This paper provides a legged stable walking control strategy based on multi-sensor information feedback about BIT-NAZA-II, a large load parallel hexapod wheel-legged robot developing for the problem of vertical contact impact and horizontal sliding of heavy leg robot in complex terrain environments. The BIT-NAZA-II robot has six legs and six wheels, and the wheels are installed on the foot-end. The wheels of each foot-end for the legs of the robot are locked when walking with legs. In order to realize the smooth transition between swing phase and stance phase, the leg motion is divided into different stages for control by state machine switching controller based on event (SMSCE). In the Z-direction, in order to avoid the shaking of the body caused by the contact impact at the moment of contact between the foot-end and the ground during the walking of the robot, an active compliance controller (ACC) based on impedance control (IC) is applied to solve the problem of contact impact. Moreover, in the X-direction, the swing leg retraction (SLR) based on Bezier curve (BC) is introduced to generate the foot-end trajectory of the robot, which solves the slip problem of the heavy leg robot and improves the horizontal stability. Finally, the control strategy of stable walking is respectively verified by the simulations and experiments. The results show that the ACC based on IC can effectively reduce the contact impact between the foot-end and the ground in the Z-direction and improve the stability of body. Besides, the anti-sliding ability is realized after introducing SLR based on BC in the X-direction, and we also verify that stable walking control strategy is effective, which provides a reference value for the stable walking of heavy leg robot in complex terrain.

Control strategy of stable walking for a hexapod wheel-legged robot.

Accurate path tracking and stability are the main challenges of lateral motion control in mobile robots, especially under the situation with complex road conditions. The interaction force between robots and the external environment may cause interference, which should be considered to guarantee its path tracking performance in dynamic and uncertain environments. In this article, a flexible lateral control scheme is considered for the developed wheel-legged robot, which consists of a cubature Kalman algorithm to evaluate the centroid slip angle and the yaw rate. Furthermore, a fuzzy compensation and preview angle-enhanced sliding model controller to improve the tracking accuracy and robustness. Finally, some simulations and experimental demonstrations using the four-wheel-legged robot (BIT-NAZA) are carried out to illustrate the effectiveness and robustness, and the proposed method has achieved satisfactory results in high-precision trajectory tracking and stability control of the mobile robot. 

Fuzzy-Torque Approximation-Enhanced Sliding Mode Control for Lateral Stability of Mobile Robot

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. 

OpenStreetMap-Based Autonomous Navigation for the Four Wheel-Legged Robot Via 3D-Lidar and CCD Camera

Flexible gait transition for six wheel-legged robot with unstructured terrains

Neural networks‐based sliding mode tracking control for the four wheel‐legged robot under uncertain interaction

Accurate path tracking and stability are the main challenges of lateral motion control in mobile robots, especially under the situation with complex road conditions. The interaction force between robots and the external environment may cause interference, which should be considered to guarantee its path tracking performance in dynamic and uncertain environments. In this article, a flexible lateral control scheme is considered for the developed wheel-legged robot, which consists of a cubature Kalman algorithm to evaluate the centroid slip angle and the yaw rate. Furthermore, a fuzzy compensation and preview angle-enhanced sliding model controller to improve the tracking accuracy and robustness. Finally, some simulations and experimental demonstrations using the four-wheel-legged robot (BIT-NAZA) are carried out to illustrate the effectiveness and robustness, and the proposed method has achieved satisfactory results in high-precision trajectory tracking and stability control of the mobile robot.

Neural fuzzy approximation enhanced autonomous tracking control of the wheel-legged robot under uncertain physical interaction

This paper aims on the trajectory tracking of the developed six wheel-legged robot with heavy load conditions under uncertain physical interaction. The accuracy of trajectory tracking and stable operation with heavy load are the main challenges of parallel mechanism for wheel-legged robots, especially in complex road conditions. To guarantee the tracking performance in an uncertain environment, the disturbances, including the internal friction, external environment interaction, should be considered in the practical robot system.,In this paper, a fuzzy approximation-based model predictive tracking scheme (FMPC) for reliable tracking control is developed to the six wheel-legged robot, in which the fuzzy logic approximation is applied to estimate the uncertain physical interaction and external dynamics of the robot system. Meanwhile, the advanced parallel mechanism of the electric six wheel-legged robot (BIT-NAZA) is presented.,Co-simulation and comparative experimental results using the BIT-NAZA robot derived from the developed hybrid control scheme indicate that the methodology can achieve satisfactory tracking performance in terms of accuracy and stability.,This research can provide theoretical and engineering guidance for lateral stability of intelligent robots under unknown disturbances and uncertain nonlinearities and facilitate the control performance of the mobile robots in a practical system.

Parallel structure of six wheel-legged robot trajectory tracking control with heavy payload under uncertain physical interaction

The invention relates to a load stimulation device of a servo system and a performance testing device of the servo system and belongs to the field of detection technology and automation technology. The devices integrate three modes of inertia loading, servo motor loading and spring loading, can realize independent loading of three loading modes, two combination loading or simultaneous loading, and can stimulate various loading conditions, such as inertia load, elastic loading, unbalanced load, impact load, time change load and the combination of various loads. Acquired signals of a tested servo system, a torsion rotational speed sensor and an angle sensor are sent to a computer for processing so as to realize the static and dynamic performance test under different loading conditions.

Complex load behavior simulation and performance test apparatus for servo system

When it comes to the high accuracy autonomous motion of the mobile robot, it is challenging to effectively control the robot to follow the desired trajectory and transport the payload simultaneously, especially for the cloud robot system. In this paper, a flexible trajectory tracking control scheme is developed via iterative learning control to manage a distributed cloud robot (BIT-6NAZA) under the payload delivery scenarios.,Considering the relationship of six-wheeled independent steering in the BIT-6NAZA robot, an iterative learning controller is implemented for reliable trajectory tracking with the payload transportation. Meanwhile, the stability analysis of the system ensures the effective convergence of the algorithm.,Finally, to evaluate the developed method, some demonstrations, including the different motion models and tracking control, are presented both in simulation and experiment. It can achieve flexible tracking performance of the designed composite algorithm.,This paper provides a feasible method for the trajectory tracking control in the cloud robot system and simultaneously promotes the robot application in practical engineering.

Junzheng Wang

Papers

Fuzzy-Torque Approximation-Enhanced Sliding Mode Control for Lateral Stability of Mobile Robot

Neural fuzzy approximation enhanced autonomous tracking control of the wheel-legged robot under uncertain physical interaction

Parallel structure of six wheel-legged robot trajectory tracking control with heavy payload under uncertain physical interaction

Complex load behavior simulation and performance test apparatus for servo system

Iterative learning control for a distributed cloud robot with payload delivery