Abstract
To solve the problem that the time-consuming optimization process of Genetic Algorithm (GA) can erode the expected time-saving brought by the algorithm, time-optimal trajectory planning based on cubic spline was used, after the modification to classical fitness sharing function of NGA, a dual-threaded method utilizing elite strategy characteristic was designed which was based on Niche Genetic Algorithm (NGA) with the fitness sharing technique. The simulation results show that the proposed method can mitigate the contradiction of the long term the optimization algorithm takes but a short running time the trajectory gets, demonstrating the effectiveness of the proposed method. Besides, the improved fitness sharing technique has reduced the subjective process of determining relevant parameters and the optimized trajectory results met performance constraints of the robot joints.
Highlights
Genetic Algorithm (GA), capable of avoiding local minimum, independent of gradient information, is extremely suitable for large-scale complex optimization problem that traditional methods found hard to model and solve
From the point of view of productivity, the joint space constraint condition of the robot was set, which was guided by the motor and reducer to give a full play to their potential. e optimal trajectory running time is considered as the optimization objective, and the natural language expression of its constraint conditions is the corresponding velocity, acceleration, and jerk of each joint at every trajectory point do not exceed the upper limit of joint performance. e optimization target and constrains were shown in equation (6), where i is the index of time intervals, while j is the index of joints; jCjaCj, and vCj, respectively, stand for the upper limit of the jth joint in the field of jerk acceleration and velocity
Based on the fact that normal distribution has been widely used in simulating natural systems, this paper proposes to replace the classical fitness function with modified normal distribution function to reduce the subjective process of quantitative estimation of crowding degree. shm(d) represents the fitness function after modification
Summary
Genetic Algorithm (GA), capable of avoiding local minimum, independent of gradient information, is extremely suitable for large-scale complex optimization problem that traditional methods found hard to model and solve. E quantum genetic algorithm (QGA) was used by Chen and Zhou [13] to perform trajectory planning for a space manipulator with a floating base. It borrows the concept from quantum theory and encodes GA’s chromosome with quantum states, while population propagation is accomplished by a quantum rotate gate, enriching GA’s genetic diversity. It is proposed to solve the problem that the coarse design of the termination conditions and the granularity of the traversal domain can erode the expected time-saving when GA is used routinely in the situation that the trajectory running time relative to the optimization process is short-lived
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