Accurate target recognition and localization remain significant challenges for robotic fruit harvesting in unstructured orchard environments characterized by branch occlusion and leaf clutter. To address the difficulty in identifying and locating apples under such visually complex conditions, this paper proposes an improved YOLOv5-based visual recognition algorithm incorporating an efficient channel attention (ECA) module. The ECA module is strategically integrated into specific C3 layers (C3-3, C3-6, C3-9) of the YOLOv5 network architecture to enhance feature representation for occluded targets. During operation, the system simultaneously acquires apple pose information and achieves precise spatial localization through coordinate transformation matrices. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed system. The custom-designed six-degree-of-freedom (6-DOF) robotic arm exhibits a wide operational range with a maximum working angle of 120°. The ECA-enhanced YOLOv5 model achieves a confidence level of 90% and an impressive in-range apple recognition rate of 98%, representing a 2.5% improvement in the mean Average Precision (mAP) compared to the baseline YOLOv5s algorithm. The end-effector positioning error is consistently controlled within 1.5 mm. The motion planning success rate reaches 92%, with the picking completed within 23 s per apple. This work provides a novel and effective vision recognition solution for future development of harvesting robots.

To improve wheel trafficability in soft and muddy soils such as paddy fields, a bionic walking wheel is designed based on the structural morphology and movement mode of the feet of waders living in marshes and mudflats, similar to the muddy soil of paddy fields. The bionic walking wheel adopts the arrangement of double-row wheel legs and staggered arrays to imitate the walking posture of waders. The two legs move alternately, cooperate with each other, and improve the smoothness of movement. The cam inside the bionic walking wheel is used to control the movement mode of the feet. The flippers open before touching the ground to increase the contact area and reduce sinking, and the toes bend and grip the ground while touching the ground to increase traction. Multi-rigid-body dynamics software (Adams View 2020) is used to simulate the movement of the wheel during the wading process, and the movement coordination and interference between the wheel legs are analyzed. The simulation results show that there is no interference between the parts and that the movement smoothness is good. The interaction between the bionic walking wheel and muddy soil was analyzed via coupled EDEM–ADAMS simulation, and the simulation analysis and experiments were conducted and compared with those for a common paddy wheel. The results showed that the bionic walking wheel designed in this paper improved the drawbar pull by 113.56% compared with that of a common paddy wheel and had better anti-sinking performance. By analyzing the effect of toe grip on traction, it was found that the soil under the feet can be disturbed to provide greater traction when the toe is bent downward. This study provides a reference for improving the trafficability of walking mechanisms in soft and muddy soils, such as paddy fields.

To enable mobile robots to effectively deal with the emergency of visual contamination, contingency planning based on case-based reasoning (CBR) was performed in this paper. First, for a wheeled mobile robot (WMR) equipped with a chameleon-inspired visual system, a target search model in chameleon-inspired binocular negative correlation movement (CIBNCM) mode was established. Second, a CBR-based contingency planning model of visual contamination for WMRs was established, where the reasoning process using CBR for visual contamination was analyzed in detail. Third, through the analysis of environment perception when visual contamination occurs, a perception model in chameleon-inspired visual contamination for WMRs was built. Finally, to validate the proposed approach, a contingency planning experiment scheme for visual contamination was designed based on the robot’s general planning of target tracking, and the experimental result is discussed. The proposed CBR-based contingency planning approach for visual contamination can reason out effective solutions corresponding to the contamination situations. The rationality of the approach was verified by experiments with satisfactory results. Moreover, compared with the contingency planning method based on rule-based reasoning, the accuracy of target retracking after the robot visual system is contaminated is significantly higher for the CBR-based contingent planning method used in this paper.

Stable mobility of rail-climbing robots in complex aerial worksites remains a significant challenge for industrial inspection and maintenance. Existing designs often struggle with reliable adhesion, efficient locomotion, and adaptability to omnidirectional movement on rails with varying curvature and orientation. This study proposes OmniClimb, a novel omnidirectional rail-climbing robot integrating a gravity-actuated locking mechanism and a variable-posture passive adaptation mechanism. The front locomotion unit employs gravity-actuated locking mechanism to ensure consistent contact stress and friction while self-adjusting to rail inclination. A variable-posture passive adaptation mechanism dynamically adjusts the pitch between front and rear frames, enhancing the gravity-actuated locking effect on the rear unit during curved-rail climbing. A multistage compound transmission system combined with an five-wheel-drive configuration improve power transmission efficiency and disturbance rejection in complex rail environments. Dynamic models were established, and structural parameters were optimized using ADAMS multi-body dynamics simulations. Experiments on a 3D-printed physical prototype demonstrated stable mobility and locking capability on multi-curvature rails under loaded conditions, achieving a maximum speed of 0.053 m/s with controlled motor torque fluctuation (mean 1.25 N·m). OmniClimb’s validated adaptability, stability, and efficient climbing performance provide a promising novel solution for deploying rail robots in complex scenarios such as transmission towers and utility tunnels.

To improve the environment perception ability of wheeled mobile robots (WMRs), the visual behavior mechanism of the negative-correlation motion of chameleons is introduced into the binocular vision system of WMRs, and a shifty-behavior-based environment perception model with chameleon-inspired active vision for WMRs is established, where vision–motor coordination is achieved. First, a target search sub-model with chameleon-inspired binocular negative-correlation motion is built. The relationship between the rotation angles of two cameras and the neck and the camera’s field of view (FOV), overlapping angle, region of interest, etc., is analyzed to highlight the binocular negative-correlation motion compared with binocular synchronous motion. The search efficiency of the negative-correlation motion is doubled compared with binocular synchronous motion, and the search range is also greatly improved. Second, the FOV model of chameleon-inspired vision perception based on a shifty-behavior mode is set up. According to the different functional requirements of target searching and tracking stages, the shift of the robot visual behavior is analyzed from two aspects, measuring range and accuracy. Finally, a chameleon-inspired active-vision-based environment perception strategy for mobile robots is constructed based on the shifty-behavior mode, and experimental verification is deployed, which achieves the reproduction of the visual behavior of chameleons in the vision system of mobile robots with satisfactory results.