Optimal trajectory planning of free-floating space manipulator using differential evolution algorithm

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

“…This category of robots has simple geometrical structure and contributes to convenient operation when performing tasks and less computational cost when verifying a developed approach. Study and application of this category of robots can also be found in [11][12][13]17,32].…”

“…Next, the trajectory-planning problem is converted into a parameter-optimization problem, with the objective function of minimum maneuver time or maximum manipulability of the robotic system, or minimum attitude disturbance acting on the free-floating base spacecraft during the robotic maneuver. Finally, the unknown coefficients are optimized by the optimization algorithms, including the basic heuristic algorithms such as the particle-swarm optimization algorithm (PSO) [13] and genetic algorithm (GA) [14,15], and the improved optimization algorithms such as hybrid PSO [16] and the constrained differential evolution algorithm (DE) [17]. In the aforementioned algorithms, the PSO is a random search algorithm based on group collaboration, more specifically, simulating the foraging behavior of a flock of birds.…”

Motion planning optimization of trajectory path of space manipulators

“…As described above, in the working process, space manipulators first divide the tasks of manipulators according to the input task target, then plan the movement trajectory of manipulators according to the constraint set and action set and use the relevant optimization algorithm to optimize the motion trajectory of manipulators according to the requirement of the target set. In the process of trajectory optimization, the manipulator model is simplified to some versatility in order to facilitate calculation [8]. As shown in Figure 2, the complex manipulator structure is simplified into a structure with the composition of a rigid body and a joint: B 0 represents the rigid body of the manipulator pedestal, B 1 ∼ B n represent the links of the manipulator, J 1 ∼ J n represent the joints of the manipulator, C 0 represents the centroid of the pedestal, C 1 ∼ C n represent the centroids of the corresponding link, S O represents an inertial coordinate system that assumes absolutely stationary, S e represents the coordinate system of the manipulator pedestal, r 0 represents the position vector of the pedestal in the inertial coordinate system, r 1 ∼ r n represent the position vectors of the centroid of the corresponding link in the inertial coordinate system, p 1 ∼ p n represent the position vectors of the joint centroid in the inertial coordinate system, p e represents the position vectors of the end of the manipulator in the inertial coordinate system, a i represents the vector of J i pointing to C i , i ∈ (1, 2, .…”

The investigation of trajectory control for an anthropomorphic manipulator attached to a vehicle

“…An approximation tool for an industrial robot inverse model based on an adaptive neural model optimized by advanced DE was presented [19]. An optimal joint trajectory planning method was proposed using forward kinematics of 7-DoF free-floating space robot based on DE method [20], depicting the general aspect of equality and inequality constraints which govern each joint in the manipulator. Shuf-"Instrumentation Engineering, Electronics and Telecommunications -2019" Proceedings of the V International Forum (Izhevsk, Russia, November 20-22, 2019) 42 fled frog-leaping algorithm SFLA was introduced which is a population-based collaborative search metaphor inspired by natural memes [21].…”

“…"Instrumentation Engineering, Electronics and Telecommunications -2019" Proceedings of the V International Forum (Izhevsk, Russia, November[20][21][22] 2019) …”