Analysis and Design of Automated Transport
and Path Planning for Robots in Cluttered Environments using Novel Hybrid Average
Genetic-Neural Control Technique
Parhi Dayal R1* and Sat Chidananda2
1Department of Mechanical Engineering, National Institute of Technology Rourkela, Sundargarh, Odisha, India
2Delhi Public School, Rourkela, Sundargarh, Odisha, India
Submission:March 08, 2021; Published: March 25, 2021
*Corresponding Author:Parhi Dayal R, Department of Mechanical Engineering, National Institute of Technology Rourkela, Sundargarh, Odisha, India
How to cite this article:Parhi D, Sat C. Analysis and Design of Automated Transport and Path Planning for Robots in Cluttered Environments using
Novel Hybrid Average Genetic-Neural Control Technique. Civil Eng Res J. 2021; 11(3): 555815. DOI: 10.19080/CERJ.2021.11.555815
In the current investigation on automated transport and navigational path planning of robots, a new Hybrid Average Genetic-Neural (HAGN) technique has been developed. The HAGN technique uses genetic algorithm and multi layered neural technique as important parts for its development. The robots are equipped with several sensors to map the surrounding environments and to recognize the obstacles and targets around. During the navigation robots take into account front, left and right obstacle distances obtained from sensors to negotiate with obstacles and reach targets with the help of HAGN technique. To prove authenticity of the proposed method several simulation and experimental exercises have been carried out. Comparisons between simulation and experimental results are presented in pictorial and tabular forms. The deviation between simulation and experimental results are found to be within 2.8%. Other engineering applications can also be addressed using HAGN AI technique.
Keywords:Genetic; Neural; Average; Robots; Artificial intelligence Navigation, Control
Scientists and engineers have used different type of artificial intelligence techniques for addressing various engineering optimisation problems including various robots control related problems. Cuckoo birds laying eggs patterns have been mathematically modelled as cuckoo search algorithm [1-2] by researchers for solving control strategies of robots. Artificial potential field methods [3-4] have been used by researchers for automotive control of robots. Particle swarm optimisation method [5-7] is a community driven method used by engineers for addressing robot navigation problems. Artificial immune system [8-10] is one of the potential AI methods for addressing various engineering optimisation problems. Robots navigations have been addressed using artificial immune system in papers [11-13]. Firefly nature inspired algorithm [14,15] has been discussed by scientists and engineers for solving various optimisation problems. Paper  discusses navigation control of
robots in unknown environments. Swarm intelligence technique
[17-21] is inspired from pattern of herd community travel and has been used by researchers to solve optimisation problems including robots path optimisation problems. In today’s world Artificial Intelligence [22-24] techniques play important roles in solving various complicated problems. Papers [25-27] discuss about robot navigation control using AI techniques. Path planning of mobile robots have been analysed and discussed in research papers [28-29].
Regression based analysis [30-32] is used by many engineers to address various optimisation problem. In ant colony method , behaviour of ant movement has been mathematically modelled by researchers to solve various optimisation problems in engineering fields. Papers [34-36] have discussed path planning of mobile robot using ant colony optimisation technique. Dayani AI technique  has been used by researchers to control robots in unknown environments. Crack identifications in various elements
used for robotic structures have been done by the help of artificial
intelligence techniques and are discussed in papers [38-45].
Genetic algorithm [46-47] is one of frontier biological inspired
technique for solving various engineering problems. Papers [48-
50] discuss path planning of mobile robot using genetic algorithm.
Using various AI soft computing techniques researchers
have tried to analyse vibration signatures of different robotic
frames and skeletons and are depicted in the papers [51-57].
Suitable mathematical expressions can be coined together to
formulate rule based techniques [58-59] to address robot path
planning problems and other engineering problems. Papers [60-
66] discuss about various vibration patterns in dynamic robotic
structures with the help of smart intelligent techniques. Paper 
discusses analysis of robot manipulator used for various tasks.
Bacteria foraging method  is one of the promising artificial
intelligence technique used for solving navigational path planning
of mobile robot in cluttered environments. Differential evolution
algorithm  has been used by the researchers to address many
optimisation problems. Daykun-bip  AI technique is a smart
computational method, used by researchers to address path
planning of robots in unknown environments.
