本論文基於深度強化學習理論建立多機器人系統的導航避障神經網路。網路設計從雙機器人系統出發，考慮狀態對稱性、非完整約束，優先順序與速度障礙物等條件，以有效率的方式完成符合運動學且具社交禮讓能力的避障導航訓練。論文中特別提出創新的擴展架構，結合社交斥力觀念使雙機器人避障導航網路能以輕量的計算成本系統性地擴展於多機器人系統。此外，為了彌補避障導航網路於目標點定位精度不足的問題，論文也發展了一套軌跡規劃與追蹤方法，用於機器人導航至目標點附近的控制切換。驗證發展理論與方法採用自製輪型機器人，其上安裝了光學雷達、光流感測器、慣性感測器等，並使用擴展式卡曼濾波器進行感測融合達成精確定位。實際場域的測試證明了多機器人導航避障的可行性與性能。

This thesis develops a navigation and obstacle avoidance neural network for the multi-robot system based on deep reinforcement learning. The network design starts with the dual-robot system. Considering state symmetry, nonholonomic constraints, priority order and speed obstacles, the reinforcement learning can efficiently train the network so that it conforms to the kinematics of mobile robots and can perform collision avoidance and social navigation. An innovative extension architecture is also proposed in the thesis. Combined with the concept of social repulsive force, the dual-robot obstacle avoidance and navigation network can be systematically extended to multi-robot systems with light computational cost. In addition, in order to make up for the insufficient positioning accuracy of the network at the target point, the thesis also proposes a trajectory planning and tracking method for the control switching of the robot navigation to the vicinity of the target point. To verify the developed theories and methods, a set of differential drive wheeled robots are constructed. Each of the robots is equipped with an optical radar, an optical flow sensor, an inertial measurement unit, etc., and the extended Kalman filter is used for sensor fusion to achieve precise localization. Experiments in the indoor environment prove the feasibility and performance of the proposed multi-robot navigation and obstacle avoidance network.

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