Abstract

There are various motion planning techniques for robots or agents, such as bug algorithm, visibility graph, Voronoi diagram, cell decomposition, potential field, and other probabilistic algorithms. Each technique has its own advantages and drawbacks, depending on the number and shape of obstacles and performance criteria. Especially, a potential field has vector values for movement guidance to the goal, and the method can be used to make an instantaneous and smooth robot movement path without an additional controller. However, there may be some positions with zero force value, called local minima, where the robot or agent stops and cannot move any further. There are some solutions for local minima, such as random walk or backtracking, but these are not yet good enough to solve the local minima problem. In this paper, we propose a novel movement guidance method that is based on the water sink model to overcome the previous local minima problem of potential field methods. The concept of the water sink model is to mimic the water flow, where there is a sink or bathtub with a plughole and floating piece on the water. The plughole represents the goal position and the floating piece represents robot. In this model, when the plug is removed, water starts to drain out via the plughole and the robot can always reach the goal by the water flow. The water sink model simulator is implemented and a comparison of experimental results is done between the water sink model and potential field.

Highlights

  • IntroductionBug algorithms are very intuitive and simple, but inefficient in many cases [1], while visibility graphs can guarantee the shortest path on a two-dimensional (2D) map, but make low clearance from obstacles and are only good for polygonal obstacle environments [2]

  • There are a number of motion planning techniques for robots or agents

  • In the results of the potential field method, it could be found that the path length could be too much large, even though the complexity of map is simple

Read more

Summary

Introduction

Bug algorithms are very intuitive and simple, but inefficient in many cases [1], while visibility graphs can guarantee the shortest path on a two-dimensional (2D) map, but make low clearance from obstacles and are only good for polygonal obstacle environments [2]. PRM makes a graph with node that is randomly generated and find the shortest path on the graph [5,6]. The RRT makes a tree that is randomly expanded from the starting point to the goal. The randomized kinodynamic planning considers velocity, acceleration, force, and torque of mobile robot with original RRT [7].

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call