협소하고 복잡한 환경에서 자율 주행을 위한 샘플링 기반 모션 계획 방법

학위논문 (박사)-- 서울대학교 융합과학기술대학원 융합과학부, 2017. 8. 박재흥.Autonomous vehicles are being actively developed for fully autonomous driving without driver intervention. Motion planning is one of the most key technologies in terms of driving safety and efficiency. In particular, the motion planning in constrained narrow space such as a parking lot is very challenging because it requires many changes in forward and backward directions and adjustments of position and orientation of the vehicle. In this thesis, a sampling-based motion planning algorithm is proposed based on Rapidly-exploring Random Trees (RRT, RRT*) by specifying desired orientation during the tree expansion and the rewiring step. The contribution is as follows. First, efficient sampling method is proposed for narrow-cluttered area. In this area, the probability of obtaining a sample to pass through the area due to the obstacle area is relatively low than an open area. It may also fail to extend the path if sampled position is difficult to extend from near nodes. To solve this problem, a constraint model on the tangential direction of the random sample is proposed. Second, we propose an extension method based on tangential direction constraint. In the process of expanding the tree to random samples, a large number of nodes in narrow-cluttered regions cannot pass the collision test. This increases unnecessary iteration numbers and increases memory usage. To solve this problem, we propose a node extension method based on gradient descent. The proposed algorithm has been tested in various situations and its results demonstrated much faster target path search and convergence to the optimal path than the existing nonholonomic RRT*.I. Introduction 1 1.1 Autonomous Vehicles 1 1.2 Planning System of Autonomous Vehicles 2 1.3 Contribution of Thesis 4 II. Related Works 6 2.1 Motion Planning for Aunomous Vehicles 6 2.2 Sampling-based Motion Planning Algorithms 9 III. Sampling-based Kinodynamic Motion Planning Algorithm for Narrow Cluttered Environments 14 3.1 Overview 14 3.2 Preliminary Definition 15 3.2.1 Problem Statements 15 3.2.2 Autonomous Vehicle Model 16 3.3 Kinodynamic RRT and Limitations 16 3.3.1 Overview of DO-RRT Algorithm 20 3.4 Magnetic-like Field based Desired Orientation Model 20 3.4.1 Magnet-like Field Model 22 3.4.2 Pfaffian Constraints 24 3.4.3 DO(Desired Orientation) Model 26 3.5 Sampling Fuction of DO-RRT 28 3.6 Extend Function of DO-RRT 30 3.7 Experimental Results 31 3.7.1 Experimental Condition 31 3.7.2 Simulation Test Results 32 3.7.3 Vehicle Test Results 34 IV. Sampling-based Geometric Motion Planning Algorithm for Narrow Cluttered Environments 38 4.1 Overview 38 4.2 Backgrounds 39 4.2.1 Algorithm Description and Limitations 39 4.2.2 Overview of Proposed Algorithm 42 4.3 Desired Orientation based Random Sampling Method 44 4.4 Desired Orientation based Extend Method 47 4.5 Analysis 49 4.5.1 Probabilistic Completeness 49 4.5.2 Asymptotic Optimality 51 4.5.3 Configuration Space Analysis 53 4.6 Experimental Results 58 4.6.1 Experimental Condition 59 4.6.2 The Result of Desired Orientation-RRT Planner 59 4.6.3 Result of Desired Orientation-RRT 65 V. Experimental Platform Development 71 5.1 Hardware Architecture 71 5.2 Software Architecture 73 5.3 Valet Parking System 74 5.3.1 Perception System 75 5.3.2 Localization System 76 5.3.3 Planning System 77 5.3.4 Control System 79 5.4 Experimental Validation 81 VI. Conclusion 85 Bibliography 86Docto

Planning With Adaptive Dimensionality

Modern systems, such as robots or virtual agents, need to be able to plan their actions in increasingly more complex and larger state-spaces, incorporating many degrees of freedom. However, these high-dimensional planning problems often have low-dimensional representations that describe the problem well throughout most of the state-space. For example, planning for manipulation can be represented by planning a trajectory for the end-effector combined with an inverse kinematics solver through obstacle-free areas of the environment, while planning in the full joint space of the arm is only necessary in cluttered areas. Based on this observation, we have developed the framework for Planning with Adaptive Dimensionality, which makes effective use of state abstraction and dimensionality reduction in order to reduce the size and complexity of the state-space. It iteratively constructs and searches a hybrid state-space consisting of both abstract and non-abstract states. Initially the state-space consists only of abstract states, and regions of non-abstract states are selectively introduced into the state-space in order to maintain the feasibility of the resulting path and the strong theoretical guarantees of the algorithm---completeness and bounds on solution cost sub-optimality. The framework is able to make use of hierarchies of abstractions, as different abstractions can be more effective than others in different parts of the state-space. We have extended the framework to be able to utilize anytime and incremental graph search algorithms. Moreover, we have developed a novel general incremental graph search algorithm---tree-restoring weighted A*, which is able to minimize redundant computation between iterations while efficiently handling changes in the search graph. We have applied our framework to several different domains---navigation for unmanned aerial and ground vehicles, multi-robot collaborative navigation, manipulation and mobile manipulation, and navigation for humanoid robots

On the Collaboration of an Automatic Path-Planner and a Human User for Path-Finding in Virtual Industrial Scenes

This paper describes a global interactive framework enabling an automatic path-planner and a user to collaborate for finding a path in cluttered virtual environments. First, a collaborative architecture including the user and the planner is described. Then, for real time purpose, a motion planner divided into different steps is presented. First, a preliminary workspace discretization is done without time limitations at the beginning of the simulation. Then, using these pre-computed data, a second algorithm finds a collision free path in real time. Once the path is found, an haptic artificial guidance on the path is provided to the user. The user can then influence the planner by not following the path and automatically order a new path research. The performances are measured on tests based on assembly simulation in CAD scenes

Mobile robots and vehicles motion systems: a unifying framework

Robots perform many different activities in order to accomplish their tasks. The robot motion capability is one of the most important ones for an autonomous be- havior in a typical indoor-outdoor mission (without it other tasks can not be done), since it drastically determines the global success of a robotic mission. In this thesis, we focus on the main methods for mobile robot and vehicle motion systems and we build a common framework, where similar components can be interchanged or even used together in order to increase the whole system performance