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

Modified spline-based navigation: Guaranteed safety for obstacle avoidance

© 2017, Springer International Publishing AG. Successful interactive collaboration with a human demands mobile robots to have an advanced level of autonomy, which basic requirements include social interaction, real time path planning and navigation in dynamic environment. For mobile robot path planning, potential function based methods provide classical yet powerful solutions. They are characterized with reactive local obstacle avoidance and implementation simplicity, but suffer from navigation function local minima. In this paper we propose a modification of our original spline-based path planning algorithm, which consists of two levels of planning. At the first level, Voronoi-based approach provides a number sub-optimal paths in different homotopic groups. At the second, these paths are optimized in an iterative manner with regard to selected criteria weights. A new safety criterion is integrated into both levels of path planning to guarantee path safety, while further optimization of a safe path relatively to other criteria is secondary. The modified algorithm was implemented in Matlab environment and demonstrated significant advantages over the original algorithm

Local path planning for mobile robots based on intermediate objectives

International audienceThis paper presents a path planning algorithm for autonomous navigation of non-holonomic mobile robots in complex environments. The irregular contour of obstacles is represented by segments. The goal of the robot is to move towards a known target while avoiding obstacles. The velocity constraints, robot kinematic model and non-holonomic constraint are considered in the problem. The optimal path planning problem is formulated as a constrained receding horizon planning problem and the trajectory is obtained by solving an optimal control problem with constraints. Local minima are avoided by choosing intermediate objectives based on the real time environment

A New Hybrid Method in Global Dynamic Path Planning of Mobile Robot

Path planning and real-time obstacle avoidance is the key technologies of mobile robot intelligence. But the efficiency of the global path planning is not very high. It is not easy to avoid obstacles in real time. Aiming at these shortcomings it is proposed that a global dynamic path planning method based on improved A* algorithm and dynamic window method. At first the improved A* algorithm is put forward based on the traditional A* algorithm in the paper. Its optimized heuristic search function is designed. They can be eliminated that the redundant path points and unnecessary turning points. Simulation experiment 1 results show that the planned path length is reduced greatly. And the path transition points are less, too. And then it is focused on the global dynamic path planning of fusion improved A* Algorithm and Dynamic Window Method. The evaluation function is constructed taking into account the global optimal path. The real time dynamic path is planning. On the basis of ensuring the optimal global optimization of the planning path, it is improved that the smoothness of the planning path and the local real-time obstacle avoidance ability. The simulation experiments results show that the fusion algorithm is not only the shorter length, but also the smoother path compared the traditional path planning algorithms with the fusion algorithm in the paper. It is more fit to the dynamics of the robot control. And when a dynamic obstacle is added, the new path can be gained. The barrier can be bypass and the robot is to reach the target point. It can be guaranteed the global optimality of the path. Finally the Turtlebot mobile robot was used to experiment. The experimental results show that the global optimality of the proposed path can be guaranteed by the fusion algorithm. And the planned global path is smoother. When the random dynamic obstacle occurs in the experiment, the robot can be real-time dynamic obstacle avoidance. It can re-plan the path. It can bypass the random obstacle to reach the original target point. The outputting control parameters are more conducive to the robot’s automatic control. The fusion method is used for global dynamic path planning of mobile robots in this paper. In summary the experimental results show that the method is good efficiency and real-time performance. It has great reference value for the dynamic path planning application of mobile robot

Algoritmo de planificación de movimiento para un robot móvil con un sistema de visión artificial inteligente

