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Introduction
An autonomous robot is a programmable and multi-functional machine, able to extract
information from its surrounding using different kinds of sensors to plan and execute
collision free motions within its environment without human intervention. Navigation is a
crucial issue for robots that claim to be mobile. A navigation system can be divided into two
layers: High level global planning and Low-level reactive control. In high-level planning, a
prior knowledge of environment is available and the robot workspace is completely or
partially known. Using the world model, the global planner can determine the robot motion
direction and generates minimum-cost paths towards the target in the presence of complex
obstacles. However, since it is not capable of changing the motion direction in presence of
unforeseen or moving obstacles, it fails to reach target. In contrast, in low-level reactive
control, the robot work space is unknown and dynamic. It generates control commands
based on perception-action configuration, which the robot uses current sensory information
to take appropriate actions without planning process. Thus, it has a quick response in
reacting to unforeseen obstacles and uncertainties with changing the motion direction.
Several Artificial intelligence techniques such as reinforcement learning, neural networks,
fuzzy logic and genetic algorithms, can be applied for the reactive navigation of mobile
robots to improve their performance. Amongst the techniques ability of fuzzy logic to
represent linguistic terms and reliable decision making in spite of uncertainty and imprecise
information makes it a useful tool in control systems.
Fuzzy control systems are rule-based or knowledge-based systems containing a collection of
fuzzy IF-THEN rules based on the domain knowledge or human experts. The simplicity of
fuzzy rule-based systems, capability to perform a wide variety tasks without explicit
computations and measurements make it extensively popular among the scientists and
researcher. This book chapter presents the significance and effectiveness of fuzzy logic in
solving the navigation problem. The chapter is organized as follows:
After the introduction of fuzzy logic importance in mobile robot navigation, Section 2
reviews methodology of previous works on navigation of mobile robots using fuzzy logic
design. Section 3, first gives a brief description about the design of a Fuzzy Controller, then
a case study shows how the fuzzy control system is used in mobile robots navigation.
Results from real systems address the fuzzy control influence and effectiveness to solve
some of the navigation difficulties and to reduce their navigation costs. Closing this book
chapter, Section 4 concludes the chapter with few comments and summarizes the
advantages and limitations of using fuzzy logic in mobile robot navigation. The chapter can
be interesting for students, researchers and different scientific communities in the areas of
robotics, artificial intelligence, intelligent transportation systems, and fuzzy control.
2. Review of fuzzy logic applications for mobile robot navigation
Robust and reliable navigation in dynamic or unknown environment relies on ability of the
robots in moving among unknown obstacles without collision and fast reaction to
uncertainties. It is highly desirable to develop these tasks using a technique which utilize
human reasoning and decision making. Fuzzy logic provides a means to capture the human
mind’s expertise. It utilizes this heuristic knowledge for representing and accomplishment
of a methodology to develop perception-action based strategies for mobile robots
navigation. Furthermore, the methodology of the FLC is very helpful dealing with
uncertainties in real world and accurate model of the environment is not absolutely required
for navigation. Therefore, based on a simple design, easy implementation and robustness
properties of FLC, many approaches were developed to solve mobile robot navigation
problem in target tracking, path tracking, obstacle avoidance, behaviour coordination,
environment modelling, and layer integration (Saffiotti, 1997). This section reviews the
proposed fuzzy control methods which used fuzzy sets for velocity control, rotation control
and command fusion with focusing on the three most popular categories of: Path tracking,
Obstacle avoidance and Behavior coordination.
2.1 Fuzzy logic for path tracking
Path tracking is a crucial function for autonomous mobile robots to navigate along a desired
path. This task includes tracking of previously computed paths using a path planner, a
defined path by human operator, tracking of walls, road edges, and other natural features in
the robot workspace (Chee et al., 1996). It involves real-time perception of the environment
to determine the position and orientation of the robot with respect to the desired path. For
example in Figure 1, if the robot is misplaced, the controller task is to steer it back on course
and minimize the orientation error (Δφ) and the position error (Δx) (Moustris & Tzafestas,
2005). Path tracking difficulties in dealing with imprecise or incomplete perception of
environment, representation of inaccuracy in measurements, sensor fusion and compliance
with the kinematic limits of the vehicle motivated many researchers to use fuzzy control
techniques for path tracking.
Ollero et al. (1994) developed a new fuzzy path-tracking method by combining fuzzy logic
with the geometric pure-pursuit and the generalized predictive control techniques. Fuzzy
logic is applied to supervise path trackers. Input of the fuzzy is the current state of the robot
to the path to generate the appropriate steering angle. A new approach proposed by
Braunstingl et al. (1995) to solve the wall following of mobile robots based on the concept of
general perception. To construct a general perception of the surroundings from the
measuring data provided by all the sensors and representing, a perception vector is assigned to each ultrasonic sensor. All these vectors adding together then combine into a single vector
of general perception. A fuzzy controller then uses the perception information to guide the
robot along arbitrary walls and obstacles. Sanchez et al. (1999) proposed a fuzzy control
system for path tracking of an autonomous vehicle in outdoor environment. The fuzzy
controller is used to generate steering and velocity required to track the path using the data
collected from experiments of driving the vehicle by a human. Bento et al. (2002) implemented
a path-tracking method by means of fuzzy logic for a Wheeled Mobile Robot. Input variables
of the fuzzy controller are position and orientation of the robot with respect to the path.
