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Group-Based Particle Swarm Optimization for Multiple-Vehicles Trajectory Planning
Anugrah K. Pamosoaji,Keum-Shik Hong 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
This paper discusses a class of group-based particle swarm optimization (GBPSO) used for figuring out admissible velocities on the three-degree Bezier-based path. Constraints of maximum allowable radial and tangential accelerations and tangential velocities are considered. The proposed method is designed for performing minimum-time collision-free trajectories in a multiple-vehicle system. The problem of minimizing the reaching time of the slowest vehicle is addressed. Additionally, the problem of generating the velocities of individual paths based on parameter and time (i.e., radial and tangential velocities) is presented as well. A particle group represents a set of particles containing the path’s two control points of each vehicle. The searching process executed by the GBPSO can be described as searching the suitable control points that perform minimum time trajectories. The first and last two control points are used as the state vector of a single particle. The proposed method has advantages in shortening velocity profile generation time and thus enhances the searching time. The results of a simulation demonstrating the performance of the proposed GBPSO also are presented.
Anugrah Kusumo Pamosoaji,Augie Widyotriatmo,Keum-Shik Hong 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10
A motion planning algorithm for a nonholonomic vehicle in triangular regions is investigated. The regions are the result of splitting a larger and complex workspace, and are classified into three classes, that are, empty regions, obstacle-inside regions, and goal regions. The vehicle has to achieve a goal configuration from any initial configuration in the workspace. A set of procedures to generate velocity vector fields by utilizing vector potential functions is proposed. The vector fields are categorized as those generated by the edges of regions, obstacles, and goal points. To deal with some constraints, i.e., maximum velocities, a set of parameter-scaling rules is provided. A state-feedback controller for a unicycle vehicle is used to show that the generated motion plan can be tracked by the vehicle. Simulation results showing the motion planning from different initial configuration are presented.
Anugrah K. Pamosoaji,Mingxu Piao,홍금식 제어·로봇·시스템학회 2019 International Journal of Control, Automation, and Vol.17 No.10
This paper discusses a particle swarm optimization (PSO)-based motion-planning algorithm in a multiple-vehicle system that minimizes the traveling time of the slowest vehicle by considering, as constraints, the radial and tangential accelerations and maximum linear velocities of all vehicles. A class of continuous-curvature three-degree Bezier curves are selected as the basic shape of the vehicle trajectories to minimize the number of parameters required to express them mathematically. In addition, velocity profile generation using the local minimum of the radial-accelerated linear velocity profile, which reduces the calculation effort, is introduced. A new PSO-based search algorithm, called “particle-group-based PSO,” is introduced to find the best combination of trajectories that minimizes the traveling time of the slowest vehicle. A particle group is designed to wrap up a set of particles representing each vehicle. The first and last two control points characterizing a curve are used as the state vector of a particle. Simulation results demonstrating the performance of the proposed method are presented. The main advantage of the proposed method is its minimization of the velocity-profile-generation time, and thereby, its maximization of the search time.
Zheming Cao,Anugrah Kusumo Pamosoaji,Keum-Shik Hong 대한기계학회 2009 대한기계학회 춘추학술대회 Vol.2009 No.5
In this paper, we will implement polar polynomial curve (PPC) to produce a continuous-curvature path to guide an unmanned autonomous forklift from an initial to a final position in an unknown environment. Generalized polar polynomials are thus obtained which yields the path for fast motions. Important properties of the polynomials are shown and their use provides a method for computing the best motion consistent with the constraints. The approach is used for a forklift's dynamic model. The simulation result of the approach will be shown.