A survey on path planning algorithms for unmanned aerial vehicles using bio inspired optimization techniques
Bio-inspired Swarm Robotics and Control: Algorithms, Mechanisms, and Strategies, ISSN: 2327-0411, Page: 16-27
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Book Chapter Description
The rapid development of unmanned aerial vehicles (UAVs) and UAV-based applications has been increased in the recent past due to the advancement in software and electronics industry. Use of UAVs are considered to be a very efficient and useful platform that can deeply monitor the critical infrastructures around the geographical areas. UAVs are also useful for data collection through different wireless sensor networks. Based on the collected data, an optimal path can be formed. Bio-inspired algorithms are inspired from the principles of the biological evolution of nature. The recent trends tend to employ the bio-inspired optimization techniques that are best-suitable for handling strenuous optimization problems. In this chapter, the authors investigate different bio-inspired algorithms for the UAV path planning over the last decade. They compared the working principles, key features, advancements, and limitations of different path planning algorithms. Furthermore, the challenges and future research scopes are also discussed and summarized.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85193626084&origin=inward; http://dx.doi.org/10.4018/979-8-3693-1277-3.ch002; https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-1277-3.ch002; https://dx.doi.org/10.4018/979-8-3693-1277-3.ch002; https://www.igi-global.com/gateway/chapter/345305
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