Output variables are linear velocity and angular velocity. Hajjaji and Bentalba (2003) have
designed a fuzzy controller for path tracking control of vehicles using its nonlinear dynamics
model. A Takagi–Sugeno (T–S) fuzzy model presents the nonlinear model of the vehicle. Then
a model-based fuzzy controller is developed based on the T–S fuzzy model. A wall-following
robot presented by Peri & Simon (2005) which the robot’s motion is controlled by a fuzzy
controller to drive it along a predefined path. Antonelli et al. (2007) address a path tracking
approach based on a fuzzy-logic set of rules which emulates the human driving behavior. The
fuzzy system input is represented by approximate information concerning the knowledge of
the curvature of the desired path ahead the vehicle and the distance between the next bend
and the vehicle. The output is the maximum value of the linear velocity needed to attain by the
vehicle in order to safely drive on the path. Yu et al. (2009) used Taguchi method to design an
optimal fuzzy logic controller for trajectory tracking of a wheeled mobile robot. Recently,
Xiong and Qu (2010) developed a method for intelligent vehicles’ path tracking with two fuzzy
controller combinations which controls vehicle direction and a preview fuzzy control method
presented by Liao et al. (2010) for path tracking of intelligent vehicle. The vehicle speed and
direction are adjusted by fuzzy control according to future path information and present path
information respectively.
Obstacle avoidance using fuzzy logic
Ability of a robot to avoid collision with unforeseen or dynamic obstacles while it is moving
towards a target or tracking a path is a vital task in autonomous navigation. Navigation
strategies can be classified to global path planning and local path planning. In global path
planning, information about the obstacles and a global model of environment is available
which mostly Configuration space, Road map, Voronoi diagram and Potential field techniques are used to plan obstacle-free path towards a target. However, in real world a reliable map of
obstacle, accurate model of environment and precise sensory data is unavailable due to
uncertainties of the environment. While the computed path may remain valid but to response
the unforeseen or dynamic obstacles, it is necessary for the robot to alter its path online. In
such situations, Fuzzy logic can provide robust and reliable methodologies dealing with the
imprecise input with low computational complexity (Yanik et al., 2010). Different obstacle
avoidance approaches were developed during past decades which proposed effective solution
to the navigation problems in unknown and dynamic environments.
Chee et al. (1996) presented a two-layer fuzzy inference system in which the first layer fuses
the sensor readings. The left and right clearances of the robot were found as outputs of the
first-layered fuzzy system. The outputs of the first layer together with the goal direction are
used as the inputs of the second-layer. Eventually, the final outputs of the controller are the
linear velocity and the turning rate of the robot. The second-stage fuzzy inference system
employs the collision avoiding, obstacle following and goal tracking behaviours to achieve
robust navigation in unknown environments. Dadios and Maravillas (2002) proposed and
implemented a fuzzy control approach for cooperative soccer micro robots. A planner
generates a path to the destination and fuzzy logic control the robot’s heading direction to
avoid obstacles and other robots while the dynamic position of obstacles, ball and robots are
considered. Zavlangas et al. (2000) developed a reactive navigation method for
omnidirectional mobile robots using fuzzy logic. The fuzzy rule-base generates actuating
command to get collision free motions in dynamic environment. The fuzzy logic also provides
an adjustable transparent system by a set of learning rules or manually. Seraji and Howard
(2002) developed a behavior-based navigation method on challenging terrain using fuzzy
logic. The navigation strategy is comprised of three behaviors. Local obstacle avoidance
behaviour is consists of a set of fuzzy logic rule statements which generates the robot’s speed
based on obstacle distance. Parhi (2005) described a control system comprises a fuzzy logic
controller and a Petri Net for multi robot navigation. The Fuzzy rules steer the robot according
to obstacles distribution or targets position. Since the obstacle’s position is not known
precisely, to avoid obstacles in a cluttered environment fuzzy logic is a proper technique for
this task. Combination of the fuzzy logic controller and a set of collision prevention rules
implemented as a Petri Net model embedded in the controller of a mobile robot enable it to
avoid obstacles that include other mobile robots. A fuzzy controller designed by Lilly (2007)
for obstacle avoidance of an autonomous vehicle using negative fuzzy rules. The negative
fuzzy rules define a set of actions to be avoided to direct the vehicle to a target in presence of
obstacles. Chao et al. (2009) developed a fuzzy control system for target tracking and obstacle
avoidance of a mobile robot. Decision making is handled by the fuzzy control strategy based
on the sensed environment using a stereo vision information. A vision- based fuzzy obstacle
avoidance proposed for a humanoid robot in (Wong et al., 2011). The nearest obstacle to the
robot captured by vision system and the difference angle between goal direction and the
robot’s heading measured by electronic compass are inputs of the fuzzy system to make a
decision for appropriate motion of the robot in unknown environment.
2.3 Fuzzy logic for behaviour coordination
To improve the total performance of a navigation system, complex navigation tasks are broken
down into a number of simpler and smaller subsystems (behaviors) which is called behaviorbased
system. In a behavior-based system, each behavior receives particular sensory nformation and transforms them into the predefined response. The behaviors include path
tracking, obstacle avoidance, target tracking, goal reaching and etc.