Point-to-point trajectory planning for space robots based on jerk constraints

This paper studies the Cartesian point-to-point optimal trajectory planning for space robots oriented to space maintenance operations. Aiming at the problems of poor stability, large base disturbance, and large joint variation in the motion planning of point-to-point maintenance in space, a planning method is proposed to minimize the base disturbance and the total joint angle variation under the jerk constraint on the premise of ensuring the accuracy of the end pose. First, the attitude of the space robot is described by the unit quaternion, and the velocity relationship between the joint angle, the end effector, and the base posture is introduced. Then, the joint trajectories were parameterized by a fifth degree polynomial, and a trajectory planning model with the minimum perturbation of the base and the minimum variation of the joint of the manipulator was established under the condition that the end effector satisfied the pose and the jerk constraint. Finally, a multi-objective optimization algorithm is proposed to deal with the trajectory optimization problem under nonlinear constraints. The simulation results show that the proposed trajectory planning method can optimize the base attitude and joint angle of the space manipulator under the premise of the optimal trajectory and stability of the terminal execution tool, which ensures the stability of the space robot's on-orbit service and reduces the energy consumption.

Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm

This work addresses the multitask-based trajectory-planning problem (MTTP) for space robotics, which is an emerging application of successively executing tasks in assembly of the International Space Station. The MTTP is transformed into a parameter-optimization problem, where piecewise continuous-sine functions are employed to depict the joint trajectories. An improved genetic algorithm (IGA) is developed to optimize the unknown parameters. In the IGA, each chromosome consists of three parts, namely the waypoint sequence, the sequence of the joint configurations, and a special value for the depiction of the joint trajectories. Numerical simulations, including comparisons with two other approaches, are developed to test IGA validity.

Nonholonomic motion planning for minimizing base disturbances of space manipulators based on multi-swarm PSO

Because space manipulators must satisfy the law of conservation of momentum, any motion of a manipulator within a space-manipulator system disturbs the position and attitude of its free-floating base. In this study, the authors have designed a multi-swarm particle swarm optimization (PSO) algorithm to address the motion planning problem and so minimize base disturbances for 6-DOF space manipulators. First, the equation of kinematics for space manipulators in the form of a generalized Jacobian matrix (GJM) is introduced. Second, sinusoidal and polynomial functions are used to parameterize joint motion, and a quaternion representation is used to represent the attitude of the base. Moreover, by transforming the planning problem into an optimization problem, the objective function is analyzed and the proposed algorithm explained in detail. Finally, numerical simulation results are used to verify the validity of the proposed algorithm.

PSO-Lyapunov motion/force control of robot arms with model uncertainties

A method for motion/force control of robot arms with model uncertainties is presented. Tracking control of complex trajectories is guaranteed using a Lyapunov approach with high-precision performance ensured using a particle swarm optimization (PSO) algorithm. Tracking performance and robustness are simulated for a robotic device for limb rehabilitation that is designed to be adapted easily to different subjects by considering model parameter uncertainties. Controller parameters are optimized offline using the PSO algorithm with Lyapunov stability conditions considered as inequality constraints. Using the control scheme, the robot can guide limbs on smooth and non-smooth trajectories, under model uncertainties and measurement noise.

Repetitive Motion Planning of Free-Floating Space Manipulators

Abstract In this paper, a repetitive motion-planning scheme of free-floating space manipulators is presented. Repetitive motion means when one task ends, the end-effector pose, the joint angles and the base pose should reset (return to their initial value), which will facilitate the subsequent tasks. First, due to the lack of DOF, an order of priority to the given tasks is introduced. Second, the joint reset optimization operator, the base attitude reset optimization operator and the end-effector attitude reset optimization operator are designed. Then, the repetitive motion scheme is proposed by combining the three optimization operators above in a creative way. Finally, to make the optimization of repetitive motion obvious, the base attitude maintenance is also considered. Simulation results verify the correctness and the validity of the repetitive motion planning method of space manipulator.

Impedance Controller Tuned by Particle Swarm Optimization for Robotic Arms

This paper presents an efficient and fast method for fine tuning the controller parameters of robot manipulators in constrained motion. The stability of the robotic system is proved using a Lyapunov-based impedance approach whereas the optimal design of the controller parameters are tuned, in offline, by a Particle Swarm Optimization (PSO) algorithm. For designing the PSO method, different index performances are considered in both joint and Cartesian spaces. A 3DOF manipulator constrained to a circular trajectory is finally used to validate the performances of the proposed approach. The simulation results show the stability and the performances of the proposed approach.