黃漢邦Huang, Han-Pang臺灣大學:機械工程學研究所蔡乙至Tsai, Yi-ChihYi-ChihTsai2010-06-302018-06-282010-06-302018-06-282008U0001-2507200809403200http://ntur.lib.ntu.edu.tw//handle/246246/187372本文主要的目的在設計與建立一個機器手臂的自動領導系統，使其能在大部分的環境中運作，例如在家庭中的廚房、工廠自動化…等等。此外我們還透過動態環境演算法來延伸成雙臂的路徑規劃，藉此強化機器人的工作效率。先在這篇論文中，我們提出一個關於在狀態空間的運動規劃演算法。它能克服一些困難的靜態環境的運動規劃的問題。由於是採用任意時間規劃(anytime planning)形式，所以在運算時間有限的時候還可以不斷的回傳部份運算完成的軌跡。此外Multi-RRTs 更是利用這項不斷重新規劃軌跡的特點來調整演算法的參數。後我們針對在動態環境的軌跡規劃的問題,提出一個CT-RRTs(Bi-direction RRTs in Configuration Time space)演算法，藉由此演算法能使的機械手臂能在部分已知的動態環境中成功的到達目標點。然而此演算法的是在不斷擴大的狀態-時間的空間中做規劃，其中機械手臂組態空間資訊以及移動物體的位置資訊都包含在狀態-時間的空間中。並且利用多種技術去加速規劃的時間以及增強它的安全性。最後我們利用CT-RRTs去發展雙臂的運動規劃，並且將運動規劃的問題推廣至更高階。驗部份分為模擬與實作。模擬部分建立一多功能的軟體平台，並且利用虛擬實境的方法來建造環境以及顯現模擬結果。在實作上，整合了影像系統以及馬達控制模組。整體系統可以在靜態環境中即時引導機械手臂。The main objective of this thesis is to develop an autonomous navigation system for robot arms, which can operate in most of environments, such as kitchen, factory, etc. Furthermore, by extending our planner into a dual-arm planner, we can enhance the work efficiency of the robot.o begin with, we propose an algorithm about motion planning in the state space. It can overcome some difficult planning problems in static environments. Due to the anytime planning fashion, the partial plan can also be returned when deliberation time is limited. Further, the Multi-RRTs algorithm uses this advantage of continuously re-planning to adjust the parameters.ext, in order to solve the problem of planning in the dynamic environments, we proposed a CT-RRTs (Bi-direction RRTs in Configuration Time space) planner, which can make the robot arm reach goal successfully in the partially known dynamic environment. However it plans in the augmented state-time space of robot arm configuration and the positions of moving objects. Various techniques are used to accelerate planning and enhance its safety.inally, we utilize the new planner to develop the dual-arm planner, and it can be used as the basis to solve higher level problems in motion planning. software platform is developed for both simulation and for real-world navigation, where environment and planning results are visualized in 3D. In real-world implementation, a vision module for distance measurement and a motor control module are integrated. In our experiments, the system is able to navigate the robot arm in static environments in real time.致謝 iiist of Tables viiiist of Figures ixhapter 1 Introduction 1.1 Motivation 1.2 Objectives and Contributions 3.3 Thesis Organization 5hapter 2 Background Knowledge and Relevant Research 7.1 Kinematics Analysis 7.1.1 Forward Kinematics 8.1.2 Inverse Kinematics 9.1.3 Singularity Avoidance 10.1.4 Joint Limit Avoidance 11.2 Planning as a Search Problem 12.2.1 Basic Search Strategies 13.2.2 Heuristic Search 14.2.3 Bidirectional and Multi-Directional Search 16.3 Path Planning Problems to Search Problems 17.3.1 The Configuration Space 17.3.2 Distance Metric 19.4 Randomized Path Planning 20.4.1 Probabilistic Roadmap 21.4.2 Rapidly-Exploring Random Tree 22hapter 3 Planning in the State Space 23.1 Planning with RRT 24.1.1 Basic RRT Planner 24.1.2 RRT-Connect and Bi-direction RRT 26.2 Random Configuration 27.2.1 Dynamic Domain RRT 27.3 Nearest-Neighbor Searching 30.3.1 K-Dimension tree 31.3.2 MPNN 32.3.3 Hybrid Nearest-Neighbor Search 34.3.4 Summary 37.4 Planning in the Dynamic Environments 38.4.1 LRF (Lazy Reconfiguration Forest) 38.4.2 Dynamic Obstacle Prediction 41.5 The Framework of Multi-RRTs 44hapter 4 Planning in the State-Time Space 46.1 Configuration-Time Space 47.2 Modifications of Bi-directions RRTs 48.2.1 Inserting Time Information 48.2.2 Inserting Cost function 52.2.3 Re-planning 56.3 Anytime Planning 59.4 The Framework of CT-RRTs 60hapter 5 Motion planning for Dual-Arm 64.1 Based on Multi-RRTs 65.2 Based on CT-RRTs 67hapter 6 Implementation and Experimental Results 69.1 Software Platform 69.2 Hardware Platform 72.2.1 Humanoid Robot Arm 72.2.2 Control modules 73.3 Experiment Results 75.3.1 “Strict Static-Environment” Scenario 75.3.2 “Moving Objects” Scenario 78.3.3 “Dual-Arm planning” Scenario 80.3.4 “Transport an Object in the Clutter Environment” Scenario 82.3.5 Integration on a Real-World Robot 84hapter 7 Conclusions and Future Works 86.1 Conclusions 86.2 Future Works 87eferences 897118699 bytesapplication/pdfen-US路徑規劃軌跡規劃運動規劃雙臂式機器人path planningplanningmotion planningRRTCT-RRTsmanipulatorthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/187372/1/ntu-97-R95522805-1.pdf