Multi-Robot Navigation in Unknown Environment Using Strawberry Algorithm

Multi-Robot Navigation in Unknown Environment Using Strawberry Algorithm

B. Sai Charan, Ayush Mittal, Ritu Tiwari
Copyright: © 2020 |Pages: 22
DOI: 10.4018/978-1-7998-1754-3.ch055
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Abstract

Path Planning focuses on the robot motion from the initial position to final position such that it must avoid the hurdles and finally reach the goal in optimal path. But it is not an easy task because many conditions are included for the efficiency of final result like working on different environments, known or unknown target etc. In this paper the authors have proposed an algorithm inspired by the strawberry plants, and is applied in the path planning. The algorithm can efficiently work for different optimization parameters like Path Length, Energy and Number of Turns. The proposed algorithm is compared with RRT, A-star, PSO and the results obtained are satisfactory. The work can be applied in the real life challenges faced during area exploration
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Li Lu et al. (2008) (Lu & Gong, 2008), proposed method for path planning in the unknown environment using the particle swarm optimization, this approach transformed to minimization concept. The advanced fitness function is based on the target and also obstacles in the search space or environment. The environment is unknown because of the limited sensor range of the robot. The global best solution calculated by executing PSO iteratively. The robot updates the environment on its each move. The final path is generated using the fitness function, and also simulation is done in the dynamic environment so that final path generated do not collide with obstacles.

Amin Zarger et al. (2009) (Nasrollahy & Javadi, 2009), introduced a method assuming the goal position is moving according to the time, and also obstacles are not static. Particles swarm optimization used to find collision free path and concept used for fitness function development is minimization concept. This method is applied irrespective of shape and size of objects. Ellipsmasehian et al. (Masehian & Sedighizadeh, 2010), proposed a method which introduces a concept that is particle swarm optimization hybrid with the probabilistic roadmap. The PSO for choosing the global best and the probabilistic roadmap for avoiding the obstacles. In this method, two objective functions developed which minimizes the path length and path smoothness such that robot reaches its goal position.

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