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

Virtual Prototyping and Validation for Autonomous Assistive Mobility

Physical disability in humans is something that no one has control over. It is catastrophic for people to experience it, and sometimes hinders them from enjoying the essence of life. Though disability is something people have no control over, they do have control on how they can go forward and make life beautiful and meaningful. Wheelchairs have helped people with disabilities in mobility, allowing them to move around with the help of others or sometimes on their own. This research will focus on development of an autonomous assistive mobility robot to help the disabled using virtual prototyping tools for development and validation. The developed virtual model will also be developed in real world and validated to navigate autonomously. The virtual and real-world autonomous model developed will take a systems engineering approach. The key features of this system are mapping, localisation, and navigating towards a goal autonomously. The virtual model is validated in different virtual environments for its functionality. The real-world model is developed similar to its virtual counterpart and is tested and validated for its functionality. The local path planner implemented is analyzed quantitatively for both the real-world and virtual models. The differences in design and development are analyzed and identified. To conclude, the research has lead to the development of a virtual and real-world model of an autonomous wheelchair that has been tested and validated in both the environments

Unlimited-wokspace teleoperation

Thesis (Master)--Izmir Institute of Technology, Mechanical Engineering, Izmir, 2012Includes bibliographical references (leaves: 100-105)Text in English; Abstract: Turkish and Englishxiv, 109 leavesTeleoperation is, in its brief description, operating a vehicle or a manipulator from a distance. Teleoperation is used to reduce mission cost, protect humans from accidents that can be occurred during the mission, and perform complex missions for tasks that take place in areas which are difficult to reach or dangerous for humans. Teleoperation is divided into two main categories as unilateral and bilateral teleoperation according to information flow. This flow can be configured to be in either one direction (only from master to slave) or two directions (from master to slave and from slave to master). In unlimited-workspace teleoperation, one of the types of bilateral teleoperation, mobile robots are controlled by the operator and environmental information is transferred from the mobile robot to the operator. Teleoperated vehicles can be used in a variety of missions in air, on ground and in water. Therefore, different constructional types of robots can be designed for the different types of missions. This thesis aims to design and develop an unlimited-workspace teleoperation which includes an omnidirectional mobile robot as the slave system to be used in further researches. Initially, an omnidirectional mobile robot was manufactured and robot-operator interaction and efficient data transfer was provided with the established communication line. Wheel velocities were measured in real-time by Hall-effect sensors mounted on robot chassis to be integrated in controllers. A dynamic obstacle detection system, which is suitable for omnidirectional mobility, was developed and two obstacle avoidance algorithms (semi-autonomous and force reflecting) were created and tested. Distance information between the robot and the obstacles was collected by an array of sensors mounted on the robot. In the semi-autonomous teleoperation scenario, distance information is used to avoid obstacles autonomously and in the force-reflecting teleoperation scenario obstacles are informed to the user by sending back the artificially created forces acting on the slave robot. The test results indicate that obstacle avoidance performance of the developed vehicle with two algorithms is acceptable in all test scenarios. In addition, two control models were developed (kinematic and dynamic control) for the local controller of the slave robot. Also, kinematic controller was supported by gyroscope

A snake-based scheme for path planning and control with constraints by distributed visual sensors

YesThis paper proposes a robot navigation scheme using wireless visual sensors deployed in an environment. Different from the conventional autonomous robot approaches, the scheme intends to relieve massive on-board information processing required by a robot to its environment so that a robot or a vehicle with less intelligence can exhibit sophisticated mobility. A three-state snake mechanism is developed for coordinating a series of sensors to form a reference path. Wireless visual sensors communicate internal forces with each other along the reference snake for dynamic adjustment, react to repulsive forces from obstacles, and activate a state change in the snake body from a flexible state to a rigid or even to a broken state due to kinematic or environmental constraints. A control snake is further proposed as a tracker of the reference path, taking into account the robot’s non-holonomic constraint and limited steering power. A predictive control algorithm is developed to have an optimal velocity profile under robot dynamic constraints for the snake tracking. They together form a unified solution for robot navigation by distributed sensors to deal with the kinematic and dynamic constraints of a robot and to react to dynamic changes in advance. Simulations and experiments demonstrate the capability of a wireless sensor network to carry out low-level control activities for a vehicle.Royal Society, Natural Science Funding Council (China

Dynamic obstacles avoidance algorithms for unmanned ground vehicles

En las últimas décadas, los vehículos terrestres no tripulados (UGVs) están siendo cada vez más empleados como robots de servicios. A diferencia de los robots industriales, situados en posiciones fijas y controladas, estos han de trabajar en entornos dinámicos, compartiendo su espacio con otros vehículos y personas. Los UGVs han de ser capaces de desplazarse sin colisionar con ningún obstáculo, de tal manera que puedan asegurar tanto su integridad como la del entorno. En el estado del arte encontramos algoritmos de navegación autónoma diseñados para UGVs que son capaces de planificar rutas de forma segura con objetos estáticos y trabajando en entornos parcialmente controlados. Sin embargo, cuando estos entornos son dinámicos, se planifican rutas más peligrosas y que a menudo requieren de un mayor consumo de energía y recursos, e incluso pueden llegar a bloquear el UGV en un mínimo local. En esta tesis, la adaptación de algunos algoritmos disponibles en el estado del arte para trabajar en entornos dinámicos han sido planteados. Estos algoritmos incluyen información temporal tales como los basados en arcos de curvatura (PCVM y DCVM) y los basados en ventanas dinámicas (DW4DO y DW4DOT). Además, se ha propuesto un planificador global basado en Lattice State Planner (DLP) que puede resolver situaciones donde los evitadores de obstáculos reactivos no funcionan. Estos algoritmos han sido validados tanto en simulación como en entornos reales, utilizando distintas plataformas robóticas, entre las que se incluye un robot asistente (RoboShop) diseñado y construido en el marco de esta tesis

Trajectory Servoing: Image-Based Trajectory Tracking Using SLAM

This paper describes an image based visual servoing (IBVS) system for a nonholonomic robot to achieve good trajectory following without real-time robot pose information and without a known visual map of the environment. We call it trajectory servoing. The critical component is a feature-based, indirect SLAM method to provide a pool of available features with estimated depth, so that they may be propagated forward in time to generate image feature trajectories for visual servoing. Short and long distance experiments show the benefits of trajectory servoing for navigating unknown areas without absolute positioning. Trajectory servoing is shown to be more accurate than pose-based feedback when both rely on the same underlying SLAM system