scholarly journals Modelling of Ship’s Trajectory Planning in Collision Situations by Hybrid Genetic Algorithm

2018 ◽  
Vol 25 (3) ◽  
pp. 14-25 ◽  
Author(s):  
Shengke Ni ◽  
Zhengjiang Liu ◽  
Yao Cai ◽  
Xin Wang
Keyword(s):  
Genetic Algorithm ◽  
Collision Avoidance ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Hybrid Genetic Algorithm ◽  
Test Cases ◽  
Open Sea ◽  
Restricted Condition ◽  
Ship Collision ◽  
Optimal Trajectory Planning

Abstract Ship collision-avoidance trajectory planning aims at searching for a theoretical safe-critical trajectory in accordance with COLREGs and good seamanship. In this paper, a novel optimal trajectory planning based on hybrid genetic algorithm is presented for ship collision avoidance in the open sea. The proposed formulation is established based on the theory of the Multiple Genetic Algorithm (MPGA) and Nonlinear Programming, which not only overcomes the inherent deficiency of the Genetic Algorithm (GA) for premature convergence, but also guarantees the practicality and consistency of the optimal trajectory. Meanwhile, the encounter type as well as the obligation of collision avoidance is determined according to COLREGs, which is then considered as the restricted condition for the operation of population initialization. Finally, this trajectory planning model is evaluated with a set of test cases simulating various traffic scenarios to demonstrate the feasibility and superiority of the optimal trajectory.

Energy Optimal Trajectory Planning of a Robotic Manipulator Using Genetic Algorithm

2010 ◽  
Author(s):  
Aritra Biswas ◽  
B. L. Deekshatulu ◽  
Shibendu Shekhar Roy ◽  
Swapan Paruya ◽  
Samarjit Kar ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Robotic Manipulator ◽  
Optimal Trajectory Planning

scholarly journals Optimal trajectory planning of complicated robotic timber joints based on particle swarm optimization and an adaptive genetic algorithm

2021 ◽  
Author(s):  
Yiping Meng ◽  
Yiming Sun ◽  
Wen-shao Chang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Particle Swarm ◽  
Adaptive Genetic Algorithm ◽  
Swarm Optimization ◽  
Timber Joint ◽  
Timber Joints ◽  
Optimal Trajectory Planning

AbstractIn this paper, a methodology for path distance and time synthetic optimal trajectory planning is described in order to improve the work efficiency of a robotic chainsaw when dealing with cutting complex timber joints. To demonstrate this approach one specific complicated timber joint is used as an example. The trajectory is interpolated in the joint space by using a quantic polynomial function which enables the trajectory to be constrained in the kinematic limits of velocity, acceleration, and jerk. The particle swarm optimization (PSO) is applied to optimize the path of all cutting surfaces of the timber joint in operating space to achieve the shortest path. Based on the optimal path, an adaptive genetic algorithm (AGA) is used to optimize the time interval of interpolation points of every joint to realize the time-optimal trajectory. The results of the simulation show that the PSO method shortens the distance of the trajectory and that the AGA algorithm reduces time intervals and helps to obtain smooth trajectories, validating the effectiveness and practicability of the two proposed methodology on path and time optimization for 6-DOF robots when used in cutting tasks.

scholarly journals A Dual-Thread Method for Time-Optimal Trajectory Planning in Joint Space Based on Improved NGA

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Kaipeng Zhang ◽  
Ning Liu ◽  
Gao Wang
Keyword(s):  
Genetic Algorithm ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Joint Space ◽  
Time Saving ◽  
Expected Time ◽  
Fitness Sharing ◽  
Elite Strategy ◽  
Time Optimal ◽  
Optimal Trajectory Planning

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.

The Simulation and Analyses of Timing-Optimal Trajectory Planning for 6DOF Robot

2014 ◽  
Vol 716-717 ◽  
pp. 1555-1558
Author(s):  
Zhi Jian Gou
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimal Solution ◽  
Global Search ◽  
Search Performance ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Optimal Trajectory Planning

The algorithm has been improved to the adaptive genetic operators and flow based on the basic theory of simple genetic algorithm and adopted elitism strategy to select the best individual for iterative operation. The improved genetic algorithm not only ensured better global search performance, but also improved the convergent speed. The optimal solution was obtained and simulated by the improved genetic algorithms under the kinematical constraints.

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