In the future of automated production processes, the manipulator must be more efficient to complete certain tasks. Compared to single-arm robots, dual-arm robots have a larger workspace and stronger load capacity. Coordinated motion planning of multi-arm robots is a problem that must be solved in the process of robot development. This paper proposes an obstacle avoidance path planning method for the dual-arm robot based on the goal probability bias and cost function in a rapidly-exploring random tree algorithm (GA_RRT). The random tree grows to the goal point with a certain probability. At the same time, the cost function is calculated when the random state is generated. The point with the lowest cost is selected as the child node. This reduces the randomness and blindness of the RRT algorithm in the expansion process. The detection algorithm of the bounding sphere is used in the process of collision detection of two arms. The main arm conducts obstacle avoidance path planning for static obstacles. The slave arm not only considers static obstacles, but also takes on the role of the main arm at each moment as a dynamic obstacle for path planning. Finally, MATLAB is used for algorithm simulation, which proves the effectiveness of the algorithm for obstacle avoidance path planning problems for the dual-arm robot.

For a grasping task planner, the pre-planning of the reachable tip pose in a manipulator’s workspace is important. On this basis, for a seven-degree-of-freedom (7-DOF) redundant manipulator, it is very meaningful to study how to give full play to its redundant characteristics to achieve more dexterous grasping. In this paper, through the improved shape primitive method, the reachability spheres of the 7-DOF manipulator studied were more accurately captured, and a more accurate reachability capability map was generated. Then, based on the idea of redundancy angle discretization, the concept of the obstacle avoidance ability (OAA) index is innovatively proposed to measure the dexterity of the grasping task of the manipulator in a certain tip pose. Based on the OAA index axis, the distribution of the OAA index in each reachability sphere was analyzed. The prediction models of the OAA index of all reachability spheres in the workspace of the manipulator were obtained by the Levenberg–Marquardt algorithm and finally formed a dexterity capability map of the manipulator, which provides a new idea for the pre-planning of the dexterous grasping task of the redundant manipulator. Finally, we give the general frameworks of different grasping pre-planning by combining two kinds of capability maps and verify the effect.

The kinematics and dynamics of n(3RRlS) reconfigurable series–parallel manipulators (RSPMs) are the theoretical foundations for capturing noncooperative targets in space. In this study, a unified kinematic and dynamic modeling method of n(3RRlS) RSPMs is proposed based on geometric constraints, the vector method, recursive and inductive methods, and the principle of recursive virtual power, and the kinematic and dynamic modeling progress has clear concepts, unified format, and fast calculation. Then, the theoretical and simulated results of the 3RRlS reconfigurable series–parallel manipulator (RPM) and 3(3RRlS) RSPM are compared verifying the correctness of the derived kinematic and dynamic models. Finally, the reconfiguration, position accuracy, and deformation measurement experiments of the two-driven-unit 3RRlS RPM and 3(3RRlS) RSPM are carried out, and the results show that the 3(3RRlS) RSPM has the advantages of high folding ratio, motion uncoupling, and stiffness improvement.