A New Hybrid Method in Global Dynamic Path Planning of Mobile Robot
Keywords:Mobile robot, path planning, DWM algorithm
AbstractPath planning and real-time obstacle avoidance is the key technologies of mobile robot intelligence. But the efficiency of the global path planning is not very high. It is not easy to avoid obstacles in real time. Aiming at these shortcomings it is proposed that a global dynamic path planning method based on improved A* algorithm and dynamic window method. At first the improved A* algorithm is put forward based on the traditional A* algorithm in the paper. Its optimized heuristic search function is designed. They can be eliminated that the redundant path points and unnecessary turning points. Simulation experiment 1 results show that the planned path length is reduced greatly. And the path transition points are less, too. And then it is focused on the global dynamic path planning of fusion improved A* Algorithm and Dynamic Window Method. The evaluation function is constructed taking into account the global optimal path. The real time dynamic path is planning. On the basis of ensuring the optimal global optimization of the planning path, it is improved that the smoothness of the planning path and the local real-time obstacle avoidance ability. The simulation experiments results show that the fusion algorithm is not only the shorter length, but also the smoother path compared the traditional path planning algorithms with the fusion algorithm in the paper. It is more fit to the dynamics of the robot control. And when a dynamic obstacle is added, the new path can be gained. The barrier can be bypass and the robot is to reach the target point. It can be guaranteed the global optimality of the path. Finally the Turtlebot mobile robot was used to experiment. The experimental results show that the global optimality of the proposed path can be guaranteed by the fusion algorithm. And the planned global path is smoother. When the random dynamic obstacle occurs in the experiment, the robot can be real-time dynamic obstacle avoidance. It can re-plan the path. It can bypass the random obstacle to reach the original target point. The outputting control parameters are more conducive to the robot’s automatic control. The fusion method is used for global dynamic path planning of mobile robots in this paper. In summary the experimental results show that the method is good efficiency and real-time performance. It has great reference value for the dynamic path planning application of mobile robot.
Abd, M.E.; Ahmed, M.Y.; Amgad, M.B.; Yehia, Z.E. (2018). Fixed ground-target tracking control of satellites using a nonlinear model predictive control, Mathematical Modelling of Engineering Problems, 5(1), 11-20, 2018. https://doi.org/10.18280/mmep.050102
Bhattacharya, P.; Gavrilova, M.L. (2008). Roadmap-based path planning - using the Voronoi diagram for a clearance-based shortest path, IEEE Robotics & Automation Magazine, 15(2), 58-66, 2008. https://doi.org/10.1109/MRA.2008.921540
Dai, Y.; Zhu, X.; Chen, L.S.; Liu, H.; Zhang, T.; Liu, S.J.A. (2015). New Multi-Body Dynamic Model of A Seafloor Miner And Its Trafficability Evaluation, International Journal of Simulation Modelling, 14(4), 732-743, 2015. https://doi.org/10.2507/IJSIMM14(4)CO19
Dai, Y.; Pang, L.; Chen, L.; Zhu, X.; Zhang, T. (2016);
A New Multi-Body Dynamic Model of a Deep Ocean Mining Vehicle-Pipeline-Ship System and Its Integrated Motion Simulation, Journal of Mechanical Engineering, 62(12): 757-763, 2016. https://doi.org/10.5545/sv-jme.2015.3211
Dragic, M.; Sorak, M. (2016). Simulation for Improving the Performance of Small and Medium Sized Enterprises, Journal of Simulation modeling, 15(4), 597-610, 2016.
