在這項研究中，我們整合了用於搜救的混合無人機和雙足機器人團隊系統(URTS)的局部運動規劃和強健H∞分散式基於觀測器的前饋參考追踪容錯控制。URTS中每個代理執行搜救任務的系統架構被提出，以解釋如何在用於搜救的URTS中集成參考軌跡規劃和追踪控制。為了將任務最佳地分配給URTS中的每個代理，任務分配問題被研究。為了最佳化URTS中每個代理到達這些分配的任務位置的路徑，路徑規劃問題被陳述。為了處理複雜的搜救地形，我們將路徑規劃問題分解為三個步驟：(1)全域路徑規劃、(2)行為決策和(3)局部運動規劃。通過這樣的分解，一些基於道路圖的路徑規劃算法得以應用於URTS中代理的全局路徑規劃。通過行為決策，我們可以根據地形環境決定什麼行為來跟隨全局路徑。接著，我們重點研究了無人機飛行行為和機器人行走行為的局部運動規劃問題，然後是混合團隊系統中無人機和機器人的追踪控制問題。局部運動規劃和追踪控制之間的關係，即參考軌蹟的轉換，也被詳細探討。通過所提出的新穎的前饋線性化控制方案，強健H∞分散式基於觀測器的前饋參考追蹤容錯控制設計對於URTS中的每個代理被顯著簡化。一種新穎的錯誤信號平滑信號模型被嵌入到線性化系統中，以實現透過觀測器估測來達到主動容錯控制。透過將強健H∞分散式基於觀測器的前饋參考追蹤容錯控制策略的設計轉化為混合團隊系統中每個代理的線性矩陣不等式(LMI)約束優化問題，該問題得以用兩步驟設計程序解出。借助 MATLAB LMI Toolbox，URTS中各無人機和機器人的強健H∞分散式基於觀測器的前饋參考追蹤容錯控制設計問題可以被有效解決。最後，模擬結果展現了局部運動規劃與混合URTS執行搜救任務的集成，並驗證所提出的混合URTS在外部干擾和制動器和傳感器錯誤下的強健H∞分散式基於觀測器的前饋參考追蹤容錯控制方法。

In this study, we integrate a local motion planning and robust H∞ decentralized feedforward reference tracking fault-tolerant control (FTC) of a hybrid UAVs and biped robots team system (URTS) for the purpose of search and rescue (S&R). A system architecture of performing S&R tasks for each agent in URTS is proposed to explain how to integrate reference trajectory planning and tracking control in URTS for S&R usage. In order to optimally allocate tasks to each agent in URTS, a task allocate problem is investigated. In order to optimally plan a path for each agent in URTS to reach these allocated task locations, a path planning problem is formulated. To deal with complex S&R terrain, we decompose the path planning problem into three steps, i.e., (i) global path planning, (ii) behavior decision and (iii) local motion planning. Through such decomposition, some roadmap-based path planning algorithms can be applied to the global path planning of agents in URTS. By the behavior decision, we can decide what behavior to follow the global path according to the terrain environment. Next, we focus on the local motion planning problem of flying behavior for UAV and walking behavior for robot, and then the tracking control problem for UAVs and robots in the hybrid team system. The relationship between local motion planning and tracking control, i.e., the transformation of the reference trajectory, is also explored in detail. By a proposed novel feedforward linearization control scheme, the robust H∞ decentralized observer-based feedforward reference tracking FTC design is significantly simplified for each agent in URTS. A novel smoothing signal model of fault signal is embedded into the linearized system to achieve the active FTC through observer estimation. Then, the design of the robust H∞ decentralized observer-based feedforward reference tracking FTC strategy of URTS is transformed into a linear matrix inequality (LMI) -constrained optimization problem of each agent in the hybrid team system which can be solved by a two-step design procedure. With the help of MATLAB LMI Toolbox, the robust H∞ decentralized observer-based feedforward reference tracking FTC design problem of each UAV and robot in URTS is effectively solved. Finally, the simulation results are used to demonstrate the integration of local motion planning with the S&R tasks of hybrid URTS and to verify the effectiveness of the proposed robust H∞ decentralized observer-based feedforward reference tracking FTC method of hybrid URTS under the external disturbance and the actuator and sensor fault.

