Collision-free autonomous robot navigation in unknown environments utilizing PSO for path planning

• Autorzy: Krell Evan , Sheta Alaa , Balasubramanian Arun Prassanth Ramaswamy , King Scott A.

Identyfikatory

DOI

10.2478/jaiscr-2019-0008

• Języki publikacji: EN

Abstrakty

EN

The autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.

Słowa kluczowe

EN

mobile robot; particle swarm optimization (PSO); path planning

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2019
• Tom: Vol. 9, No. 4
• Strony: 267--282
• Opis fizyczny: Bibliogr. 28 poz., rys.

Twórcy

autor

Krell Evan

• Department of Computing Sciences, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA

autor

Sheta Alaa

• Department of Computing Sciences, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA

autor

Balasubramanian Arun Prassanth Ramaswamy

• Department of Computing Sciences, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA

autor

King Scott A.

• Department of Computing Sciences, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA

Bibliografia

• [1] A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning, 28.
• [2] M Shahab Alam, M Usman Rafique, and M Umer Khan. Mobile robot path planning in static environments using particle swarm optimization. International Journal of Computer Science and Electronics Engineering (IJCSEE), 3(3):253–257, 2015.
• [3] Dora-Luz Almanza-Ojeda, Yazmin Gomar-Vera, and Mario Ibarra-Manzano. Occupancy Map Construction for Indoor Robot Navigation. 10 2016.
• [4] Ismail Altaharwa, Alaa Sheta, and Mohammed Alweshah. A mobile robot path planning using genetic algorithm in static environment. Journal of Computer Science, 4, 01 2008.
• [5] J. C. Bansal, P. K. Singh, M. Saraswat, A. Verma, S. S. Jadon, and A. Abraham. Inertia weight strategies in particle swarm optimization. In 2011 Third World Congress on Nature and Biologically Inspired Computing, pages 633–640, Oct 2011.
• [6] Jan Bacık, Frantisek Durovsky, Milan Biros, Karol Kyslan, Daniela Perdukova, and P Sanjeevikumar. Pathfinder – development of automated guided vehicle for hospital logistics. IEEE Access, 5:26892 – 26900, 10 2017.
• [7] Sumana Biswas, Sreenatha G. Anavatti, and Matthew A. Garratt. Obstacle avoidance for multiagent path planning based on vectorized particle swarm optimization. In George Leu, Hemant Kumar Singh, and Saber Elsayed, editors, Intelligent and Evolutionary Systems, pages 61–74, Cham, 2017. . Springer International Publishing
• [8] E. A. S. Carballo, L. Morales, and F. TrujilloRomero. Path planning for a mobile robot using genetic algorithm and artificial bee colony. In 2017 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE), pages 8–12, Nov 2017.
• [9] Xin Chen and Yangmin Li. Smooth Path Planning of a Mobile Robot Using Stochastic Particle Swarm Optimization. In 2006 International Conference on Mechatronics and Automation, pages 1722–1727, Luoyang, June 2006. IEEE.
• [10] R. Craig Coulter. Implementation of the pure pursuit path tracking algorithm. Technical Report CMU-RI-TR-92-01, Carnegie Mellon University, Pittsburgh, PA, January 1992.
• [11] Stan Franklin and Art Graesser. Is it an agent, or just a program?: A taxonomy for autonomous agents. In Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages, ECAI ’96, pages 21–35, London, UK, UK, 1997. Springer-Verlag.
• [12] Avinash Gautam and Sudeept Mohan. A review of research in multi-robot systems. 08 2012.
• [13] C. Georgoulas, T. Linner, A. Kasatkin, and T. Bock. An ami environment implementation: Embedding turtlebot into a novel robotic service wall.In ROBOTIK 2012; 7th German Conference on Robotics, pages 1–6, May 2012.
• [14] S. Ghosh, P. K. Panigrahi, and D. R. Parhi. Analysis of fpa and ba meta-heuristic controllers for optimal path planning of mobile robot in cluttered environment. IET Science, Measurement Technology, 11(7):817–828, 2017.
• [15] Suhanya Jayaprakasam, Sharul Kamal Abdul Rahim, and Chee Yen Leow. Psogsa-explore: A new hybrid metaheuristic approach for beampattern optimization in collaborative beamforming. Applied Soft Computing, 30:229–237, 2015.
• [16] Kyriakos Kentzoglanakis. Particle swarm optimization in c. https://github.com/kkentzo/pso, 2017.
• [17] N. Koenig and A. Howard. Design and use paradigms for gazebo, an open-source multi-robot simulator. In 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566), volume 3, pages 2149–2154 vol.3, Sept 2004.
• [18] E. Masehian and D. Sedighizadeh. Multi-Objective PSO- and NPSO-based Algorithms for Robot Path Planning. Advances in Electrical and Computer Engineering, 10(4):69–76, 2010.
• [19] Amin Zargar Nasrollahy and Hamid Haj Seyyed Javadi. Using Particle Swarm Optimization for Robot Path Planning in Dynamic Environments with Moving Obstacles and Target. In 2009 Third UKSim European Symposium on Computer Modeling and Simulation, pages 60–65, Athens, Greece, 2009. IEEE.
• [20] Millie Pant, Radha Thangaraj, and Ajith Abraham. Particle swarm optimization: performance tuning and empirical analysis. Foundations of Computational Intelligence, 3:101–128, 2009.
• [21] P. Raja and S. Pugazhenthi. Path Planning for Mobile Robots in Dynamic Environments Using Particle Swarm Optimization. In 2009 International Conference on Advances in Recent Technologies in Communication and Computing, pages 401–405, Kottayam, Kerala, India, 2009. IEEE.
• [22] P Raja and S Pugazhenthi. Optimal path planning of mobile robots: A review. International Journal of the Physical Sciences, 7:1314–1320, 02 2012.
• [23] L. Scharf, W. Harthill, and P. Moose. A comparison of expected flight times for intercept and pure pursuit missiles. IEEE Transactions on Aerospace and Electronic Systems, 4:672–673, 1969.
• [24] K. H. Sedighi, K. Ashenayi, T. W. Manikas, R. L. Wainwright, and Heng-Ming Tai. Autonomous local path planning for a mobile robot using a genetic algorithm. In Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), volume 2, pages 1338–1345 Vol.2, June 2004.
• [25] Dmitri Sokolov. tinyrenderer, 2017. [26] Monica Anderson LaPoint Maria Gini Nikolaos Papanikolopoulos John Budenske Steven Damer, Luke Ludwig. Dispersion and exploration algorithms for robots in unknown environments. Volume 6230, 2006.
• [27] Girma S Tewolde, Darrin M Hanna, and Richard E Haskell. Enhancing performance of pso with automatic parameter tuning technique. In 2009 IEEE Swarm Intelligence Symposium, pages 67–73. IEEE, 2009.
• [28] Gerhard Venter and Jaroslaw SobieszczanskiSobieski. Particle swarm optimization. AIAA journal, 41(8):1583–1589, 2003.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).