This study is devoted to the challenges of motion planning for mobile robots with smart machine vision systems. Motion planning for mobile robots in the environment with obstacles is a problem to deal with when creating robots suitable for operation in real-world conditions. The solutions found today are predominantly private, and are highly specialized, which prevents judging of how successful they are in solving the problem of effective motion planning. Solutions with a narrow application field already exist and are being already developed for a long time, however, no major breakthrough has been observed yet. Only a systematic improvement in the characteristics of such systems can be noted. The purpose of this study: develop and investigate a motion planning algorithm for a mobile robot with a smart machine vision system. The research subject for this article is a motion planning algorithm for a mobile robot with a smart machine vision system. This study provides a review of domestic and foreign mobile robots that solve the motion planning problem in a known environment with unknown obstacles. The following navigation methods are considered for mobile robots: local, global, individual. In the course of work and research, a mobile robot prototype has been built, capable of recognizing obstacles of regular geometric shapes, as well as plan and correct the movement path. Environment objects are identified and classified as obstacles by means of digital image processing methods and algorithms. Distance to the obstacle and relative angle are calculated by photogrammetry methods, image quality is improved by linear contrast enhancement and optimal linear filtering using the Wiener-Hopf equation. Virtual tools, related to mobile robot motion algorithm testing, have been reviewed, which led us to selecting Webots software package for prototype testing. Testing results allowed us to make the following conclusions. The mobile robot has successfully identified the obstacle, planned a path in accordance with the obstacle avoidance algorithm, and continued moving to the destination. Conclusions have been drawn regarding the concluded research.Este estudio está dedicado a los desafíos de la planificación del movimiento para robots móviles con sistemas inteligentes de visión artificial. La planificación del movimiento para robots móviles en un entorno con obstáculos es un problema con el que lidiar al crear robots adecuados para operar en condiciones del mundo real. Las soluciones que se encuentran en la actualidad son predominantemente privadas y altamente especializadas, lo que impide juzgar qué tan exitosas son para resolver el problema de la planificación eficaz del movimiento. Ya existen soluciones con un campo de aplicación estrecho y ya se están desarrollando durante mucho tiempo, sin embargo, aún no se han observado avances importantes. Solo se puede observar una mejora sistemática en las características de tales sistemas. El propósito de este estudio: desarrollar e investigar un algoritmo de planificación de movimiento para un robot móvil con un sistema de visión artificial inteligente. El tema de investigación de este artículo es un algoritmo de planificación de movimiento para un robot móvil con un sistema de visión artificial inteligente. Este estudio proporciona una revisión de robots móviles nacionales y extranjeros que resuelven el problema de planificación de movimiento en un entorno conocido con obstáculos desconocidos. Se consideran los siguientes métodos de navegación para robots móviles: local, global, individual. En el transcurso del trabajo e investigación se ha construido un prototipo de robot móvil, capaz de reconocer obstáculos de formas geométricas regulares, así como planificar y corregir la trayectoria del movimiento. Los objetos del entorno se identifican y clasifican como obstáculos mediante métodos y algoritmos de procesamiento de imágenes digitales. La distancia al obstáculo y el ángulo relativo se calculan mediante métodos de fotogrametría, la calidad de la imagen se mejora mediante la mejora del contraste lineal y el filtrado lineal óptimo utilizando la ecuación de Wiener-Hopf. Se han revisado las herramientas virtuales, relacionadas con las pruebas de algoritmos de movimiento de robots móviles, lo que nos llevó a seleccionar el paquete de software Webots para las pruebas de prototipos. Los resultados de las pruebas nos permitieron sacar las siguientes conclusiones. El robot móvil identificó con éxito el obstáculo, planificó una ruta de acuerdo con el algoritmo de evitación de obstáculos y continuó avanzando hacia el destino. Se han extraído conclusiones con respecto a la investigación concluida

Symbiotic Navigation in Multi-Robot Systems with Remote Obstacle Knowledge Sharing

Large scale operational areas often require multiple service robots for coverage and task parallelism. In such scenarios, each robot keeps its individual map of the environment and serves specific areas of the map at different times. We propose a knowledge sharing mechanism for multiple robots in which one robot can inform other robots about the changes in map, like path blockage, or new static obstacles, encountered at specific areas of the map. This symbiotic information sharing allows the robots to update remote areas of the map without having to explicitly navigate those areas, and plan efficient paths. A node representation of paths is presented for seamless sharing of blocked path information. The transience of obstacles is modeled to track obstacles which might have been removed. A lazy information update scheme is presented in which only relevant information affecting the current task is updated for efficiency. The advantages of the proposed method for path planning are discussed against traditional method with experimental results in both simulation and real environments