[1] L. Kloeker, T. Moers, L. Vater, A. Zlocki, and L. Eckstein, “Utilization and potentials of unmanned aerial vehicles (uavs)
in the field of automated driving: A survey,” 2021 5th International Conference on Vision, Image and Signal Processing
(ICVISP), pp. 9–17, 2021.
[2] A. Dorri, S. S. Kanhere, and R. Jurdak, “Multi-agent systems: A survey,” IEEE Access, vol. 6, pp. 28 573–28 593, 2018.
[3] F. Chen, W. Ren et al., “On the control of multi-agent systems: A survey,” Foundations and Trends® in Systems and
Control, vol. 6, no. 4, pp. 339–499, 2019.
[4] W. Cheng, K. Zhang, B. Jiang, and S. X. Ding, “Fixed-time fault-tolerant formation control for heterogeneous multi-agent
systems with parameter uncertainties and disturbances,” IEEE Trans. Circuits and Systems I: Regular Papers, vol. 68,
no. 5, pp. 2121–2133, 2021.
[5] G. M. Skaltsis, H.-S. Shin, and A. Tsourdos, “A survey of task allocation techniques in mas,” 2021 International Conference
on Unmanned Aircraft Systems (ICUAS), pp. 488–497, 2021.
[6] H. y. Zhang, W. m. Lin, and A. x. Chen, “Path planning for the mobile robot: A review,” Symmetry, vol. 10, no. 10, p.
450, 2018.
[7] J. N. Yasin, S. A. S. Mohamed, M.-H. Haghbayan, J. Heikkonen, H. Tenhunen, and J. Plosila, “Unmanned aerial vehicles
(uavs): Collision avoidance systems and approaches,” IEEE Access, vol. 8, pp. 105 139–105 155, 2020.
42
[8] B. Arbanas, A. Ivanovic, M. Car, M. Orsag, T. Petrovic, and S. Bogdan, “Decentralized planning and control for uav–ugv
cooperative teams,” Autonomous Robots, vol. 42, no. 8, pp. 1601–1618, 2018.
[9] L. Li, Y. Miao, A. H. Qureshi, and M. C. Yip, “Mpc-mpnet: Model-predictive motion planning networks for fast, nearoptimal planning under kinodynamic constraints,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4496–4503,
2021.
[10] T. Olcay and A. Ozkurt, “Design and walking pattern generation of a biped robot,” ¨ Turkish Journal of Electrical Engineering
and Computer Sciences, vol. 25, no. 2, pp. 761–769, 2017.
[11] Y. Sabri, N. El Kamoun, and F. Lakrami, “A survey: Centralized, decentralized, and distributed control scheme in smart
grid systems,” 2019 7th Mediterranean Congress of Telecommunications (CMT), pp. 1–11, 2019.
[12] C. Wang, J. Wang, P. Wu, and J. Gao, “Consensus problem and formation control for heterogeneous multi-agent systems
with switching topologies,” Electronics, vol. 11, no. 16, p. 2598, 2022.
[13] D. Rotondo, F. Nejjari, and V. Puig, “Passive and active ftc comparison for polytopic lpv systems,” 2013 European Control
Conference (ECC), pp. 2951–2956, 2013.
[14] B. Paden, M. Cˇ ap, S. Z. Yong, D. Yershov, and E. Frazzoli, “A survey of motion planning and control techniques for ´
self-driving urban vehicles,” IEEE Trans. intelligent vehicles, vol. 1, no. 1, pp. 33–55, 2016.
