Mobile Robot Navigation in Cluttered Environment Using Spider Monkey Optimization Algorithm
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RESEARCH PAPER
Mobile Robot Navigation in Cluttered Environment Using Spider Monkey Optimization Algorithm Ngangbam Herojit Singh1
•
Khelchandra Thongam2
Received: 8 October 2018 / Accepted: 27 January 2020 Ó Shiraz University 2020
Abstract Swarm intelligence is one of the most emerging methods used for autonomous mobile robot navigation. Researchers have developed many algorithms in mobile robot navigation by simulating the swarming behavior of various creatures like ants, firefly, honey bees, and cuckoo, and the findings are very motivating. The paper presents application and implementation of spider monkey optimization (SMO) along with Three Path method (TPM) for mobile robot navigation in cluttered environment. A collision-free path is selected by using TPM. When all the three paths are blocked by obstacles, SMO is used for obstacles avoidance. The finding of global and local leaders from the groups is the key concept of the proposed method. The proposed method efficiently improves the global search in less number of iterations, and hence, it can be easily implemented for real-time obstacle avoidance. The computational path and time are less as compared to other navigational methods. Some simulation results are presented at the end of the paper to show the effectiveness of the proposed navigational method. Keywords Swarm intelligence Spider monkey optimization Mobile robot navigation Obstacle avoidance
1 Introduction Over the last decades, mobile robot navigation is one of the most important challenging topics in robotics research. Nowadays, human work loads are replaced by robot. It needs to navigate from source to the target point. For optimal path navigation, it is necessary to use efficient navigation techniques. Many researchers have developed different navigation techniques like fuzzy logic, genetic algorithm, neural networks, potential field method, particle swarm optimization (PSO), others optimization and hybrids methods. A neuro-fuzzy rule-based optimization technique is studied in AbuBaker (2012), to control the autonomous mobile robot that moved along a collision-free trajectory
& Ngangbam Herojit Singh [email protected] Khelchandra Thongam [email protected] 1
National Institute of Technology Mizoram, Aizawl, India
2
National Institute of Technology Manipur, Imphal, India
until it reaches its destination. The neural network effectively chooses the optimum number of activation rules in order to reduce the computational time. But the computational time is not so reduce as well as the path length of the mobile robot is not optimized. In Zhang et al. (2013), different algorithms are proposed to allow the mobile node to navigate without the help of a map,GPS, or extra sensor devices, only using the (received signal strength indication) RSSI and odometry information. A cuckoo search algorithm is applied in Mohanty and Parhi (2016) for finding the optimal path planning for a mobile robot in an unknown environment or partially known environment populated with numbers of static obstacl
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