Continuum hyper-redundant manipulators (CHRMs) have been widely applied in aerospace, medical or other fields to complete tasks in narrow and multi-obstacles environments with its unique structural advantages. Due to the redundancy, the inverse kinematics of CHRMs is rather complex and the trajectory planning has always been the focal and challenging point in the research of CHRMs. To tackle this issue, the present study proposes an overall configuration planning method of CHRMs based on the improved artificial potential field (APF) method. The method avoids the complicated inverse kinematics and vastly reduces the computational complexity. First, a virtual guiding pipeline (VGP) with no obstacles inside is constructed based on a known target configuration and the minimum safe distance. The movement of the CHRM is restricted to the pipe by the APF method. Then, we propose a method to construct a guided potential field in the VGP to solve the local minimum problem of the traditional artificial potential field method. Further, to address the issue of contradictory forces in the overall configuration planning, the maximum-work planning strategy is proposed. Finally, simulations are conducted and the results demonstrate the high performance of the method in terms of planning efficiency, environmental adaptability.

This paper proposes a multi-map path planning approach for rescue robots to address the challenges posed by complex obstacle information, high uncertainty, and the difficulties in mine disaster scenarios. Based on multiple possible environmental maps, each with associated subjective probabilities derived from prior knowledge and expert estimations, a mathematical model for multi-map path planning in mine disaster rescue scenarios is developed. An improved hybrid algorithm combining ant colony optimization (ACO) and genetic algorithm (GA) is then proposed to solve the established model. In the hybrid approach, the improved ACO is employed to overcome the limitations of traditional genetic algorithms, such as poor initial population quality, slow convergence, and suboptimal results. Additionally, a grid-based, rectangular-area, obstacle avoidance strategy is incorporated to precisely evaluate the obstacle avoidance path of each individual across different obstacle maps. Finally, the feasibility and effectiveness of the proposed hybrid algorithm are validated through simulations involving both single and multiple mine disaster maps. The results demonstrate the potential of the proposed approach for solving robot path optimization problems in complex multi-environment scenarios.

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.

Point clouds with rich local geometric information have potentially huge implications in several applications, especially in areas of robotic manipulation and autonomous driving. However, most point cloud processing methods cannot extract enough geometric features from a raw point cloud, which restricts the performance of their downstream tasks such as point cloud classification, shape retrieval and part segmentation. In this paper, the authors propose a new method where a convolution based on geometric primitives is adopted to accurately represent the elusive shape in the form of a point cloud to fully extract hidden geometric features. The key idea of the proposed approach is building a brand-new convolution net named ResSANet on the basis of geometric primitives to learn hierarchical geometry information. Two different modules are devised in our network, Res-SA and Res­SA­2, to achieve feature fusion at different levels in ResSANet. This work achieves classification accuracy up to 93.2% on the ModelNet40 dataset and the shape retrieval with an effect of 87.4%. The part segmentation experiment also achieves an accuracy of 83.3% (class mIoU) and 85.3% (instance mIoU) on ShapeNet dataset. It is worth mentioning that the number of parameters in this work is just 1.04 M while the network depth is minimal. Experimental results and comparisons with state-of-the-art methods demonstrate that our approach can achieve superior performance.

Three-fingered hands can offer more contact points and flexible fingertip configurations, enabling complex grasping modes and finer manipulations for objects of various shapes and sizes. However, existing research on visual-tactile integrated robotic grasping primarily focuses on grippers, and lacks a general dataset merging visual and tactile information for the entire grasping process. In this letter, we introduce the VTG dataset, which can be used for various aspects of three-fingered robotic grasping control. The VTG dataset includes three-view point clouds of objects, grasping modes, and finger angles of three-fingered hands, as well as tactile data at multiple spatial contact locations during the grasping process. By integrating visual and tactile information, we develop a robotic grasping controller that leverages a grasping stability prediction module and a grasping adjustment module. By representing tactile data as a static graphical structure based on the spatial distribution of tactile sensors, the grasping stability prediction module is constructed based on a multi-scale graph neural network, MS-GCN. It combines multi-scale graph topological features with various grasping modes, and achieving an accuracy of 98.4% in robotic grasping stability prediction. Additionally, this controller successfully adapts to unknown objects of varying hardness and shapes, providing stable grasping within approximately 0.4s after contact.

The traditional A* algorithm performs well in single-map environments, but it is prone to path redundancy and obstacle handling delays in complex multi-map collaborative scenarios, making it unsuitable for the characteristics of multi-environment maps. To address these challenges of traditional A* algorithms, this paper proposes a multi-environment map rescue robot path planning method based on an improved A* algorithm. This method introduces an expected cost evaluation function to achieve weighted fusion of path costs and heuristic values from multiple maps, allowing the algorithm to integrate obstacle distributions and weight information across different environments. A random obstacle replacement mechanism is further designed to maintain path feasibility by locally substituting blocked nodes with adjacent accessible nodes, thereby ensuring continuity without global replanning. Through the combination of multi-map information fusion and local obstacle handling, the algorithm generates a globally optimized path that balances planning efficiency, robustness, and adaptability in uncertain rescue scenarios. Experiment results for a 50 × 50 map scenario show that the improved algorithm significantly outperforms single-map planning results in terms of path redundancy, total length, and turning characteristics. The expansion experiments demonstrate that the paths planned by the proposed algorithm are highly consistent with the optimal paths in terms of direction and local deviations, verifying its good feasibility and effectiveness.