Multi Spherical Robot Control System for Nuclear Radiation Leak Detection

Xinyu He; Jianwen Huo; Rui Lin

Abstract

With the development of the global nuclear industry, the need for advanced safety and security measures has increased. Robotic systems have become a leading solution for detecting radioactive sources in nuclear power plants. However, a single robot often lacks flexibility and adaptability, especially in complex environments. To address these challenges, an innovative multi-spherical robot formation system based on the XK-I spherical robot platform, which is optimized for detecting nuclear leaks, is proposed in this paper. Firstly, this paper introduces an affine formation control algorithm, which supports formation maintenance, transformation, and scaling. Subsequently, an improved RRT* path planning algorithm is proposed, which ensures formation reliable path planning. These capabilities enable the multi-spherical robot system to autonomously inspect and detect nuclear power plant environments. Finally, based on theoretical analysis and calculations, a series of experiments are conducted to assess the ability of performing precise formation movements in nuclear power plants. The experimental results demonstrate that the multi-spherical robot control system can efficiently detect nuclear radioactive source leakage and achieve different formation motions accurately.

References

1.
Ristic, B.; Angley, D.; Moran, B.; et al. Autonomous multi-robot search for a hazardous source in a turbulent environment. Sensors 2017, 17, 918.
2.
Li, B.; Zhu, Y.; Wang, Z.; et al. Use of multi-rotor unmanned aerial vehicles for radioactive source search. Remote Sens. 2018, 10, 728.
3.
Luo, M.; Huo, J.; Liu, M.; et al. Distributed collaboration: Cognitive difference and collaborative decision for multi-robot radioactive source search. Ann. Nucl. Energy 2024, 196, 110210.
4.
Liu, M.; Lin, R.; Yang, M.; et al. Active disturbance rejection motion control of spherical robot with parameter tuning. Ind. Robot Int. J. Robot. Res. Appl. 2022, 49, 332–343.
5.
Ni, J.; Shi, P. Adaptive neural network fixed-time leader–follower consensus for multiagent systems with constraints and disturbances. IEEE Trans. Cybern. 2020, 51, 1835–1848.
6.
Moreira, M.S.M.; Villa, D.K.D.; Sarcinelli-Filho, M. Controlling a Virtual Structure Involving a UAV and a UGV for Warehouse Inventory. J. Intell. Robot. Syst. 2024, 110, 121.
7.
Lee, G.; Chwa, D. Decentralized behavior-based formation control of multiple robots considering obstacle avoidance. Intell. Serv. Robot. 2018, 11, 127–138.
8.
Ma, N.; Cao, Y. Consensus-based distributed formation control for coordinated battle system of manned/unmanned aerial vehicles. Trans. Inst. Meas. Control 2024, 46, 3–14.
9.
Chen, H.; Chen, H.; Qiang, L. Multi-UAV 3D formation path planning based on improved artificial potential field. J. Syst. Simul. 2020, 32, 414–420.
10.
Li, Y.; Dong, S.; Li, K. Fuzzy adaptive finite-time event-triggered control of time-varying formation for nonholonomic multirobot systems. IEEE Trans. Intell. Veh. 2023, 9, 725–737.
11.
Dong, X.; Zhou, Y.; Ren, Z.; et al. Time-varying formation tracking for second-order multi-agent systems subjected to switching topologies with application to quadrotor formation flying. IEEE Trans. Ind. Electron. 2016, 64, 5014–5024.
12.
Lin, Z.; Chen, Z.; Fu, M. A linear control approach to distributed multi-agent formations in d-dimensional space. In Proceedings of the 52nd IEEE Conference on Decision and Control, Firenze, Italy, 10–13 December 2013; pp. 6049–6054.
13.
Zhao, S. Affine formation maneuver control of multiagent systems. IEEE Trans. Autom. Control 2018, 63, 4140–4155.
14.
LaValle, S.M.; Kuffner, J.J. Rapidly-exploring random trees: Progress and prospects. In Algorithmic and Computational Robotics; CRC Press: Boca Raton, FL, USA, 2001; pp. 303–307.
15.
Karaman, S.; Walter, M.R.; Perez, A.; et al. Anytime motion planning using the RRT. In Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China, 9–13 May 2011; pp. 1478–1483.
16.
Kuffner, J.J.; LaValle, S.M. RRT-connect: An efficient approach to single-query path planning. In Proceedings of the IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No. 00CH37065), San Francisco, CA, USA, 24–28 April 2000; pp. 995–1001.
17.
Urmson, C.; Simmons, R. Approaches for heuristically biasing RRT growth. In Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No. 03CH37453), Las Vegas, NV, USA, 27–31 October 2003; pp. 1178–1183.

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