To improve the soft-landing crash performance of the movable lander (ML), this study presents an investigation of a newly designed gradual energy-absorbing structure subjected to impact loads using an ML for theoretical calculation and numerical simulations. In this work, we present a novel computational approach to optimizing the energy absorption (EA) of the ML. Our framework takes as inputting the geometrical parameter (GP) as well as EA. The finite element model of the HB1, HB2, and HB3 was established and effectively verified using numerical simulation and experimental data. The relationship between the GP of the buffer material and the EA was obtained through static experiment and impact experiment, and the cushioning performance of the lander was optimized according to the ML load mass, contact speed, and EA function. According to the optimization results, we chose an outer diameter of 240 mm, an inner diameter of 50 mm, heights of HB1 = 140 mm, HB2 = 110 mm, and HB3 = 225 as the collocation, and completed the numerical simulation of three different cases. By comparing the results of theoretical calculation and numerical simulation experiments, it can be found that the overload response rates of the main body in 4 type landing, 2-2 type landing, and 1-2-1 type landing are 4.72 G, 2.61 G, and 2.33 G, respectively. It also laid the foundation for the theoretical and methodological research of the ML and manned lander in the future.

Summary: Determining an appropriate grasp configuration for multi-finger grasping is difficult due to the complexity of robotic hands. The multi-finger grasp planning should consider not only geometry constraints of objects but also kinematics and dynamics of robotic hand. In this paper, a precision grasp-planning framework is presented for multi-finger hand to grasp unknown objects. First, the manipulation capabilities of the robotic hand are analyzed. The analysis results are further used as bases for the precision grasp planning. Second, the superquadric (SQ) fitting method is used for approximating unknown object models. Finally, a local–global optimization method is implemented to find appropriate grasp configurations for dexterous hand. The presented planning framework is validated in simulation experiments. Simulation results demonstrated that the presented grasp-planning framework enables the multi-finger hand to grasp unknown objects effectively.

For the formation and obstacle avoidance challenges of UAVs (unmanned aerial vehicles) in complex scenarios, this paper proposes an improved collaborative strategy based on APF (artificial potential field). This strategy combines graph theory, the Leader–Follower method, and APF. Firstly, we used graph theory to design formation topology and dynamically adjust the distances between UAVs in real time. Secondly, we introduced APF to avoid obstacles in complicated environments. This algorithm innovatively integrates the Leader–Follower formation method. The design of this attractive field is replaced by the leader’s attraction to the followers, overcoming the problem of unreachable targets in APF. Meanwhile, the introduced Leader–Follower mode reduces information exchange within the swarm, realizing a more efficient “few controlling many” paradigm. Afterwards, we incorporated rotational force to assist the swarm in breaking free from local minima. Ultimately, the stability of the integrated formation strategy was demonstrated using Lyapunov functions. The feasibility and effectiveness of the proposed strategy were validated across multiple platforms.

Space non-cooperative target recognition is crucial for on-orbit servicing. Multi-satellite cooperation has great potential for broadening the observation scope and enhancing identification efficiency. However, there is currently a lack of research on recognition methods tailored for multi-satellite cooperative observation. In this paper, we propose a novel space non-cooperative target recognition method to identify satellites and debris in images from multi-satellite observations. Firstly, we design an image-stitching algorithm to generate space-wide-area images. Secondly, we propose a two-stage multi-target detection model, a lighter CNN model with distance merge threshold (LCNN-DMT). Specifically, in the first stage, we propose a novel foreground extraction model based on a minimum bounding rectangle with the threshold for distance merging (MBRT-D) to address redundant detection box extraction for satellite components. Then, in the second stage, we propose an improved SqueezeNet model by introducing separable convolution and attention mechanisms for target classification. Moreover, due to the absence of a public multi-target detection dataset containing satellites and debris, we construct two space datasets by introducing a randomized data augmentation strategy. Further experiments demonstrate that our method can achieve high-precision image stitching and superior recognition performance. Our LCNN-DMT model outperforms mainstream algorithms in target localization accuracy with only 0.928 M parameters and 0.464 GFLOPs, making it ideal for on-orbit deployment.

Purpose: This paper aims to improve the diversity and richness of haptic perception by recognizing multi-modal haptic images. Design/methodology/approach: First, the multi-modal haptic data collected by BioTac sensors from different objects are pre-processed, and then combined into haptic images. Second, a multi-class and multi-label deep learning model is designed, which can simultaneously learn four haptic features (hardness, thermal conductivity, roughness and texture) from the haptic images, and recognize objects based on these features. The haptic images with different dimensions and modalities are provided for testing the recognition performance of this model. Findings: The results imply that multi-modal data fusion has a better performance than single-modal data on tactile understanding, and the haptic images with larger dimension are conducive to more accurate haptic measurement. Practical implications: The proposed method has important potential application in unknown environment perception, dexterous grasping manipulation and other intelligent robotics domains. Originality/value: This paper proposes a new deep learning model for extracting multiple haptic features and recognizing objects from multi-modal haptic images.