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A Hybrid Fireworks Algorithm to Navigation and Mapping

A Hybrid Fireworks Algorithm to Navigation and Mapping

Tingjun Lei, Chaomin Luo, John E. Ball, Zhuming Bi
Copyright: © 2020 |Pages: 20
ISBN13: 9781799816591|ISBN10: 1799816591|EISBN13: 9781799816607
DOI: 10.4018/978-1-7998-1659-1.ch010
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MLA

Lei, Tingjun, et al. "A Hybrid Fireworks Algorithm to Navigation and Mapping." Handbook of Research on Fireworks Algorithms and Swarm Intelligence, edited by Ying Tan, IGI Global, 2020, pp. 213-232. https://doi.org/10.4018/978-1-7998-1659-1.ch010

APA

Lei, T., Luo, C., Ball, J. E., & Bi, Z. (2020). A Hybrid Fireworks Algorithm to Navigation and Mapping. In Y. Tan (Ed.), Handbook of Research on Fireworks Algorithms and Swarm Intelligence (pp. 213-232). IGI Global. https://doi.org/10.4018/978-1-7998-1659-1.ch010

Chicago

Lei, Tingjun, et al. "A Hybrid Fireworks Algorithm to Navigation and Mapping." In Handbook of Research on Fireworks Algorithms and Swarm Intelligence, edited by Ying Tan, 213-232. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1659-1.ch010

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Abstract

In recent years, computer technology and artificial intelligence have developed rapidly, and research in the field of mobile robots has continued to deepen with development of artificial intelligence. Path planning is an essential content of mobile robot navigation of computing a collision-free path between a starting point and a goal. It is necessary for mobile robots to move and maneuver in different kinds of environment with objects and obstacles. The main goal of path planning is to find the optimal path between the starting point and the target position in the minimal possible time. A new firework algorithm (FWA) integrated with a graph theory, Dijkstra's algorithm developed for autonomous robot navigation, is proposed in this chapter. The firework algorithm is improved by a local search procedure that a LIDAR-based local navigator algorithm is implemented for local navigation and obstacle avoidance. The grid map is utilized for real-time intelligent robot mapping and navigation. In this chapter, both simulation and comparison studies of an autonomous robot navigation demonstrate that the proposed model is capable of planning more reasonable and shorter, collision-free paths in non-stationary and unstructured environments compared with other approaches.

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