臺灣博碩士論文加值系統

過去有很多不同的控制器可以用於控制機械手臂，然而FPGA仍是主流的選擇之一。在工業中，FPGA之於機械手臂更是被預期將朝向兩大應用層面發展包含嵌入式手臂應用和電力電子與驅動器應用，現今機器人學中倘若要有軌跡控制的需求，大多會選擇先在空間中進行軌跡規劃後再利用逆向運動學(Inverse Kinematic)計算出各軌跡上的點對應到手臂各軸的角度，然而使用逆向運動學解出得到輸出控制命令方程式極為複雜，需要帶入過多的條件，對於硬體條件實踐相對嚴苛，不僅邏輯元件消耗資源多，在路徑規劃層面更需要與軟韌體進行配合，導致整個系統過於龐大。而這個研究主要是設計一個軌跡追蹤演算法是基於運動規劃的正向運動學，在路徑規劃加以限制，實現一個不需利用逆向運動學就可以達到軌跡控制的系統。這個系統預期可以有效降低 LEs的數量並且提供低誤差與高速的效能。

In the past, many different controllers that could be used for robotic control, however, the classic position controller for robots by FPGA is still the most popular. In the industry, FPGA for robotic arms control is expected to develop towards two major application including embedded arm applications and power electronics driver applications. Nowadays, if there is a need for trajectory planning on robot, most of them will plan in Cartesian space and use inverse kinematic to calculate the angle of each angle corresponding to each axis of the robot arm. However, inverse kinematics solution is extremely complicated and requires too many boundary conditions that cause implementation on hardware is relatively strict, not only logic components consuming, but also need to cooperate with soft or firmware at the path planning level, which resulting in the entire system is too large. And this research is mainly to design a real-time trajectory tracking algorithm based on the forward kinematics of motion planning, and limit the path planning to realize a system that can achieve trajectory control system without using inverse kinematics. This system is expected to reduce LEs (logic elements) in FPGA with high speed and low path error.

摘要 i
ABSTRACT ii
Table of Contents iv
FIGURE CAPTIONS vi
TABLE CAPTIONS vii
I. Introduction 1
1.1 Foreword 1
1.2 Motivative 3
1.3 The Structure of System 4
II. Related Research Background 6
2.1 Kinematic 6
2.1.1 Denavit–Hartenberg parameters 6
2.1.2 Forward Kinematic 8
2.1.3 Inverse Kinematic 11
2.2 RRT 12
2.3 Joint Sampled based Trajectory Planning 15
III. FPGA-based Trajectory Planning Algorithm 24
3.1 ChangeTheta Module 26
3.2 Forward Kinematic Module 27
3.3 Angle Determine Module 29
3.3.1 Preprocessing Module 29
3.3.2 Dot Module 31
3.3.3 distance Module 32
3.3.4 Judgement Module 33
3.4 ThetaCorrection Module 36
3.5 Overall Module 37
IV. Simulation and Testing Result 39
4.1 Background 39
4.2 Determine ???????? 39
4.3 Simulation of Trajectory Tracking Algorithm 41
4.4 FPGA Implementation 43
4.4.1 Gate Level Simulation 43
4.4.2 Testing 46
4.5 Specification Comparison 49
V. Conclusion and Future Work 51
5.1 Conclusion 51
5.2 Future Work 51
Reference 53

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