Penetration Planning and Design Method of Unmanned Aerial Vehicle Inspired by Biological Swarm Intelligence Algorithm
Open Access
- 31 December 2021
- journal article
- research article
- Published by Hindawi Limited in Wireless Communications and Mobile Computing
- Vol. 2021, 1-13
- https://doi.org/10.1155/2021/4312592
Abstract
Unmanned aerial vehicles (UAVs) are gradually used in logistics transportation. They are forbidden to fly in some airspace. To ensure the safety of UAVs, reasonable path planning and design is one of the key factors. Aiming at the problem of how to improve the success rate of unmanned aerial vehicle (UAV) maneuver penetration, a method of UAV penetration path planning and design is proposed. Ant colony algorithm has strong path planning ability in biological swarm intelligence algorithm. Based on the modeling of UAV planning and threat factors, improved ant colony algorithm is used for UAV penetration path planning and design. It is proposed that the path with the best pheromone content is used as the planning path. Some principles are given for using ant colony algorithm in UAV penetration path planning. By introducing heuristic information into the improved ant colony algorithm, the convergence is completed faster under the same number of iteratives. Compared with classical methods, the total steps reduced by 56 with 50 ant numbers and 200 iterations. 62 fewer steps to complete the first iteration. It is found that the optimal trajectory planned by the improved ant colony algorithm is smoother and the shortest path satisfying the constraints.Keywords
Funding Information
- Natural Science Foundation of Hunan Province (2019JJ50724, 2021JJ40693, 61806212, U1734208)
This publication has 33 references indexed in Scilit:
- A novel path planning algorithm based on plant growth mechanismSoft Computing, 2016
- UAV feasible path planning based on disturbed fluid and trajectory propagationChinese Journal of Aeronautics, 2015
- Route planning of stacker by improved genetic algorithmPublished by Institution of Engineering and Technology (IET) ,2012
- Three-dimensional multi-constraint route planning of unmanned aerial vehicle low-altitude penetration based on coevolutionary multi-agent genetic algorithmJournal of Central South University, 2011
- Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planningAerospace Science and Technology, 2010
- Highly Constrained Optimal Launch Ascent GuidanceJournal of Guidance, Control, and Dynamics, 2010
- Probabilistic planning with clear preferences on missing informationArtificial Intelligence, 2009
- Robust algorithm for real-time route planningIEEE Transactions on Aerospace and Electronic Systems, 2000
- Ant colony system: a cooperative learning approach to the traveling salesman problemIEEE Transactions on Evolutionary Computation, 1997
- Ant system: optimization by a colony of cooperating agentsIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996