A Survey on Path Planning Algorithms for Unmanned Aerial Vehicles Using Bio-Inspired Optimization Techniques

A Survey on Path Planning Algorithms for Unmanned Aerial Vehicles Using Bio-Inspired Optimization Techniques

Arpan Adhikary, Dipanwita Ghosh, Sima Das, Asit Kumar Nayek, Rabindranath Sahu
Copyright: © 2024 |Pages: 12
DOI: 10.4018/979-8-3693-1277-3.ch002
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Abstract

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.
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Li, J. et al. (2018) explored the application of bio-inspired optimization techniques, such as Genetic Algorithms and Particle Swarm Optimization, for UAV path planning in dynamically changing environments. The research emphasizes adaptability and real-time decision-making capabilities crucial for UAV navigation.

Gupta, S., et al. (2017) conducted a comparative analysis of Ant Colony Optimization techniques applied to UAV route planning. The study delves into the efficiency of ant-inspired algorithms in optimizing routes for unmanned aerial vehicles, particularly in scenarios with varying mission constraints.

Chen, H. et al. (2009) investigated the application of evolutionary strategies, including Genetic Algorithms and Evolutionary Programming, for optimizing UAV trajectories. The study highlights the role of evolutionary approaches in achieving optimal and adaptive paths for UAVs.

Kim, Y., et al. (2019) focused on leveraging swarm intelligence for cooperative path planning among UAVs. The research explores how swarm-based algorithms, inspired by natural behaviors, enhance collaboration and coordination among multiple unmanned aerial vehicles.

Singh, A. et al. (2023) examined the effectiveness of hybrid bio-inspired approaches, combining multiple optimization techniques, for UAV navigation in challenging environments. The study investigates the synergies of different algorithms to address complexities such as obstacles and varying terrains.

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