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International Journal of Multimedia Computing, 2020, 1(1); doi: 10.38007/IJMC.2020.010102.

Motion Control and Path Planning and Tracking Algorithm of Football Robot


Aria Ghabussi

Corresponding Author:
Aria Ghabussi

Texas Tech University, USA


In robotic football, the effectiveness of robot actions and behaviors is completely based on accurate prediction and effective planning of future walking paths and correct adjustment of the posture of the robot when it collides with the football. The purpose of path planning is mainly to plan a collision-free path that meets an evaluation index on a competition-filled field. As the basis for the realization of the basic action of a soccer robot, its pros and cons will directly affect the real-time and accuracy of the action. Therefore, every soccer robot researcher has made it a research focus. This paper mainly studies the motion control and path planning tracking algorithms of soccer robots. In the simulation experiment of this paper, at the end of the interpolation, each joint of the soccer robot has a sudden change in speed from 0.1 rad / s to 0.5 rad / s. Comparing the two speed planning interpolation methods, the speed of the forward segmented speed planning interpolation is more stable. Compared with the trapezoidal speed planning, the speeds jitter of each joint when the robot stops is reduced by more than 90%. This paper proposes the theory of computational verbs combined with the area search method to solve the problem of real-time image information acquisition of small football robotic systems, template matching and similarity solution for robot identification, and decomposing the computational verbs into action words and column verbs to reduce the system's calculation load. It improves the accuracy and stability of image information and lays a foundation for path planning and other research.


Football Robot, Motion Control, Path Planning, Robot Motion, Algorithm Research

Cite This Paper

Aria Ghabussi. Motion Control and Path Planning and Tracking Algorithm of Football Robot. International Journal of Multimedia Computing (2020), Vol. 1, Issue 1: 15-30. https://doi.org/10.38007/IJMC.2020.010102.


[1] Kaarthik Sundar, Sivakumar Rathinam. Algorithms for Heterogeneous, Multiple Depot, Multiple Unmanned Vehicle Path Planning Problems. Journal of Intelligent & Robotic Systems, 2016, 88(2-4):1-14. https://doi.org/10.1007/s10846-016-0458-5

[2] Hamed Fazlollahtabar, Samaneh Hassanli. Hybrid cost and time path planning for multiple autonomous guided vehicles. Applied Intelligence, 2017, 48(1):482-498. https://doi.org/10.1007/s10489-017-0997-x

[3] J.-W. Zhang, L. Liu, K. Chen. Omni-directional bipedal walking path planning. Acta Automatica Sinica, 2016, 42(2):189-201.

[4] Wei-Jen Chen, Bing-Gang Jhong, Mei-Yung Chen. Design of Path Planning and Obstacle Avoidance for a Wheeled Mobile Robot. International Journal of Fuzzy Systems, 2016, 18(6):1-12. https://doi.org/10.1007/s40815-016-0224-7

[5] Patrick Grosch, Federico Thomas. Geometric Path Planning without Maneuvers for Non-Holonomic Parallel Orienting Robots. IEEE Robotics & Automation Letters, 2016, 1(2):1066-1072. https://doi.org/10.1109/LRA.2016.2529688

[6] Joonwoo Lee. Heterogeneous-ants-based path planner for global path planning of mobile robot applications. International Journal of Control Automation & Systems, 2017, 15(5):1-16. https://doi.org/10.1007/s12555-016-0443-6

[7] Ping-Huan Kuo, Tzuu-Hseng S. Li, Guan-Yu Chen. A migrant-inspired path planning algorithm for obstacle run using particle swarm optimization, potential field navigation, and fuzzy logic controller. Knowledge Engineering Review, 2016, 32(158):1-17. https://doi.org/10.1017/S0269888916000151

[8] Jie Ji, Amir Khajepour, Wael William Melek. Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints. IEEE Transactions on Vehicular Technology, 2017, 66(2):952-964. https://doi.org/10.1109/TVT.2016.2555853

[9] Hossein Akbaripour, Ellips Masehian. Semi-lazy probabilistic roadmap: a parameter-tuned, resilient and robust path planning method for manipulator robots. International Journal of Advanced Manufacturing Technology, 2016, 89(5-8):1-30. https://doi.org/10.1007/s00170-016-9074-6

[10] Y. Chen, Y. Tan, L. Cheng. Path planning for a heterogeneous aerial-ground robot system with neighbourhood constraints. Robot, 2017, 39(1):1-7.

