Region-adaptive path planning for precision optical polishing with industrial robots

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...

PSO-Based Path Planning Algorithm for Humanoid Robots Considering Safety

In this paper we introduce an improvement in the path planning algorithm for the humanoid soccer playing robot which uses Ferguson splines and PSO (Particle Swarm Optimization). The objective of the algorithm is to find a path through other playing robots to the ball, which should be as short as possible and also safe enough. Ferguson splines create preliminary paths using random generated para...

Determination of Location and Path Planning Algorithms for Industrial Robots

Nonmyopic Adaptive Informative Path Planning for Multiple Robots

Many robotic path planning applications, such as search and rescue, involve uncertain environments with complex dynamics that can be only partially observed. When selecting the best subset of observation locations subject to constrained resources (such as limited time or battery capacity) it is an important problem to trade off exploration (gathering information about the environment) and explo...

Combining manual haptic path planning of industrial robots with automatic path smoothing

Nowadays, industrial robots are preferably programmed offline, i.e. without interference with the real cell and running production processes. Usually a simulation tool is used to manually define individual locations and to check the created raw path for possible collisions. Within this paper an approach is presented, that combines a haptic input device by means of automated path smoothing. The ...