In this paper application of average fuzzy inference technique has been analysed for navigation control of robotic agent. Also,
the reviews of other AI techniques for control of robots are carried out. The robotic agent uses sensors to map the surroundings
and take the decision with the help of Fuzzy AI technique to avoid obstacles. In this paper a novel averaging method has been
deployed to optimize the results obtained from various fuzzy membership functions. Using the Average Fuzzy Inference technique
robot navigates from start position to goal position avoiding obstacles while reaching the target. The simulation results agree
with experimental results. The methodology can be used for various applications by the scientific communities to address various
engineering problems.
Keywords:Robot; Artificial intelligence; Control; Navigation; Average fuzzy inference
Scientists throughout the world are working on various AI
techniques to address control strategies of robots. The works
done by various researchers to navigate and control robots in
various environmental conditions are given below. Navigation
of mobile robots using various AI techniques in highly cluttered
environments while avoiding obstacles have been reported in [1-4].
AI techniques [5-8] are found to be suitable for robot’s navigation
control. In Ant Colony optimization technique, rate of pheromones
deposition and evaporation have been used mathematically for
getting a methodology to address various optimization problems in
engineering and scientific fields. Papers [9-11] have elaborated Ant
Colony optimization technique for control of mobile robots in various
complex environments. The authors have carried out various types
of exercises to corroborate their claimed methodology. Control of
7-degree redundant manipulator has been analysed in paper [12]
.Bacteria foraging methodology [13-14] has been used by scientists
for motion planning of humanoid type robots. Hybrid Differential
Evolution Algorithm [15] and Cuckoo Search Algorithm [16-17]
have been discussed for solving various engineering problems
by researchers. Researchers have used Dayani AI [18] method to
control two wheeled mobile robots in an unknown environment.
Using Daykun-Bip virtual target AI method [19] robots can find the
targets in a highly cluttered environment. Fuzzy logic [20-24] is
one of the robust artificial intelligence techniques used for solving
various engineering problems. Various scientist has used fuzzy
logic [25-28] to navigate robots in highly cluttered environments.
Mobile robots can use fuzzy inference technique [29-33] for selfautonomous
control in complex environments. Finite element
analysis [34-37] can be utilized for evaluating the mechanical
properties of various structures used for fabricating bodies and
frames of the robots. Nature driven Fire Fly algorithm is one of the
promising AI technique to address many optimization problems.
Engineers have used Fire Fly algorithm [38-40] for path planning
of robotic agents in uncertain environments. Fuzzy Inference technique has been hybridized with neural network technique to
obtain Neuro-Fuzzy technique[41-46]. Many researchers have used
Fuzzy-Neuro [47-51] techniques in the modern time for handling
optimization problems also for finding optimized paths for robotic
agents in highly cluttered unknown environments
Flower Pollination Algorithm (FPA) and Bat Algorithm (BA)
in combination have been discussed in the paper [52] for finding
optimized path of robot in obstacles prone environments. Gait
analysis has been discussed in the paper [53] for movement pattern
of the robots. Genetic Algorithm [54-57] is one of the efficient AI
technique for finding solutions for various engineering problems.
Paper [58] discussed about the suitable use of Genetic Algorithm
for Intelligent Robot control. Researcher has used Harmonic Search
[59] algorithm for finding out optimal solution. Immune system has
been mathematically encoded, to get Artificial Immune system [60-
62] and are subsequently used for solving complex optimization
applications. Papers [63-65] have discussed about Artificial Immune
System for navigation control of robots in complex environments.
Paper [66] has discussed about invasive optimization technique
as path planner for mobile robot navigation. Paper [67-70] have
discussed about the kinematic analysis of various types of robots
so that they can be applied in practical fields. Paper [71-72] have
discussed about mobile computing techniques using artificial
intelligence technique. Evolution of Brain is one of the important
links in biological evolution. This is due to evolution of network
consisting of neurons. Papers [73-77] have discussed about neural
networks for navigational control of mobile robots in highly
cluttered environments. Neural networks [78-82] can be efficiently
used for solving various engineering problems along with problems
related to robots’ control. Potential energy attraction has been used
by scientists and engineers to model artificial intelligence potential
field method for solving various engineering problems. Papers [83-
84] have discussed about the use of artificial potential field method
to address control of robot in various environments. Particle Swarm
Optimisation (PSO) is one of the robust natures driven artificial
intelligence technique for solving various complex optimisation
problems. PSO [85-87] has been used efficiently to handle various
robot control related problem in uncertain environments. Real time
navigation for mobile robot has been address in the paper [88]
subjected to unknown environment. Statistical Regression based
analysis [89-91] has been used to analyse robot dynamics subjected
various situations. Use of artificial intelligence techniques for robot
control and path planning has been discussed in papers [92-93]. Rule
based algorithms [94-95] are derived from various mathematical
functions. They can be used successfully for various robotic control
tasks. Simulated Algorithm [96] has a high potential and has been
used by researchers to handle various robot control. Various
engineering optimisation problems can be solved by soft computing
methods. Many engineers have used soft computing methods [97-
98] for solving various robot control related problems. Nature
inspired swarm intelligence technique [99-103] has been used for
solving robot navigational problems. Scientists and engineers have
used various methods to analyse crack identification [104-111] in
vibrating structures. Papers [112-125] discuss about novel neural
network and fuzzy logic to address problem of crack identification
in complicated dynamic structures. Papers [126-129] discuss about
hybrid AI techniques for robot control.
The paper uses Average Fuzzy Inference Technique for Robot
Control. For average fuzzy technique two sets of membership
functions (Triangular and Trapezoidal) are used. The inputs to
the fuzzy inference systems are various sensors inputs (such as
Left Obstacle Distance-LOS; Right Obstacle Distance-ROS and
Front Obstacle Distance-FOS). The output is the average steering
angle calculated from the Steering Angle (SA) of Fuzzy Inference
Technique-1 (Consists of Triangular Membership Functions) and
Fuzzy Inference Technique-2 (Consists of Trapezoidal Membership
Functions). Figure 1 shows the architecture of the Average
Fuzzy Inference System. Figure 2 shows the Simulation Path and
Experimental Path for Khepera-II mobile robot [130] from start to
goal. Table 1 shows the simulation and experimental path length
and time taken. The deviation of Path length and time taken are to
be found within 2%.
Table 1: Simulation and Experimental Path Length and Time Taken.
Figure 1: Architecture of Average Fuzzy Inference System.
Figure 2: Simulation and Experimental Result of Khepera-II Robot from Start to Goal Point.
From the above analysis and reviews on mobile robots the
following conclusions are drawn. From the analysis it is concluded
that using average fuzzy inference technique robot can negotiate
with obstacles and reach the target efficiently. The results are
compared in simulation and experimental modes and the deviation
between them is found to be within 2%. During the review of
papers, it has been found that artificial intelligence technique can
be used efficiently for solving various engineering problems and
robotic related problems. In the future more robust AI methods
will be explored for solving robot navigation problem in efficient
manner.
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