Dynamic Path Planning and Replanning for Mobile Robots using RRT*

It is necessary for a mobile robot to be able to efficiently plan a path from its starting, or current, location to a desired goal location. This is a trivial task when the environment is static. However, the operational environment of the robot is rarely static, and it often has many moving obstacles. The robot may encounter one, or many, of these unknown and unpredictable moving obstacles. The robot will need to decide how to proceed when one of these obstacles is obstructing it's path. A method of dynamic replanning using RRT* is presented. The robot will modify it's current plan when an unknown random moving obstacle obstructs the path. Various experimental results show the effectiveness of the proposed method

Development of Navigation Control Algorithm for AGV Using D* Search Algorithm

In this paper, we present a navigation control algorithm for Automatic Guided Vehicles (AGV) that move in industrial environments including static and moving obstacles using D* algorithm. This algorithm has ability to get paths planning in unknown, partially known and changing environments efficiently. To apply the D* search algorithm, the grid map represent the known environment is generated. By using the laser scanner LMS-151 and laser navigation sensor NAV-200, the grid map is updated according to the changing of environment and obstacles. When the AGV finds some new map information such as new unknown obstacles, it adds the information to its map and re-plans a new shortest path from its current coordinates to the given goal coordinates. It repeats the process until it reaches the goal coordinates. This algorithm is verified through simulation and experiment. The simulation and experimental results show that the algorithm can be used to move the AGV successfully to reach the goal position while it avoids unknown moving and static obstacles. [Keywords— navigation control algorithm; Automatic Guided Vehicles (AGV); D* search algorithm

Dos agentes con algoritmos GBFS trabajando de forma cooperativa para obtener la ruta más corta

This study is carried out in order to verify if the implementation of the concept of cooperative work among two agents, that use path planners A* to obtain the shortest path (previous work of the authors) is also valid when the cooperative strategy is applied using another path planner such as the so-called GBFS (Greedy Best First Search). In this sense, this paper shows a path planning strategy that combines the capabilities of two Agents each one with its own path planner GBFS (slightly different from each other) in order to obtain the shortest path. The comparisons between paths are made by analyzing the behavior and results obtained from the agents operating in different forms: (1) Working individually; (2) Working as a team (cooperating and exchanging information). The results show that in all analyzed situations are obtained shortest traveled distances when the path planners work as a cooperative team.Este estudio se lleva a cabo con el fin de verificar si la implementación del concepto de trabajo cooperativo entre dos agentes, usado con planificadores A* para obtener la ruta más corta (trabajo previo de los autores) también es válida cuando la estrategia cooperativa es aplicada usando otro planificador de rutas como el llamado GBFS (Greedy Best First Search). En este sentido, el articulo muestra una estrategia de planificación de rutas que combina las capacidades de dos agentes cada uno con su propio planificador de rutas GBFS (ligeramente diferentes entre sí) para obtener la ruta más corta. La comparación entre las dos rutas se realiza analizando el comportamiento y comparando los resultados obtenidos para cada uno de los que operan en diferentes formas: (1) Trabajando individualmente; (2) Trabajando como un equipo (cooperando e intercambiando información). Los resultados muestran que para todos los casos analizados se obtiene la distancia recorrida más corta cuando los planificadores de ruta trabajan como un equipo colaborativo

Collided path replanning in dynamic environments using RRT and Cell decomposition algorithms

The motion planning is an important part of robots’ models. It is responsible for robot’s movements. In this work, the cell decomposition algorithm is used to find a spatial path on preliminary static workspaces, and then, the rapidly exploring random tree algorithm (RRT) is used to validate this path on the actual workspace. Two methods have been proposed to enhance the omnidirectional robot’s navigation on partially changed workspace. First, the planner creates a RRT tree and biases its growth toward the path’s points in ordered form. The planner reduces the probability of choosing the next point when a collision is detected, which in turn increases the RRT’s expansion on the free space. The second method uses a straight planner to connect path’s points. If a collision is detected, the planner places RRTs on both sides of the collided segment. The proposed methods are compared with the others approaches, and the simulation shows better results in term of efficiency and completeness.Plánování pohybu robota je důležitou součástí modelování funkcí robotů. Plán řídí pohyby robota. V této práci se algoritmus rozkladu na buňky používá k nalezení cesty pracovní plochou a algoritmus prozkoumání náhodného stromu (RRT) k ověření cesty skutečným prostorem. Byly navrženy dvě metody ke zlepšení navigace všesměrové pohyblivého robota částečně změněnou pracovní plochou. Za prvé, plánovač vytvoří RRT strom a vychyluje jeho růst směrem k bodu na cestě. Plánovač snižuje pravděpodobnost výběru dalšího bodu, když je detekována kolize, což zase zvyšuje expanzi RRT na volném prostoru. Druhá metoda používá shodný plánovač pro napojení bodů cesty. Pokud je detekována kolize, plánovač upravuje RRT na obou stranách kolizního segmentu. Navrhované metody jsou porovnávány s dalšími používanými přístupy, přečemž simulace ukazuje lepší výsledky z hlediska účinnosti a úplnosti plánování cesty.The motion planning is an important part of robots’ models. It is responsible for robot’s movements. In this work, the cell decomposition algorithm is used to find a spatial path on preliminary static workspaces, and then, the rapidly exploring random tree algorithm (RRT) is used to validate this path on the actual workspace. Two methods have been proposed to enhance the omnidirectional robot’s navigation on partially changed workspace. First, the planner creates a RRT tree and biases its growth toward the path’s points in ordered form. The planner reduces the probability of choosing the next point when a collision is detected, which in turn increases the RRT’s expansion on the free space. The second method uses a straight planner to connect path’s points. If a collision is detected, the planner places RRTs on both sides of the collided segment. The proposed methods are compared with the others approaches, and the simulation shows better results in term of efficiency and completeness

Dos agentes con algoritmos GBFS trabajando de forma cooperativa para obtener la ruta más corta

This study is carried out in order to verify if the implementation of the concept of cooperative work among two agents, that use path planners A* to obtain the shortest path (previous work of the authors) is also valid when the cooperative strategy is applied using another path planner such as the so-called GBFS (Greedy Best First Search). In this sense, this paper shows a path planning strategy that combines the capabilities of two Agents each one with its own path planner GBFS (slightly different from each other) in order to obtain the shortest path. The comparisons between paths are made by analyzing the behavior and results obtained from the agents operating in different forms: (1) Working individually; (2) Working as a team (cooperating and exchanging information). The results show that in all analyzed situations are obtained shortest traveled distances when the path planners work as a cooperative team.Este estudio se lleva a cabo con el fin de verificar si la implementación del concepto de trabajo cooperativo entre dos agentes, usado con planificadores A* para obtener la ruta más corta (trabajo previo de los autores) también es válida cuando la estrategia cooperativa es aplicada usando otro planificador de rutas como el llamado GBFS (Greedy Best First Search). En este sentido, el articulo muestra una estrategia de planificación de rutas que combina las capacidades de dos agentes cada uno con su propio planificador de rutas GBFS (ligeramente diferentes entre sí) para obtener la ruta más corta. La comparación entre las dos rutas se realiza analizando el comportamiento y comparando los resultados obtenidos para cada uno de los que operan en diferentes formas: (1) Trabajando individualmente; (2) Trabajando como un equipo (cooperando e intercambiando información). Los resultados muestran que para todos los casos analizados se obtiene la distancia recorrida más corta cuando los planificadores de ruta trabajan como un equipo colaborativo

RobRob oCup SSL における中間目標点を用いた移動時推定法に関する研究

2013年度卒業論文要旨, 情報科学部情報科学