A Novel Improved Bat Algorithm in UAV Path Planning

Path planning algorithm is the key point to UAV path planning scenario. Many traditional path planning methods still suffer from low convergence rate and insufficient robustness. In this paper, three main methods are contributed to solving these problems. First, the improved artificial potential field (APF) method is adopted to accelerate the convergence process of the bat’s position update. Second, the optimal success rate strategy is proposed to improve the adaptive inertia weight of bat algorithm. Third chaos strategy is proposed to avoid falling into a local optimum. Compared with standard APF and chaos strategy in UAV path planning scenarios, the improved algorithm CPFIBA (The improved artificial potential field method combined with chaotic bat algorithm, CPFIBA) significantly increases the success rate of finding suitable planning path and decrease the convergence time. Simulation results show that the proposed algorithm also has great robustness for processing with path planning problems. Meanwhile, it overcomes the shortcomings of the traditional meta-heuristic algorithms, as their convergence process is the potential to fall into a local optimum. From the simulation, we can see also obverse that the proposed CPFIBA provides better performance than BA and DEBA in problems of UAV path planning.

[1]  Qiang Qiu,et al.  Ubiquitous Robotic Technology for Smart Manufacturing System , 2016, Comput. Intell. Neurosci..

[2]  Oktay Baysal,et al.  Path planning for autonomous UAV via vibrational genetic algorithm , 2007 .

[3]  David Anderson,et al.  Reactive route selection from pre-calculated trajectories – application to micro-UAV path planning , 2011, The Aeronautical Journal (1968).

[4]  Kai Wang,et al.  A Novel Teaching-Learning-Based Optimization with Error Correction and Cauchy Distribution for Path Planning of Unmanned Air Vehicle , 2018, Comput. Intell. Neurosci..

[5]  Sue Ellen Haupt,et al.  UAV navigation by an expert system for contaminant mapping with a genetic algorithm , 2010, Expert Syst. Appl..

[6]  Anzar Mahmood,et al.  Three-Dimensional UAV Routing With Deconfliction , 2018, IEEE Access.

[7]  Dayal R. Parhi,et al.  Analysis of FPA and BA meta-heuristic controllers for optimal path planning of mobile robot in cluttered environment , 2017 .

[8]  Yuhui Shi,et al.  ?Hybrid Particle Swarm Optimization and Genetic Algorithm for Multi-UAV Formation Reconfiguration , 2013, IEEE Computational Intelligence Magazine.

[9]  Seyedali Mirjalili,et al.  Three-dimensional path planning for UCAV using an improved bat algorithm , 2016 .

[10]  Philippe Martinet,et al.  Vision-based navigation of unmanned aerial vehicles , 2010 .

[11]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[12]  Albert Y. Zomaya,et al.  QoS-Aware Inter-Domain Multicast for Scalable Wireless Community Networks , 2015, IEEE Transactions on Parallel and Distributed Systems.

[13]  Ganesh K. Venayagamoorthy,et al.  Bio-inspired Algorithms for Autonomous Deployment and Localization of Sensor Nodes , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[14]  Xin-She Yang,et al.  Economic dispatch using chaotic bat algorithm , 2016 .

[15]  José Luis Risco-Martín,et al.  On the performance comparison of multi-objective evolutionary UAV path planners , 2013, Inf. Sci..

[16]  Shengming Li,et al.  Path Planning of UAVs Based on Collision Probability and Kalman Filter , 2018, IEEE Access.

[17]  Kimon P. Valavanis,et al.  Evolutionary algorithm based offline/online path planner for UAV navigation , 2003, IEEE Trans. Syst. Man Cybern. Part B.

[18]  Yasar Ayaz,et al.  Potentially Guided Bidirectionalized RRT* for Fast Optimal Path Planning in Cluttered Environments , 2018, Robotics Auton. Syst..

[19]  Sebastian Magierowski,et al.  Vehicle Routing Problems for Drone Delivery , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[20]  K. N. Seetharamu,et al.  Multi-objective optimization using bat algorithm for shell and tube heat exchangers , 2017 .

[21]  Xin-She Yang,et al.  New directional bat algorithm for continuous optimization problems , 2017, Expert Syst. Appl..

[22]  Wei Pan,et al.  Grey wolf optimizer for unmanned combat aerial vehicle path planning , 2016, Adv. Eng. Softw..

[23]  Mingyue Ding,et al.  Route Planning for Unmanned Aerial Vehicle (UAV) on the Sea Using Hybrid Differential Evolution and Quantum-Behaved Particle Swarm Optimization , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[24]  Huijun Gao,et al.  Evolutionary Pinning Control and Its Application in UAV Coordination , 2012, IEEE Transactions on Industrial Informatics.

[25]  Debasish Ghose,et al.  Reducing convergence times of self-propelled swarms via modified nearest neighbor rules , 2012 .

[26]  Li-Der Chou,et al.  A Star Search Algorithm for Civil UAV Path Planning with 3G Communication , 2014, 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[27]  Hao Jiang,et al.  Online Path Planning of Autonomous UAVs for Bearing-Only Standoff Multi-Target Following in Threat Environment , 2018, IEEE Access.

[28]  Zuren Feng,et al.  A GA-ACO hybrid algorithm for the multi-UAV mission planning problem , 2014, 2014 14th International Symposium on Communications and Information Technologies (ISCIT).

[29]  D. Alejo,et al.  Collision-Free 4D Trajectory Planning in Unmanned Aerial Vehicles for Assembly and Structure Construction , 2014, J. Intell. Robotic Syst..

[30]  Gaige Wang,et al.  A Bat Algorithm with Mutation for UCAV Path Planning , 2012, TheScientificWorldJournal.

[31]  Zhiwei Ye,et al.  A “Tuned” Mask Learnt Approach Based on Gravitational Search Algorithm , 2016, Comput. Intell. Neurosci..

[32]  Fanlei Yan.  Autonomous vehicle routing problem solution based on artificial potential field with parallel ant colony optimization (ACO) algorithm , 2018, Pattern Recognit. Lett..

[33]  Gang Wang,et al.  Stability-Capacity-Adaptive Routing for High-Mobility Multihop Cognitive Radio Networks , 2011, IEEE Transactions on Vehicular Technology.

[34]  Antonios Tsourdos,et al.  Co-operative path planning of multiple UAVs using Dubins paths with clothoid arcs , 2010 .

[35]  O. Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[36]  Kuo-Chu Chang,et al.  UAV Path Planning with Tangent-plus-Lyapunov Vector Field Guidance and Obstacle Avoidance , 2013, IEEE Transactions on Aerospace and Electronic Systems.

[37]  Aníbal Ollero,et al.  Conflict Detection and Resolution Method for Cooperating Unmanned Aerial Vehicles , 2012, J. Intell. Robotic Syst..

[38]  Yang Liu,et al.  Survey on computational-intelligence-based UAV path planning , 2018, Knowl. Based Syst..

[39]  Timothy W. McLain,et al.  Real-time dynamic trajectory smoothing for unmanned air vehicles , 2005, IEEE Transactions on Control Systems Technology.

[40]  Yu Yao,et al.  Chaotic Artificial Bee Colony Algorithm for System Identification of a Small-Scale Unmanned Helicopter , 2015 .

[41]  Xin-She Yang,et al.  An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems , 2016, Eng. Appl. Artif. Intell..