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

Abstract

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.

Robot automatic path-finding algorithm based on computer vision and neural network model

“…In the process of path finding, the robot needs to judge whether a fork in the road passes repeatedly, so as to choose a different direction from the last one, so the neural network is required to have certain memory characteristics. From the input layer to the output layer, the neurons in each layer are connected with each other; Each layer of neurons uses a relatively simple way to process information; The connection weights between layers reflect the learning ability and memory ability of neural network to external knowledge 10 .…”

A Real-time Look-ahead Trajectory Planning Methodology for Multi Small Line Segments Path

“…The kinematic limitations of machine tools have been fully considered to obtain a time-optimal feed rate curve [17]. Various of acceleration and deceleration trajectory planning algorithms, such as polynomial, trapezoidal, and S-shaped jerk-limited methods [18][19][20] and the jerk-continuous methods [21][22][23], are available, and the S-shaped acceleration and deceleration algorithm is widely used [24][25][26]. After the path-smoothing and trajectory planning methods are selected, the look-ahead function that calculates the velocity at each point should be determined.…”