Using fuzzy inference techniques [71-74] researchers have
solved many automated problems for various engineering
applications. Several researchers have used fuzzy logic [75-78]
for navigation control of robots. In papers [79-81] obstacles
avoidance by robots has been achieved using fuzzy inference
techniques. Papers [82-84] analyse robots movements using fuzzy
logic techniques. Finite element methods have been discussed in
papers [85-88] for addressing structural dynamics of various
mechanical components in intelligent ways. Gait analysis of biped
robot has been investigated by engineers in the paper . Paper
 discusses harmonic search AI technique for robot control.
Kinematic analyses of robots have been discussed in the
papers [91-94]. Real time control of robot has been discussed in
the paper . Robot navigation can be addressed in unknown
terrain using artificial intelligence techniques [96-97]. Simulated
annealing  is mimicking of heat treatment processes and is
used as a soft computing method for solving local minima problem
during path planning of robots. Soft computing methods [99-100]
have been used by engineers to address various problems related
to control of robots movements. Paper  discusses hybrid
AI Cuckoo-Neuro technique for robot path analysis in complex
environments. Researchers have discussed about radial basis
neural network  for control of robots in unknown scenarios.
Hybrid method such as Simulated-Annealing-Neural technique
has been discussed in the paper  for complex robot
movements. Hybrid fuzzy immune  technique has been
analysed by researchers for target seeking of robots in cluttered
Mobile computing [105-106] can be used for addressing
various intelligent network communications. In neural network
[107-111] inputs are given to the neurons in input layers and
output is obtained from neuron in output layer. Neural networks
[112-116] have been used efficiently for robot navigation
control of robot. Using Bat algorithm  researchers have
tried to solve robot path planning problem. Neuro-fuzzy [118-
122] hybrid technique can be used efficiently for path planning
of robots from start to goal point while avoiding obstacles. In
this method neural algorithm has been hybridised with fuzzy
inference methods to obtain neuro-fuzzy [123-125] hybrid
technique. Various researchers have used neuro-fuzzy [126-128]
techniques to control mobile robots. Paper  analyses Grey
Wolf optimisation method for solving task management strategy
being carried out by robot. Navigation of mobile robot using
Cuckoo Search method has been discussed in the paper .
Papers [131-133] discusses about invasive weed optimisation
technique for navigation and control of robot subjected to various
conditions. Keeping in view the above findings a novel hybrid
average genetic-neuro control technique has been developed in
this paper to address navigation of mobile robots in cluttered
In the current paper genetic and neural controllers are
hybridised to get Genetic-Neural controller. The outputs from
genetic and neural controllers are obtained separately and the
average of them is taken as the final output from the hybrid
controller. The inputs to the hybrid controller (Figure 1) are
obstacle distances obtained from sensors mounted on front,
left and right directions of the robots. The output from Genetic
algorithm is steering angle-1 (SA-1) and the output from Neural
network is steering angle-2 (SA-2). The Genetic algorithm uses
several parents to produce offspring from the crossover and
subsequently finds the best child to get SA-1 according to fitness
function. The Neural network considered here consists of five
layers. The input layer has three neurons. Three hidden layers
have five, ten and three neurons. The output layer has one neuron
representing SA-2. The Final Steering Angle (FSA) for robots is
calculated by taking the average of SA-1 and SA-2. With the help
of FSA robots negotiate with obstacles in the process of achieving
targets during navigation. For carrying out the simulations and
experiments exercises Khepera-II  and Hemisson 
robots are used. Simulation and experimental results are shown
in Tabular form (Tables 1-2) and pictorial form (Figure 2). The
simulation and experimental results agree with each other and
deviations between them are found to be within 2.8%.
In the current research Genetic-Neural hybrid control
technique has been used to navigate mobile robots from start
points to goal points. In the process steering angles SA-1 and
SA-2 are obtained from Genetic and Neural parts of the hybrid
algorithm. The FSA used for navigation is calculated by averaging
SA-1 and SA-2 values. Using the Genetic-Neural hybrid control
technique several exercises are conducted in simulation and
experimental modes. The deviations between simulation and
experimental results are found to be within 2.8%. For carrying
out the simulations and experiments exercises Khepera-II and
Hemisson robots are used. From simulation and experimental
results as depicted in tabular and pictorial forms show the
efficiency of the proposed technique during navigation. Genetic-
Neural hybrid control technique can be used for solving other
engineering problems as an AI technique where there is a need
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