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

Assistive trajectories for human-in-the-loop mobile robotic platforms

Autonomous and semi-autonomous smoothly interruptible trajectories are developed which are highly suitable for application in tele-operated mobile robots, operator on-board military mobile ground platforms, and other mobility assistance platforms. These trajectories will allow a navigational system to provide assistance to the operator in the loop, for purpose built robots or remotely operated platforms. This will allow the platform to function well beyond the line-of-sight of the operator, enabling remote operation inside a building, surveillance, or advanced observations whilst keeping the operator in a safe location. In addition, on-board operators can be assisted to navigate without collision when distracted, or under-fire, or when physically disabled by injury

Near-Optimal Motion Planning Algorithms Via A Topological and Geometric Perspective

Motion planning is a fundamental problem in robotics, which involves finding a path for an autonomous system, such as a robot, from a given source to a destination while avoiding collisions with obstacles. The properties of the planning space heavily influence the performance of existing motion planning algorithms, which can pose significant challenges in handling complex regions, such as narrow passages or cluttered environments, even for simple objects. The problem of motion planning becomes deterministic if the details of the space are fully known, which is often difficult to achieve in constantly changing environments. Sampling-based algorithms are widely used among motion planning paradigms because they capture the topology of space into a roadmap. These planners have successfully solved high-dimensional planning problems with a probabilistic-complete guarantee, i.e., it guarantees to find a path if one exists as the number of vertices goes to infinity. Despite their progress, these methods have failed to optimize the sub-region information of the environment for reuse by other planners. This results in re-planning overhead at each execution, affecting the performance complexity for computation time and memory space usage. In this research, we address the problem by focusing on the theoretical foundation of the algorithmic approach that leverages the strengths of sampling-based motion planners and the Topological Data Analysis methods to extract intricate properties of the environment. The work contributes a novel algorithm to overcome the performance shortcomings of existing motion planners by capturing and preserving the essential topological and geometric features to generate a homotopy-equivalent roadmap of the environment. This roadmap provides a mathematically rich representation of the environment, including an approximate measure of the collision-free space. In addition, the roadmap graph vertices sampled close to the obstacles exhibit advantages when navigating through narrow passages and cluttered environments, making obstacle-avoidance path planning significantly more efficient. The application of the proposed algorithms solves motion planning problems, such as sub-optimal planning, diverse path planning, and fault-tolerant planning, by demonstrating the improvement in computational performance and path quality. Furthermore, we explore the potential of these algorithms in solving computational biology problems, particularly in finding optimal binding positions for protein-ligand or protein-protein interactions. Overall, our work contributes a new way to classify routes in higher dimensional space and shows promising results for high-dimensional robots, such as articulated linkage robots. The findings of this research provide a comprehensive solution to motion planning problems and offer a new perspective on solving computational biology problems

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

Path planning algorithms for autonomous navigation of a non-holonomic robot in unstructured environments

openPath planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms.Path planning is a crucial aspect of autonomous robot navigation, enabling robots to efficiently and safely navigate through complex environments. This thesis focuses on autonomous navigation for robots in dynamic and uncertain environments. In particular, the project aims to analyze the localization and path planning problems. A fundamental review of the existing literature on path planning algorithms has been carried on. Various factors affecting path planning, such as sensor data fusion, map representation, and motion constraints, are also analyzed. Thanks to the collaboration with E80 Group S.p.A., the project has been developed using ROS (Robot Operating System) on a Clearpath Dingo-O, an indoor mobile robot. To address the challenges posed by unstructured and dynamic environments, ROS follows a combined approach of using a global planner and a local planner. The global planner generates a high-level path, considering the overall environment, while the local planner handles real-time adjustments to avoid moving obstacles and optimize the trajectory. This thesis describes the role of the global planner in a ROS-framework. Performance benchmarking of traditional algorithms like Dijkstra and A*, as well as other techniques, is fundamental in order to understand the limits of these methods. In the end, the Hybrid A* algorithm is introduced as a promising approach for addressing the issues of unstructured environments for autonomous navigation of a non-holonomic robot. The core concepts and implementation details of the algorithm are discussed, emphasizing its ability to efficiently explore continuous state spaces and generate drivable paths.The effectiveness of the proposed path planning algorithms is evaluated through extensive simulations and real-world experiments using the mobile platform. Performance metrics such as path length, execution time, and collision avoidance are analyzed to assess the efficiency and reliability of the algorithms

Path Navigation For Robot Using Matlab

Path navigation using fuzzy logic controller and trajectory prediction table is to drive a robot in the dynamic environment to a target position,without collision. This path navigation method consists of static navigation method and dynamic path planning. The static navigation used to avoid the static obstacles by using fuzzy logic controller, which contains four sensor input and two output variables. If the robot detects moving obstacles, the robot can recognize the velocity and moving direction of each obstacle and generate the Trajectory Prediction Table to predict the obstacles’ future trajectory. If the trajectory prediction table which reveals that the robot will collide with an obstacle, the dynamic path planning will find a new collision free path to avoid the obstacle by waiting strategy or detouring strategy. . A lot of research work has been carried out in order to solve this problem. In order to navigate successfully in an unknown or partially known environment, the mobile robots should be able to extract the necessary surrounding information from the environment using sensor input, use their built-in knowledge for perception and to take the action required to plan a feasible path for collision free motion and to reach the goal

Navigation of mobil robot using fuzzy logic controller

This chapter gives an overview of the research work reported in the thesis. First, the background of the research and the chosen problem domain are outlined. Then, the objectives of this research work are described. Finally, an outline of the thesis content is provided