Anis Koubaa - Robot Path Planning and Cooperation: Foundations, Algorithms and Experimentations

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Part I
Global Robot Path Planning

Springer International Publishing AG, part of Springer Nature 2018

Anis Koubaa , Hachemi Bennaceur , Imen Chaari , Sahar Trigui , Adel Ammar , Mohamed-Foued Sriti , Maram Alajlan , Omar Cheikhrouhou and Yasir Javed Robot Path Planning and Cooperation Studies in Computational Intelligence

1. Introduction to Mobile Robot Path Planning

Anis Koubaa 1 , Hachemi Bennaceur 2, Imen Chaari 3, Sahar Trigui 3, Adel Ammar 2, Mohamed-Foued Sriti 2, Maram Alajlan 2, Omar Cheikhrouhou 4 and Yasir Javed 5

(1)

Prince Sultan University, Riyadh, Saudi Arabia

(2)

College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia

(3)

University Campus of Manouba, Manouba, Tunisia

(4)

College of Computers and Information Technology, Taif University, Taif, Saudi Arabia

(5)

College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

Anis Koubaa

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Abstract

Robotic is now gaining a lot of space in our daily life and in several areas in modern industry automation and cyber-physical applications. This requires embedding intelligence into these robots for ensuring (near)-optimal solutions to task execution. Thus, a lot of research problems that pertain to robotic applications have arisen such as planning (path, motion, and mission), task allocation problems, navigation, tracking. In this chapter, we focused on the path planning research problem.

1.1 Introduction

Moving from one place to another is a trivial task, for humans. One decides how to move in a split second. For a robot, such an elementary and basic task is a major challenge. In autonomous robotics, path planning is a central problem in robotics. The typical problem is to find a path for a robot, whether it is a vacuum cleaning robot, a robotic arm, or a magically flying object, from a starting position to a goal position safely. The problem consists in finding a path from a start position to a target position. This problem was addressed in multiple ways in the literature depending on the environment model, the type of robots, the nature of the application, etc.

Safe and effective mobile robot navigation needs an efficient path planning algorithm since the quality of the generated path affects enormously the robotic application. Typically, the minimization of the traveled distance is the principal objective of the navigation process as it influences the other metrics such as the processing time and the energy consumption.

This chapter presents a comprehensive overview on mobile robot global path planning and provides the necessary background on this topic. It describes the different global path planning categories and presents a taxonomy of global path planning problem.

1.2 Overview of the Robot Path Planning Problem

Nowadays, we are at the cusp of a revolution in robotics. A variety of robotic systems have been developed, and they have shown their effectiveness in performing different kinds of tasks including smart home environments []. An intelligence must be embedded into robot to ensure (near)-optimal execution of the task under consideration and efficiently fulfill the mission. However, embedding intelligence into robotic system imposes the resolution of a huge number of research problems such as navigation which is one of the fundamental problems of mobile robotics systems. To finish successfully the navigation task, a robot must know its position relatively to the position of its goal. Moreover, he has to take into consideration the dangers of the surrounding environment and adjust its actions to maximize the chance to reach the destination.

Putting it simply, to solve the robot navigation problem, we need to find answers to the three following questions: Where am I? Where am I going? How do I get there? These three questions are answered by the three fundamental navigation functions localization, mapping, and motion planning, respectively.

• Localization : It helps the robot to determine its location in the environment. Numerous methods are used for localization such as cameras []. The location can be specified as symbolic reference relative to a local environment (e.g., center of a room), expressed as topological coordinate (e.g., in Room 23) or expressed in absolute coordinate (e.g., latitude, longitude, altitude).
• Mapping : The robot requires a map of its environment in order to identify where he has been moving around so far. The map helps the robot to know the directions and locations. The map can be placed manually into the robot memory (i.e., graph representation, matrix representation) or can be gradually built while the robot discovers the new environment. Mapping is an overlooked topic in robotic navigation.
• Motion planning or path planning : To find a path for the mobile robot, the goal position must be known in advance by the robot, which requires an appropriate addressing scheme that the robot can follow. The addressing scheme serves to indicate to the robot where it will go starting from its starting position. For example, a robot may be requested to go to a certain room in an office environment with simply giving the room number as address. In other scenarios, addresses can be given in absolute or relative coordinates.

Planning is one obvious aspect of navigation that answers the question: What is the best way to go there? Indeed, for mobile robotic applications, a robot must be able to reach the goal position while avoiding the scattered obstacles in the environment and reducing the path length. There are various issues need to be considered in the path planning of mobile robots due to various purposes and functions of the virtual robot itself as shown in Fig. ].

Fig. 1.1

Different issues of path planning

Whatever the issue considered in the path planning problem, three major concerns should be considered: efficiency, accuracy, and safety []. Any robot should find its path in a short amount of time and while consuming the minimum amount of energy. Besides that, a robot should avoid safely the obstacles that exist in the environment. It must also follow its optimal and obstacle-free route accurately.

Planning a path in large-scale environments is more challenging as the problem becomes more complex and time-consuming which is not convenient for robotic applications in which real-time aspect is needed. In our research work, we concentrate on finding the best approach to solve the path planning for finding shortest path in a minimum amount of time. We also considered that the robot operates in a complex large environment containing several obstacles having arbitrary shape and positions.

1.2.1 Problem Formulation

In [], Latombe describes the path planning problem as follows:

• : The robot , it is a single moving rigid object in world W represented in the Euclidean space as or .
• 

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