Optimal Path Planning in Environments with Static Obstacles by Harmony Search Algorithm

Path planning represents an important optimization problem that need to be solved in various applications. It is a hard optimization problem thus deterministic algorithms are not usable but it can be tackled by stochastic population based metaheuristics such as swarm intelligence algorithms. In this paper we adopted and adjusted harmony search algorithm for the path planning problem in environment with static obstacles and danger zones. Objective function includes path length and safety degree. The proposed method was tested on standard benchmark examples from literature. Simulation results show that our proposed model produces better and more consistent results in spite of its simplicity.

[1]  Davide Scaramuzza,et al.  Active Autonomous Aerial Exploration for Ground Robot Path Planning , 2017, IEEE Robotics and Automation Letters.

[2]  Yang Liu,et al.  Adaptive sensitivity decision based path planning algorithm for unmanned aerial vehicle with improved particle swarm optimization , 2016 .

[3]  Huaping Liu,et al.  An improved ant colony algorithm for robot path planning , 2017, Soft Comput..

[4]  Marko Beko,et al.  Support Vector Machine Parameters Optimization by Enhanced Fireworks Algorithm , 2016, ICSI.

[5]  Junfeng Chen,et al.  Enhanced Brain Storm Optimization Algorithm for Wireless Sensor Networks Deployment , 2016, ICSI.

[6]  Yahui Yu,et al.  The Exponential Diophantine Equation 2x + b y = c z , 2014, TheScientificWorldJournal.

[7]  Marco A. Contreras-Cruz,et al.  Mobile robot path planning using artificial bee colony and evolutionary programming , 2015, Appl. Soft Comput..

[8]  Wang Juan,et al.  An improved path planning approach based on Particle Swarm Optimization , 2011, 2011 11th International Conference on Hybrid Intelligent Systems (HIS).

[9]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[10]  Muhammad Usman Rafique,et al.  Mobile robot path planning in environments cluttered with non-convex obstacles using particle swarm optimization , 2015, 2015 International Conference on Control, Automation and Robotics.

[11]  M Dorigo,et al.  Ant colonies for the travelling salesman problem. , 1997, Bio Systems.

[12]  Milan Tuba,et al.  Unmanned Combat Aerial Vehicle Path Planning by Brain Storm Optimization Algorithm , 2018 .

[13]  Bai Li,et al.  An Improved Artificial Bee Colony Algorithm Based on Balance-Evolution Strategy for Unmanned Combat Aerial Vehicle Path Planning , 2014, TheScientificWorldJournal.

[14]  Milan Tuba,et al.  Adjusted Fireworks Algorithm Applied to Retinal Image Registration , 2017 .

[15]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[16]  Marko Beko,et al.  Node localization in ad hoc wireless sensor networks using fireworks algorithm , 2016, 2016 5th International Conference on Multimedia Computing and Systems (ICMCS).

[17]  Milan Tuba,et al.  JPEG quantization tables selection by the firefly algorithm , 2014, 2014 International Conference on Multimedia Computing and Systems (ICMCS).

[18]  M. U. Khan,et al.  Mobile Robot Path Planning in Static Environments using Particle Swarm Optimization , 2020, ArXiv.

[19]  Milan Tuba,et al.  JPEG Quantization Table Optimization by Guided Fireworks Algorithm , 2017, IWCIA.

[20]  Milan Tuba,et al.  Handwritten digit recognition by support vector machine optimized by Bat algorithm , 2016 .

[21]  Xin-She Yang Harmony Search as a Metaheuristic Algorithm , 2009 .