Evolutionary Approach for Mobile Robot Path Planning in Complex environment
The shortest/optimal path planning in a static environment
is essential for the efficient operation of a mobile robot.
Recent advances in robotics and machine intelligence have
led to the application of modern optimization method such
as the genetic algorithm (GA), to solve the path-planning
problem. In this paper, the problem of finding the optimal
collision free path in complex environments for a mobile
robot is solved using a hybrid neural network, Genetic
Algorithm and local Search method. We constructed the
neural network model of environmental and used this
model to establish the relationship between a collision
avoidance path and the output of the model. What is new
in this work is a novel representation of solutions for
evolutionary algorithms that is efficient, simple and also
compatible with Hybrid algorithm. The new representation
makes it possible to solve the problem with a small
population and in a few generations. It also makes the
genetic operator simple and allows using an efficient local
search operator within the evolutionary algorithm. The
performance of the proposed GA approach is tested on
eight different environments consisting of polygonal
obstacles with increasing complexity.
Keywords: Genetic Algorithm, Robot Path Planning,
Neural Network, Local Search
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