The Ant Colony Optimization (ACO) is easy to fall into the local optimum and its convergence speed is slow in solving the Travelling Salesman Problem (TSP). Therefore, a Slime Mold-Ant Colony Fusion Algorithm (SMACFA) is proposed in this paper. Firstly, an optimized path is obtained by Slime Mold Algorithm (SMA) for TSP; Then, the high-quality pipelines are selected from the path which is obtained by SMA, and the two ends of the pipelines are as fixed-point pairs; Finally, the fixed-point pairs are directly applied to the ACO by the principle of fixed selection. Hence, the SMACFA with fixed selection of high-quality pipelines is obtained. Through the test of the chn31 in Traveling Salesman Problem Library (TSPLIB), the result of path length was 15381 by SMACFA, and it was improved by 1.42% than ACO. The convergence speed and algorithm time complexity were reduced by 73.55 and 80.25% respectively. What’s more, under the ten data sets of TSPLIB, SMACFA outperformed other algorithms in terms of the path length, convergence speed and algorithm time complexity by comparison experiments. It is fully verified that the performances of SMACFA is superior to others in solving TSP.

Dynamic movement primitives (DMPs) are a robust framework for movement generation from demonstrations. This framework can be extended by adding a perturbing term to achieve obstacle avoidance without sacrificing stability. The additional term is usually constructed based on potential functions. Although different potentials are adopted to improve the performance of obstacle avoidance, the profiles of potentials are rarely incorporated into reinforcement learning (RL) framework. In this contribution, we present a RL based method to learn not only the profiles of potentials but also the shape parameters of a motion. The algorithm employed is PI2 (Policy Improvement with Path Integrals), a model-free, sampling-based learning method. By using the PI2, the profiles of potentials and the parameters of the DMPs are learned simultaneously; therefore, we can optimize obstacle avoidance while completing specified tasks. We validate the presented method in simulations and with a redundant robot arm in experiments.

In order to solve the problem of poor local optimization of the Slime Mold Algorithm (SMA) in the Travelling Salesman Problem (TSP), a Two-way Parallel Slime Mold Algorithm by Flow and Distance (TPSMA) is proposed in this paper. Firstly, the flow between each path point is calculated by the “critical pipeline and critical culture” model of SMA; then, according to the two indexes of flow and distance, the set of path points to be selected is obtained; finally, the optimization principle with a flow index is improved with two indexes of flow and distance and added random strategy. Hence, a two-way parallel optimization method is realized and the local optimal problem is solved effectively. Through the simulation of Traveling Salesman Problem Library (TSPLIB) on ulysses16, city31, eil51, gr96, and bier127, the results of TPSMA were improved by 24.56, 36.10, 41.88, 49.83, and 52.93%, respectively, compared to SMA. Furthermore, the number of path points is more and the optimization ability of TPSMA is better. At the same time, TPSMA is closer to the current optimal result than other algorithms by multiple sets of tests, and its time complexity is obviously better than others. Therefore, the superiority of TPSMA is adequately proven.

Aiming at on-orbit automatic assembly, an improved uncalibrated visual servo strategy for hyper-redundant manipulators based on projective homography is proposed. This strategy uses an improved homography-based task function with lower dimensions while maintaining its robustness to image defects and noise. This not only improves the real-time performance but also makes the joint space of hyper-redundant manipulator redundant to the homography-based task function. Considering that the kinematic parameters of the manipulator are easily changed in a space environment, the total Jacobian between the task function and manipulator joints is estimated online and used to construct a controller to directly control the manipulator joints without a kinematics model. When designing the controller, the above-mentioned redundancy is exploited to solve the problem of the over-limiting of the joint angles of the manipulator. The KF-SVSF method, which combines the optimality of the Kalman filter (KF) and the robustness of the smooth variable structure filter (SVSF), is introduced to the field of uncalibrated visual servos for the first time to perform the online estimation of the total Jacobian. In addition, the singular value filtering (SVF) method is adopted for the first time to avoid the interference caused by the unstable condition number of the estimated total Jacobian. Finally, simulations and experiments verify the superiority of this strategy in terms of its real-time performance and precision.

To meet the special requirements of the third mirror adjustment system for an optical telescope, a 6-P-RR-R-RR parallel platform using offset RR-joints is designed with high precision, a large load-to-size ratio and high stiffness. In order to improve the adjustment accuracy and the stiffness of the whole mechanism, each rotating joint in the subchain is designed as a zero-gap bead shaft system. When compared with a traditional Hooke joint, the offset RR joint has certain characteristics, including large carrying capacity and easy processing and adjustment, that effectively reduce the risk of interference with the joint during rotation and increase the working space of the entire machine. Because of the additional variables introduced by the offset joints, the kinematics problem becomes much more complicated. Regarding the P-RRRRR series subchain, the kinematics model is established using the Denavit–Hartenberg parameter method and then solved by the numerical iteration method. The stiffness of the parallel platform is analyzed and tested, including static and fundamental frequency. Motion performance testing of the parallel platform is performed.