Path planning of cooperating industrial robots using evolutionary algorithms

Evolutionary Path Planning Algorithm for Industrial Robots

This paper proposed a new methodology to solve collision free path planning problem for industrial robot using genetic algorithms. The method poses an optimization problem that aims to minimize the significant points traveling distance of the robot. The behaviour of more two operational parameters – the end effector traveling distance and computational time – is analysed. This algorithm is able...

Determination of Location and Path Planning Algorithms for Industrial Robots

Dynamic Environment Robot Path Planning Using Hierarchical Evolutionary Algorithms

The problem of path planning deals with the computation of an optimal path of the robot, from source to destination, such that it does not collide with any obstacle on its path. In this paper we solve the problem of path planning separately in two hierarchies. The coarser hierarchy finds the path in a static environment consisting of the entire robotic map. The resolution of the map is reduced ...

Comparing the efficiency of five algorithms applied to path planning for industrial robots

PurposeTo compare the quality and efficiency of five methods for solving the path planning problem of industrial robots in complex environments Design/methodology/approachFive methods are presented for solving the path planning problem and certain working parameters have been monitored using each method. These working parameters are the distance travelled by the robot and the computational time...

Parallel Evolutionary Algorithms for Uav Path Planning

Evolutionary computation (EC) techniques have been successfully applied to compute near-optimal paths for unmanned aerial vehicles (UAVs). Premature convergence prevents evolutionary-based algorithms from reaching global optimal solutions. This often leads to unsatisfactory routes that are suboptimal to optimal path planning problems. To overcome this problem, this paper presents a framework of...