[15] A. Khamis, A. Hussein, and A. Elmogy, “Multi-robot task allocation: A review of the state-of-the-art,” Cooperative robots
and sensor networks 2015, pp. 31–51, 2015.
[16] M. Elbanhawi and M. Simic, “Sampling-based robot motion planning: A review,” Ieee access, vol. 2, pp. 56–77, 2014.
[17] Y. Liu and R. Bucknall, “A survey of formation control and motion planning of multiple unmanned vehicles,” Robotica,
vol. 36, no. 7, pp. 1019–1047, 2018.
[18] J. Yu and S. M. LaValle, “Multi-agent path planning and network flow,” in Algorithmic foundations of robotics X. Springer,
2013, pp. 157–173.
[19] F. Sabatino, “Quadrotor control: modeling, nonlinearcontrol design, and simulation,” 2015.
[20] B. S. Chen, Y. Y. Tsai, and M. Y. Lee. Supplementary file. hsinchu, taiwan. [Online]. Available: https:
//www.dropbox.com/s/qiv63y5hw7vrubp/SupplementFileRobot.pdf
[21] S. Kajita, O. Matsumoto, and M. Saigo, “Real-time 3d walking pattern generation for a biped robot with telescopic legs,”
Proceedings 2001 ICRA. IEEE international conference on robotics and automation (Cat. no. 01ch37164), vol. 3, pp.
2299–2306, 2001.
[22] Q. Huang, K. Yokoi, S. Kajita, K. Kaneko, H. Arai, N. Koyachi, and K. Tanie, “Planning walking patterns for a biped
robot,” IEEE Trans. robotics and automation, vol. 17, no. 3, pp. 280–289, 2001.
[23] S. Kajita, F. Kanehiro, K. Kaneko, K. Fujiwara, K. Harada, K. Yokoi, and H. Hirukawa, “Biped walking pattern generation
by using preview control of zero-moment point,” 2003 IEEE International Conference on Robotics and Automation (Cat.
No.03CH37422), vol. 2, pp. 1620–1626 vol.2, 2003.
[24] F. L. Lewis, D. Vrabie, and V. L. Syrmos, Optimal control. John Wiley & Sons, 2012.
[25] B. S. Chen and J. H. Lin, “Robust collaborative team formation control of hybrid teams of biped robots and wheeled
vehicles under external disturbance and communication interactions,” IEEE Access, vol. 10, pp. 77 633–77 657, 2022.
[26] J. Yang, C. Liu, M. Coombes, Y. Yan, and W.-H. Chen, “Optimal path following for small fixed-wing uavs under wind
disturbances,” IEEE Trans. Control Systems Technology, vol. 29, no. 3, pp. 996–1008, 2021.
[27] B. S. Chen, C. C. Wu, and Y. W. Chen, “Human walking gait with 11-dof humanoid robot through robust neural fuzzy
networks tracking control.” International Journal of Fuzzy Systems, vol. 15, no. 1, 2013.
[28] B. S. Chen, M. Y. Lee, W. Y. Chen, and W. Zhang, “Reverse-order multi-objective evolution algorithm for multi-objective
observer-based fault-tolerant control of t-s fuzzy systems,” IEEE Access, vol. 9, pp. 1556–1574, 2021.
43
[29] S. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan, Linear matrix inequalities in system and control theory. SIAM,
1994.
[30] E. Rastogi and L. B. Prasad, “Comparative performance analysis of pd/pid computed torque control, filtered error
approximation based control and nn control for a robot manipulator,” in 2015 IEEE UP Section Conference on Electrical
Computer and Electronics (UPCON), 2015, pp. 1–6.
[31] B. S. Chen and T. W. Hung. Hsinchu, taiwan. [Online]. Available: https://www.dropbox.com/scl/fo/5n6y25k3i8kjxsx9efxa3/
h?dl=0&rlkey=x5p4stwtuf2gmch6l07etvz4b