An intelligent navigational strategy for mobile robots in uncertain environments using smart cuckoo search algorithm
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ORIGINAL RESEARCH
An intelligent navigational strategy for mobile robots in uncertain environments using smart cuckoo search algorithm Prases K. Mohanty1 Received: 11 March 2020 / Accepted: 5 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This paper presents the implementation of smart cuckoo search (SCS) algorithm for intelligent path planning of mobile robots. A new fitness function is modeled and optimized by SCS algorithm to generate collision free optimal route for the mobile robots. The simulation results are illustrated to verify the ability of robot to deal with different environment conditions and reach to the target in all the time. Also the results obtained using SCS algorithm is compared with results of Adaptive Particle Swarm Optimization (APSO). It is noticed that SCS algorithm showed better results as compared to APSO. Finally the simulation platform results are validated with Khepera-IV mobile robot experimental results and it is revealed that proposed algorithm is valid and feasible in the mobile robot path planning problems. Keywords Cuckoo search · Path planning · Mobile robot · Obstacle avoidance · PSO · Khepera robot
1 Introduction Nowadays mobile robots are getting more attention in robotic research field due to its versatile nature for obstacle avoidance in cluttered environment. We can find many applications of mobile robot in industries, medical, office, agriculture, military operation, space exploration and many more. The most challenging task with the mobile robot is solving the path planning problem. The path planning problem is classified broadly into (1) offline robot path planning and (2) online robot path planning. When robots have previous information about the environment is called as offline path planning and in other side when the environment information completely unknown to the robot is treated as online path planning. Therefore proper path planning is necessary for solving the robot navigation problem along with obstacle avoidance in optimize way. Many literatures are available about the path planning methods of the robot (Campbell et al. 2020; Patle et al. 2019; Pandey et al. 2017; Mohanty and Parhi 2013a, b). But classical computing methods (Mac et al. 2016) are more in complex to find the solution for solving the path planning problem and also sometimes the robot may get trapped in * Prases K. Mohanty [email protected] 1
Department of Mechanical Engineering, National Institute of Technology, Yupia, Arunachal Pradesh 791112, India
the local minima position. Therefore in recent years many computational intelligence based algorithms are used by researchers for solving the path planning problems of the robots. Many deterministic approaches have been attempted to solve the navigational problems of mobile robots such as using fuzzy logic, neural network and neuro fuzzy. Seraji and Howard (2002) proposed a behavior based navigation strategy using fuzzy logic to solve the robot path planning problem. Robot path planning in unknown te
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