Eele, A.J.; Richard A. (2015). Path-planning with avoidance using nonlinear branch-andbound optimization, Journal of Guidance Control & Dynamics, 32(2), 384-394, 2015. https://doi.org/10.2514/1.40034
Endres, F.; Hess, J.; Sturm, J.; Cremers, D.; Burgard, W. (2014). 3D Mapping with an RGB-D Camera, IEEE Transactionson Robotics, 30(1), 177-187, 2014. https://doi.org/10.1109/TRO.2013.2279412
Gao, Y.; Wu, X.; Liu, Y.; Li, J.M.; Liu J.H.(2017). A Rapid Recognition of Impassable Terrain for Mobile Robots with Low Cost Range Finder Based on Hypotheses Testing Theory, International Journal of Computers Communications & Control, 12(6), 813-823, 2017. https://doi.org/10.15837/ijccc.2017.6.2981
Gao, X.; Zhang, T. (2015). Robust RGB-D simultaneous localization and mapping using planar point features, Robotics and Autonomous Systems, 72, 1-14, 2015. https://doi.org/10.1016/j.robot.2015.03.007
Genco, A.; Viggiano, A.; Magi, V. (2018). How to enhance the energy efficiency of HVAC systems, Mathematical Modelling of Engineering Problems, 5(3), 153-160, 2018. https://doi.org/10.18280/mmep.050304
Glasius, R.; Komoda, A.; Gielen, S.C.A.M. (1995). Neural network dynamics for path planning and obstacle avoidance, Neural Networks, 8(1), 125-133, 1995. https://doi.org/10.1016/0893-6080(94)E0045-M
Herrera, C.D.; Kannala, J.; Heikkila. J. (2012). Joint depth and color camera calibration with distortion correction, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(10), 2058-2064, 2014.
Kaess, M. (2015). Simultaneous localization and mapping with infinite planes, IEEE Int. Conf. on Robotics and Automation, 4605-4611, 2015. https://doi.org/10.1109/ICRA.2015.7139837
Lei, W.J.; Cheng, X.S.; Dai, N. (2014). Multi-model machining path planning based on improved genetic algorithm, Journal of Mechanical Engineering, 50(11), 153-161, 2014. https://doi.org/10.3901/JME.2014.11.153
Liu, C.M.; Liu, L.; Liu, C.B. (2018). Analysis of wind resistance of high-rise building structures based on computational fluid dynamics simulation technology, International Journal of Heat and Technology, 36(1), 376-380, 2018. https://doi.org/10.18280/ijht.360150
Liu, J.H.; Yang, J.G.; Liu H.P. (2015). Robot global path planning based on ant colony optimization with artificial potential field, Transactions of the Chinese Society for Agricultural Machinery, 46(9), 18-27, 2015.
Neerendra, K.; Zoltan, V. (2016). Heuristic Approaches in Robot Navigation, IEEE International Conference on Intelligent Engineering Systems, 219-212, 2015.
Sara, J.; Jumel, F.; Simonin, O. (2017). Dynamic multi Agent patrolling Robotic application for service delivery to mobile people, Revue d'Intelligence Artificielle, 31(4), 379-4001, 2017.
Saric, T.; Simunovic, G.; Simunovic, K.; Svalina, I. (2016). Estimation of Machining Time for CNC Manufacturing Using Neural Computing, Journal of Simulation Modeling, 15(4), p. 663-675, 2016. https://doi.org/10.2507/IJSIMM15(4)7.359
Song, X.R.; Chen, H. (2015). Stabilization Precision Control Methods of Photoelectric Aim- Stabilized System, Optics Communication, 9(351), 115-120, 2015. https://doi.org/10.1016/j.optcom.2015.04.056
Widyotriatmo, A.; Joelianto, E.; Prasdianto, A.; Bahtiar, H.; Nazaruddin, Y.Y. (2017). Implementation of Leader-Follower Formation Control of a Team of Nonholonomic Mobile Robots, International Journal of Computers Communications & Control, 12(6), 871-885, 2017. https://doi.org/10.15837/ijccc.2017.6.2774
Wang, C.; Mao, Y. S.; Du, K. J.; Hu, B. Q.; Song, L.F. (2016). Simulation on Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicle, Journal of Simulation modeling, 15(3), 460-472, 2016.
Xiao, Q.K.; Liu, S.Q. (2017). Motion retrieval based on Dynamic Bayesian Network and Canonical Time Warping, Soft Computing, 21(1), 267-280, 2017. https://doi.org/10.1007/s00500-015-1889-9
Xiao, Q.K.; Song, R. (2017). Motion retrieval based on Motion Semantic Dictionary and HMM inference, Soft Computing, 21(1), 255-265, 2017. https://doi.org/10.1007/s00500-016-2059-4
Zhang, H.; Hu, Y.L. (2016). Path planning of mobile robot based on improved D* algorithm, Industrial Control Computer, 29(11), 73-77, 2016.
Zhu, D.Q.; Sun, B.; Li L. (2015). Algorithm for AUV's 3-Dpath planning and safe obstacle avoidance based on biological inspired model, Control and Decision, 30(5), 798-806, 2015.
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