Total views : 371

Trajectory Planning of a Mobile Robot using Enhanced A-Star Algorithm

Affiliations

  • School of Computing, SRM University, Kancheepuram – 603203, Tamil Nadu, India

Abstract


Objectives: This paper presents an optimization technique for a robot to identify the best path for traversing from a start state to a goal state without colliding with any obstacles. Methods: The existing A-star algorithm is modified to make the robot to move in an unknown scenario in which static obstacles are located. The robot is expected to travel to the goal position without colliding with any of the obstacles present. The Enhanced A* algorithm guides the robot to reach the target following an optimal path. By adding a new parameter namely number of turnings (p(n)) the robot makes during its traverse, currently existing A* algorithm is improved. This is done in order to improve the algorithm for optimal motion of the robot. Findings: The proposed Enhanced A* algorithm will be shown to remove certain drawbacks, which are found in the currently existing algorithms, for optimal travel of the robot. Simulation experiments are carried out to compare the performances of the proposed Enhanced A* algorithm and the original A* algorithm. The outcome of the simulation experiments is expected to provide better performance as compared to the A* algorithm concerning Elapsed time of travel. It is expected that the Enhanced A* algorithm is able to provide an effective contrivance for exploring path planning in robotics education and path control of robot. Improvements: The Future Enhancement is to make the algorithm work in an unknown environment with dynamic obstacles present and implement the same in real time using an autonomous mobile robot.

Keywords

A* Algorithm, Mobile Robot, Machine Learning, Path Planning, Static Obstacles, Unknown Environment.

Full Text:

 |  (PDF views: 445)

References


  • Frank B, Becker M, Stachniss C, Burgard W, Teschner M. Efficient path planning for mobile robots in environments with deformable objects. IEEE International Conference on Robotics and Automation; Pasadena, CA. 2008. p. 3737-42.
  • Buniyamin N, Ngah WAJW, Sariff NZ, Mohamad A. Simple local path planning algorithm for autonomous mobile robots. International Journal of Systems Applications, Engineering and Development. 2011; 5(2):151-9.
  • Kolushev FA, Bogdanov AA. Multi-agent optimal path planning for mobile robots in environment with obstacles. LNCS: PSI’99; Berlin Heidelberg: Springer-Verlag; 2000; 17(55):503-10.
  • Taharwa IA, Sheta A, Weshah MA. A mobile robot path planning using genetic algorithm in static environment. Journal of Computer Science. 2008; 4(1):341-4.
  • Garrido S, Moreno L, Blanco D, Jurewicz P. Path planning for mobile robot navigation using voronoi diagram and fast marching. IJRA. 2011:42-64.
  • Shojaeipour S, Haris SM, Gholami E, Shojaeipour A. Webcam-based mobile robot path planning using voronoi diagrams and image processing. WSEAS International Conference on Applications of Electrical Engineering; Malaysia. 2010. p. 151-6.
  • Ersson T, Hu X. Path planning and navigation of mobile robots in unknown environments. IEEE/RSJ International Conference onIntelligent Robots and Systems; Maui, HI. 2001. p. 785-864.
  • Ganapathy V, Yun SC, Chien TW. Enhanced D star lite algorithm for autonomous mobile robot. International Journal of Applied Science and Technology. 2011:58-73.
  • Ganapathy V, Yun SCJNG. Fuzzy and neural controllers for acute obstacle avoidance in mobile robot navigation. IEEE/ASME International Conference on Advanced Intelligent Mechatronics Suntec Convention and Exhibition Center; Singapore. 2009. p. 14-7.
  • Konar A, Chakraborty IG, Singh SJ, Jain LC, Nagar AK. A deterministic improved q-learning for path planning of a mobile robot. IEEE Transactions on Systems, Man and Cybernetics: Systems. 2013; 43(5):1141–53.
  • Tsai CC, Huang HC, Chan CK. Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation. IEEE Transactions on Industrial Electronics. 2011; 58(10):4813–21.
  • Juang CF, Chang YC. Evolutionary-group-based particle-swarm-optimized fuzzy controller with application to mobile-robot navigation in unknown environments. IEEE Transactions on Fuzzy Systems. 2011; 19(2):379–92.
  • Jan GE, Sun CC, Tsai WC, Lin TH. An O(n log n) shortest path algorithm based on delaunay triangulation. IEEE/ASME Transactions on Mechatronics. 2014; 19(2):660- 6.
  • Tamilselvi D, Shalinie SM, Hariharasudan M. Hybrid approach for global path selection and dynamic obstacle avoidance for mobile robot navigation. Advances in Robot Navigation. Mexico: INTECH Open Access Publisher; 2011.
  • Shoushtary MA, Nasab HH, Fakhrzad. Team robot motion planning in dynamics environments using a new hybrid algorithm (honey bee mating optimization-tabu list). Chinese Journal of Engineering. 2014: 1-8.
  • Abdallan TY, Hamzah MI. Trajectory tracking control for mobile robot using wavelet network. International Journal of Computer Applications. 2013; 74(3):32-7.
  • Zhao J, Fu X. Improved ant colony optimization algorithm and its application on path planning of mobile robot. Journal of Computers. 2012; 7(2):2055-62.
  • Cosic A, Susic M, Katic D. Advanced algorithms for mobile robot motion planning and tracking in structured static environments using particle swarm optimization. Serbian Journal of Electrical Engineering. 2012; 9(1):9-28.
  • Das PK, Mandhata SC, Behera HS, Patro SN. An improved Q-learning algorithm for path-planning of a mobile robot. International Journal of Computer Applications. 2012; 51(9):40-6.
  • Ma Z, Ning X. The path planning algorithm and simulation for mobile robot. Journal of Theoretical and Applied Information Technology. 2013; 50(3):601-5.
  • Suresh KS, Vaithiyanathan V, Venugopal S. Layered approach for three dimensional collision free robot path planning using genetic algorithm. Indian Journal of Science and Technology. 2015 Dec; 8(35). Doi no:10.17485/ijst/2015/v8i35/86639
  • Arif A H, Waqas M, Rahman UU, Anwar S, Malik A, Iqbal J. A hybrid humanoid-wheeled mobile robotic educational platform – design and prototyping. Indian Journal of Science and Technology. 2014 Jan; 7(12). Doi no:10.17485/ijst/2014/v7i12/51053
  • Murikipudi A, Prakash V, Vigneswaran T. Performance analysis of real time operating system with general purpose operating system for mobile robotic system. Indian Journal of Science and Technology. 2015 Aug; 8(18). Doi no:10.17485/ijst/2015/v8i19/77017
  • Subramanian MB, Sudhagar K, Rajarajeswari G. Design of navigation control architecture for an autonomous mobile robot agent. Indian Journal of Science and Technology. 2016 Mar; 9(10). Doi no: 10.17485/ijst/2016/v9i10/85769
  • Prasad KM, Reddy ARM, Rao KV. Anomaly based Real Time Prevention of under rated App-DDOS attacks on web: An experiential metrics based machine learning approach. Indian Journal of Science and Technology. 2016 Jul; 9(27). Doi no: 10.17485/ijst/2016/v9i27/87872

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.