Four-wheel, independently driven skid-steer mobile robots have been widely used in some fields, such as indoor product shipping and outdoor inspection and exploration. Traditional skid-steer mobile robot controllers often use a kinematics controller to obtain the desired speed of each wheel, complete speed closed-loop control of each wheel and achieve the robot’s trajectory tracking control. However, the controller based on kinematics may lead to robot chattering and wheel spin from being directly driven by the motor on uneven grounds. To solve these problems, we developed a four-wheel, independently driven skid-steer mobile robot with a damping module for the timing-belt servo system and proposed a model-based coordinated trajectory tracking control method with the timing-belt servo system. First, the kinematics and dynamics of the mobile robot are established, including the chassis kinematics and dynamics, as well as the dynamics of the timing-belt servo system. Secondly, the hierarchical control law is designed, which has adaptive robust control of the upper-level robot chassis, middle-level control allocation approach, and adaptive robust control of the bottom-level timing-belt servo system. Finally, the proposed method is verified by the robot’s trajectory tracking control comparison simulation experiments and has a better control performance.

A stable and robust odometry system is essential for autonomous robot navigation. The 4D millimeter-wave radar, known for its resilience in harsh weather conditions, has attracted considerable attention. As the latest generation of FMCW radar, 4D millimeter-wave radar provides point clouds with both position and Doppler velocity information. However, the increased uncertainty and noise in 4D radar point clouds pose challenges that prevent the direct application of LiDAR-based SLAM algorithms. To address this, we propose a SLAM framework that fuses 4D radar data with gyroscope readings using graph optimization techniques. Initially, Doppler velocity is employed to estimate the radar’s ego velocity, with dynamic points being removed accordingly. Building on this, we introduce a pre-integration factor that combines ego-velocity and gyroscope data. Additionally, leveraging the stable RCS characteristics of radar, we design a corresponding point selection method based on normal direction and propose a scan-to-submap point cloud registration technique weighted by RCS intensity. Finally, we validate the reliability and localization accuracy of our framework using both our own dataset and the NTU dataset. Experimental results show that the proposed DGRO system outperforms traditional 4D radar odometry methods, especially in environments with slow speeds and fewer dynamic objects.

High-speed aircraft actuators are critical for precise control of aerodynamic surfaces, demanding fast response, accuracy, and robustness against uncertainties and disturbances. However, the complex nonlinear dynamics of these systems pose significant challenges for conventional control methods. Sliding mode control (SMC) offers robust performance and rapid transient response but is hindered by chattering, which can degrade performance. To address this, this paper proposes an innovative nonlinear control strategy that integrates global nonsingular terminal sliding mode control (NTSMC) for finite-time convergence with fuzzy logic-based adaptive gain tuning to mitigate chattering and suppress oscillations. A prototype actuator and experimental platform were developed to validate the approach. Experimental results demonstrate superior dynamic response and disturbance rejection compared to traditional methods, highlighting the effectiveness of the proposed control strategy.

A hyper-redundant flexible manipulator is characterized by high degree(s) of freedom (DoF), flexibility, and environmental adaptability. It has been used for missions in complex and unknown spaces, such as debris rescue and pipeline inspection, where the manipulator is not intelligent enough to face complex situations. Therefore, human intervention is required to assist in decision-making and control. In this paper, we designed an interactive navigation method based on mixed reality (MR) of a hyper-redundant flexible manipulator in an unknown space. A novel teleoperation system frame is put forward. An MR-based interface was developed to provide a virtual model of the remote workspace and virtual interactive interface, allowing the operator to observe the real-time situation from a third perspective and issue commands to the manipulator. As for environmental modeling, a simultaneous localization and mapping (SLAM) algorithm based on an RGB-D camera is applied. Additionally, a path-finding and obstacle avoidance method based on artificial potential field (APF) is introduced to ensure that the manipulator can move automatically under the artificial command in the remote space without collision. The results of the simulations and experiments validate that the system exhibits good real-time performance, accuracy, security, and user-friendliness.

Jumping robots are highly capable of overcoming obstacles. However, their explosive force, short duration, and variable trajectories pose significant challenges in achieving stable landings in complex environments. Traditional approaches rely heavily on sophisticated algorithms and electronic sensor feedback systems to ensure landing stability, which increases the implementation complexity. Inspired by the process by which humans complete jumps and achieve stable landings in complex environments, this study proposes a novel landing control method for jumping robots. By designing a mechanically coupled perception-control structure based on mechanical logic computing, the robot simulates the real-time transmission of neural signals triggered by the ground reaction force (GRF) in human reflex loops, thereby simplifying traditional control approaches. Through the collaboration of a flexible mechanical spine and a bistable foot module, the robot achieves an average height of 16.8 cm and a distance of 25.36 cm in consecutive stable jumps. It also demonstrates reliable landing performance on challenging terrain including slopes and cobblestone surfaces. This paper proposes a novel landing control method for jumping robots that simplifies traditional control approaches. The method enables stable landings on complex terrain through a mechanically coupled perception-control structure. This approach has potential applications in tasks requiring mobility over uneven terrain, such as search and rescue.