to steering vibration and degraded robustness. To deal with these problems, a fault-tolerant dynamic control method is developed for precise trajectory tracking of the FRMR, which utilizes a two-level structure to cover the wheel-ground interaction and possible actuator failures. In the high level, with a novel fractional-order sliding mode control, this method offers an effective way to regulate the steering angle and eliminate the rotation chattering simultaneously. For the redundantly-actuated issue, a robust allocation solution is then presented in the lower level to straightly determine optimal driving torques with full considerations of actuator failures and optimization efficiency. The convergence and stability of the achieved FRMR system are guaranteed theoretically. The experimental comparative results verify that higher tracking precision and enhanced robustness can be obtained using our proposed method.

That a mobile welding robot with an adaptive seam tracking ability can greatly improve welding efficiency and quality has been extensively studied. To further improve automation in multiple-station welding, we developed a novel intelligent mobile welding robot consisting of a four-wheeled mobile platform and a collaborative manipulator. With the support of simultaneous localization and mapping (SLAM) technology, the robot is capable of automatically navigating to different stations to perform welding operations. To automatically detect the welding seam, a composite sensor system including an RGB-D camera and a laser vision sensor is creatively applied. Based on the sensor system, a multi-layer sensing strategy is performed to ensure that the welding seam can be detected and tracked with high precision. By applying a hybrid filter to the RGB-D camera measurement, the initial welding seam could be effectively extracted. Next, a novel welding start point detection method is proposed. Meanwhile, to guarantee the tracking quality, a robust welding seam tracking algorithm based on laser vision sensor is presented, to eliminate the tracking discrepancy caused by the platform parking error, through which the tracking trajectory can be corrected in real-time. The experimental results show that the robot can autonomously detect and track the welding seam effectively at different welding stations. Also, multiple-station welding efficiency can be improved and quality can also be guaranteed.

The four-wheel omnidirectional mobile robot usually suffers disturbed or unstable lateral motion under harsh terrain conditions (such as uneven or oiled ground). Generally for such a challenging situation, the lumped disturbances and interconnected states render available coupling solutions difficult to achieve demand-satisfied performance. This article proposes a novel decoupled fractional supertwisting sliding mode control (FST-SMC) method by i) constructing an inverse system-based decoupling to form a pseudolinear composition system; ii) presenting an enhanced nominal sliding law for chattering mitigation, and iii) designing an unbiased multilayer fuzzy estimator with gain-learning capacity to compensate for the lumped disturbances actively. Given that the identified disturbances can be directly reflected in the FST-SMC law, this method guarantees an accurate and robust control without causing gain overestimation. Theoretical analysis is offered to verify the asymptotic stability. Under harsh terrain conditions, experimental results validate the effectiveness of the proposed FST-SMC method.