Preface

Chapter 8 **Motion Planning for Mobile Robots 145**

**VI** Contents

Xiangrong Xu, Yang Yang and Siyu Pan

Chapter 9 **Design and Implementation of a Demonstrative Palletizer**

**Robot with Navigation for Educational Purposes 167**

Montoro-Sanjose and Mario-Alberto Ibarra-Manzano

Dora-Luz Almanza-Ojeda, Perla-Lizeth Garza-Barron, Carlos Rubin

The book *Advanced Path Planning for Mobile Entities* provides a platform for practicing re‐ searchers, academics, PhD students and other scientists to design, analyze, evaluate, process and implement diversiform issues of path planning, including algorithms for multipath and mobile planning and path planning for mobile robots. The nine chapters of the book demon‐ strate capabilities of advanced path planning for mobile entities to solve scientific and engi‐ neering problems with varied degree of complexity.

The first five chapters related to advanced algorithms for multipath planning provide de‐ tails of methods for the consensus-based multipath planning with the collision avoidance applied in various environments and developed algorithms for search-based motion plan‐ ning and path planning on quadric surfaces.

The second four chapters associated with extended path planning for mobile robots demon‐ strate possibilities of new approaches in path planning using neural network memory or parametric curves, motion planning, and navigation focused on mobile robots.

I hope that beginners and professionals in the field would benefit by going through the de‐ tails given in the chapters of this book.

> **Rastislav Róka** Slovak University of Technology Institute of MICT FEI STU Bratislava, Slovakia

**Section 1**

**Advanced Algorithms for Multi-Path Planning**

**Advanced Algorithms for Multi-Path Planning**

**Chapter 1**

Provisional chapter

**Consensus-Based Multipath Planning with Collision**

DOI: 10.5772/intechopen.71288

Consensus-Based Multipath Planning with Collision

Consensus theory has been widely applied to collective motion planning related to coordinated motion. However, when the collective motion is highly irregular and adversarial, the basic consensus theory does not guarantee collision avoidance by default. As collision avoidance is a central problem of path planning, the incorporation of avoidance into the consensus algorithm is a subject of research. This work presents a new method of incorporating collision avoidance into the consensus algorithm, by applying the concept of constrained orientation control, where orientation constraints are represented as a set of linear matrix inequalities (LMI) and solved by semidefinite programming (SDP). The developed algorithm is used to simulate consensus-based multipath plan-

Path planning has found practical applications in areas such as entertainment (e.g. robot soccer) [1]; self-driving vehicles (e.g. Google's self-driving cars) [2]; intelligent highways [3], and multiple unmanned space systems [4]. Because of the potential applications, the topic of

The simplicity and potential of consensus algorithms to generate collective behaviors, such as flocking, platooning, rendezvous, and other formation configurations, make it an attractive choice for solving certain problems in multiagent control. However, the basic consensus algorithm collision avoidance mechanism is not developed for adversarial situations (i.e., opposite or attacking motion). To extend the power of the algorithm, it is therefore necessary to develop

> © The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

distribution, and reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

ning with collision avoidance for a team of communicating soccer robots.

Keywords: consensus, path planning, avoidance, optimization, LMI

multipath planning has been studied extensively, for example in [5–11].

more powerful collision avoidance capabilities.

**Avoidance Using Linear Matrix Inequalities**

Avoidance Using Linear Matrix Inequalities

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.71288

Innocent Okoloko

Innocent Okoloko

Abstract

1. Introduction

Provisional chapter
