Capture and removal of space debris is challenging in robotic on-orbit servicing (OOS) activities. A large portion of space debris does not possess any graspable features, which makes conventional grippers inapplicable. To handle such non-graspable objects, a space robotic capture system is presented. A dual-arm space robot simulator that has the advantages of miniaturization and scalability is designed for ground tests. Inspired by robotic caging, we propose a novel capture method that uses a series of hollow-shaped end-effector pairs to cage the antipodal pairs of non-graspable objects. To apply the caging-pair method steadily, space robots need exerting a squeezing action on objects, which can be characterized by the motion and force manipulation of two robotic arms in the assigned directions. Based on the velocity and force manipulability transmission ratios, a caging compatibility index is proposed to describe the capturing ability in this manner. Via the optimization of the desired caging compatibility index, an effective algorithm is proposed to plan near-optimal joint configurations for pre-grasping cages. Finally, both simulation studies and experimental tests are conducted to evaluate the performance of the proposed capture method. Index Terms-Non-graspable objects, space robot simulator, caging-pair method, caging compatibility index. R ! ! q q w J v J J Mv k J T Mv E k k k = t k J

Cryptographic algorithm identification, which refers to analyzing and identifying the encryption algorithm used in cryptographic system, is of great significance to cryptanalysis. In order to improve the accuracy of identification work, this article proposes a new ensemble learning-based model named hybrid k-nearest neighbor and random forest (HKNNRF), and constructs a block cipher algorithm identification scheme. In the ciphertext-only scenario, we use NIST randomness test methods to extract ciphertext features, and carry out binary-classification and five-classification experiments on the block cipher algorithms using proposed scheme. Experiments show that when the ciphertext size and other experimental conditions are the same, compared with the baselines, the HKNNRF model has higher classification accuracy. Specifically, the average binary-classification identification accuracy of HKNNRF is 69.5%, which is 13%, 12.5%, and 10% higher than the single-layer support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) respectively. The five-classification identification accuracy can reach 34%, which is higher than the 21% accuracy of KNN, the 22% accuracy of RF and the 23% accuracy of SVM respectively under the same experimental conditions.

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Normal alkane is an unbranched alkane whose structural formula is H-CH 2 -CH 2 -… -CH 2 -… -CH 2 -H, which can be regarded as a reconfigurable chain-type structure composed of -CH 2 -modules. Inspired by normal alkane, a normal-alkane-like reconfigurable modular robot (NAR) is proposed. The module consists of two differential gear trains mounted orthogonally. Each differential gear train contains two input degrees of freedom and two output degrees of freedom. Due to the genderless interface design, multiple modules can be assembled into chain-type configuration. With the genderless interfaces and flexible degrees of freedom, NAR can be reconfigured into different dimensions of spatial configuration. The bond matrix is used to describe the configuration, which represents the bond attitude of the adjacent connected modules. In addition, full interconnected geometric feature (FIGF) algorithm is proposed for non-isomorphic configuration enumeration and judgment. The configurations with three modules are simulated and the results verify the feasibility of the algorithm. Finally, a prototype with three modules is fabricated and the configuration motion sequence is demonstrated.

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Impact-type penetrators are devices that apply the impact generated by their internal components to penetrate the soil. The penetration effect of the impact-type penetrators is affected by the physical parameters (e.g., mass and stiffness) of their internal constituent elements. Therefore, optimal parameters must be obtained by using a dynamic impact penetrator model to maximize the dive distance of each impact. However, the dynamic impact penetrator models are nonlinear and difficult to describe. Thus, in this paper, this work proposes a segmentation method for modeling the penetrator motion to establish an accurate dynamic model that can be divided into four states. Buffer spring pre-compression, which is introduced as a new influencing parameter to improve the performance of the penetrator, and the genetic algorithm is used for the optimization in accordance with the characteristics of the required optimization parameter set. Parameter stability is then analyzed by considering the actual project application. Then, the control variable method is employed to explore the influence of changing the obtained parameters on the penetration effect. Finally, a processing prototype designed on the basis of the acquired parameters is used for the experimental verification. This work addresses the complexity of the dynamics model of penetration and the difficulty encountered in determining the parameter values.

By introducing the modular and reconfigurable design, the limbs of hexapod robot can be assembled into different configurations to meet various task requirements. Contrary to the fixed configuration system, the task capabilities of the modular reconfigurable robot vary with the configurations. This paper addresses the problem of the configuration enumeration and configuration expression of a modular reconfigurable robot. First, by establishing the permutation group of the regular hexagon (the base shape of the modular reconfigurable robot) and adopting the Pólya enumeration theorem, the theoretical formula of the non-isomorphic configuration enumeration is derived. Then, considering the change in structural features, equivalence relations based on structural features are defined and the structural feature method (SFM) is proposed to enumerate the non-isomorphic configurations. In addition, to express configurations more intuitively, a senary vertex identification array with the same dimension as the number of modules is presented, which can be transformed from the decimal index of the configuration. Simulation analysis demonstrates that the result of the SFM is consistent with that of the theoretical calculation, which also confirms the effectiveness and accuracy of the two methods applied in the non-isomorphic configuration enumeration of the modular reconfigurable robot.

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