Aimed at capture task for a free-floating space manipulator, a scheme of pre-impact trajectory planning for minimizing base attitude disturbance caused by impact is proposed in this paper. Firstly, base attitude disturbance is established as a function of joint angles, collision direction and relative velocity between robotic hand and the target. Secondly, on the premise of keeping correct capture pose, a novel optimization factor in null space is designed to minimize base attitude disturbance and ensure that the joint angles do not exceed their limits simultaneously. After reaching the balance state, a desired configuration is achieved at the contact point. Thereafter, particle swarm optimization (PSO) algorithm is employed to solve the pre-impact trajectory planning from its initial configuration to the desired configuration to achieve the minimized base attitude disturbance caused by impact and the correct capture pose simultaneously. Finally, the proposed method is applied to a 7-dof free-floating space manipulator and the simulation results verify the effectiveness

Considerable research studies have been reported on developing effective active control means to mitigate combustion instability. In this work, a neural network PID (NN-PID) controller is proposed and demonstrated to suppress the oscillating pressure in a cylindrical Rijke tube, for which the combustion instability is modelled by a 1D acoustic network model together with linear and nonlinear flame models. The flame model is based on the classical n − τ model filtered by a first-order low pass filter. The nonlinearity of the flame model is realised using a gain function saturated with velocity perturbation. A fuel valve and a loudspeaker are adopted as two independent control actuators. Results show that system oscillation begins to attenuate when the NN-PID control starts. The attenuation rate using a fuel valve as the actuator is faster than that using a loudspeaker. For nonlinear combustion oscillations, the NN-PID controller could inhibit the reference pressure oscillation in the linear growth and saturation processes. A maximum overshoot of 2.3% could occur when the loudspeaker is used as the actuator to suppress system oscillation. The NN-PID controller is not affected by noise with a fuel valve or the loudspeaker and can effectively suppress and eliminate the system pressure oscillation in different oscillating stages with different flame models.

On-orbit operation tasks require the space robot to work in an unstructured dynamic environment, where the end-effector’s trajectory and obstacle avoidance need to be guaranteed simultaneously. To ensure the completability and safety of the tasks, this paper proposes a new obstacle-avoidance motion planning method for redundant space robots via reinforcement learning (RL). First, the motion planning framework, which combines RL with the null-space motion for redundant space robots, is proposed according to the decomposition of joint motion. Second, the RL model for null-space obstacle avoidance is constructed, where the RL agent’s state and reward function are defined independent of the specific information of obstacles so that it can adapt to dynamic environmental changes. Finally, a curriculum learning-based training strategy for RL agents is designed to improve sample efficiency, training stability, and obstacle-avoidance performance. The simulation shows that the proposed method realizes reactive obstacle avoidance while maintaining the end-effector’s predetermined trajectory, as well as the adaptability to unstructured dynamic environments and robustness to the space robot’s dynamic parameters.

This article presents an obstacle-avoidance method for manipulator systems working in a cluttered environment. Based on the concept of the artificial potential field, a velocity potential field method is proposed, with the attractive velocity function designed in configuration space and the repulsive velocity function designed in Cartesian space. The attractive and repulsive velocities calculated by the local information together drive the manipulator to reach the target point without colliding with obstacles. To overcome the local minima problem, virtual target points are selected based on the rapidly exploring random trees method, which can consider the global information. The manipulator reaches the virtual target points one by one until the desired target is reached. The proposed method considers both global and local information and can significantly increase the probability of achieving a feasible and continuous trajectory for manipulator systems in cluttered environments. The simulation results verify the effectiveness of the proposed method.

The maximum contact force is one of the most important indicators for contact problems. In this paper, the configuration optimization is conducted to reduce the maximum contact force for a free-floating space robot capturing tumbling target. First, the dynamics model of a free-floating space robot is given, with which the inertial properties perceived at the end-effector can be derived. Combing the inertial properties of the contact bodies, a novel concept of integrated effective mass is proposed. It tries to transform the complex contact process into the energy change of a virtual single body with integrated effective mass. On this basis, a more general continuous contact model is established, which is also suitable for non-central collisions between space robot and the tumbling target. Thereafter, the maximum contact force is derived as an important indicator for the null-space optimization method to reduce the maximum contact force. Finally, numerical simulations with a 3-degree-of-freedom free-floating space robot and a 7-degree-of-freedom free-floating space robot, as the research objects, are carried out respectively and the results show the effectiveness of the method proposed.