[11] Junghun Suh, Joonsig Gong, Songhwai Oh. Fast Sampling-Based Cost-Aware Path Planning With Nonmyopic Extensions Using Cross Entropy. IEEE Transactions on Robotics, 2017, 33(6):1313-1326. https://doi.org/10.1109/TRO.2017.2738664

[12] M. Zimmermann, C. König. Integration of a visibility graph based path planning method in the ACT/FHS rotorcraft. Ceas Aeronautical Journal, 2016, 7(3):391-403. https://doi.org/10.1007/s13272-016-0197-0

[13] S. Zhang, X. Li, J. Zhang. UAV 3D real-time path planning based on dynamic step. Journal of Beijing University of Aeronautics & Astronautics, 2016, 42(12):2745-2754.

[14] Cheng Zhang. Path Planning for Robot based on Chaotic Artificial Potential Field Method. Science Technology & Engineering, 2018, 317(1):012056. https://doi.org/10.1088/1757-899X/317/1/012056

[15] B. Xu, L. Chen, M. Xu. Path Planning Algorithm for Plant Protection UAVs in Multiple Operation Areas. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(2):75-81.

[16] Victor Singh, Karen E. Willcox. Methodology for Path Planning with Dynamic Data-Driven Flight Capability Estimation. Aiaa Journal, 2017, 55(8):1-12. https://doi.org/10.2514/1.J055551

[17] Na Lin, Yanan Zheng, Jianming Li. Path Planning Method Based on Taxi Trajectory Data. Computer Applications & Software, 2016, 12(9):3395-3403. https://doi.org/10.12733/jics20105983

[18] Yang Zhang, Kai Tang. Automatic Sweep Scan Path Planning for Five-Axis Free-Form Surface Inspection Based on Hybrid Swept Area Potential Field. IEEE Transactions on Automation Science & Engineering, 2019, 16(1):261-277. https://doi.org/10.1109/TASE.2018.2827102

[19] Bijo Sebastian, Pinhas Ben-Tzvi. Physics Based Path Planning for Autonomous Tracked Vehicle in Challenging Terrain. Journal of Intelligent & Robotic Systems, 2019, 95(2):511-526. https://doi.org/10.1007/s10846-018-0851-3

[20] Shashikant Koul, Timothy K. Horiuchi. Waypoint Path Planning With Synaptic-Dependent Spike Latency. Circuits and Systems I: Regular Papers, IEEE Transactions on, 2019, 66(4):1544-1557. https://doi.org/10.1109/TCSI.2018.2882818

[21] Xiaoyu Tan, Pengqian Yu, Kah-Bin Lim. Robust path planning for flexible needle insertion using Markov decision processes. International Journal of Computer Assisted Radiology & Surgery, 2018, 13(2):1-13. https://doi.org/10.1007/s11548-018-1783-x

[22] Songqiao Tao, Juan Tan. Path Planning with Obstacle Avoidance Based on Normalized R -Functions. Journal of Robotics, 2018, 2018(1):1-10. https://doi.org/10.1155/2018/5868915

[23] E. Liu, X. Yao. AGV path planning based on improved genetic algorithm and implementation platform. Jisuanji Jicheng Zhizao Xitong/computer Integrated Manufacturing Systems Cims, 2017, 23(3):465-472.

[24] M. Zhang, W. Cai, L. Zhou. Obstacles Avoidance Based Quadratic Bezier Curve Path Planning for Wireless Sensor Networks. Chinese Journal of Sensors and Actuators, 2017, 30(10):1596-1601.

[25] L. An, T. Chen, A. Cheng. A Simulation on the Path Planning of Intelligent Vehicles Based on Artificial Potential Field Algorithm. Automotive Engineering, 2017, 39(12):1451-1456.

[26] Xiaochun Lu, Juntao Fei. Velocity Tracking Control of Wheeled Mobile Robots by Iterative Learning Control. International Journal of Advanced Robotic Systems, 2016, 13(3):1. https://doi.org/10.5772/63813

[27] Seokwon Yeom, Yong-Hyun Woo. Person-Specific Face Detection in a Scene with Optimum Composite Filtering and Colour-Shape Information. International Journal of Advanced Robotic Systems, 2013, 10(1):1. https://doi.org/10.5772/54239

[28] Han Wang, Hongjun Zhang, Kun Wang. Off-road Path Planning Based on Improved Ant Colony Algorithm. Wireless Personal Communications, 2018, 102(2):1705-1721. https://doi.org/10.1007/s11277-017-5229-5

[29] Zhenhua Han, Shugui Liu, Xinghua Li. Path planning method for intelligent CMMs based on safety and the high-efficiency principle. International Journal of Advanced Manufacturing Technology, 2018, 95(9-10):1-10. https://doi.org/10.1007/s00170-017-1500-x

[30] Jungyun Bae, Woojin Chung. A Heuristic for Path Planning of Multiple Heterogeneous Automated Guided Vehicles. International Journal of Precision Engineering and Manufacturing, 2018, 19(12):1765-1771. https://doi.org/10.1007/s12541-018-0205-x