A Domain Engineering Process for RFID Systems Development in Supply Chain
Leonardo Barreto, Eduardo Santana de Almeida, Srgio Donizetti, Silvio Romero de Lemos Meir · InTech eBooks · 2009
Will-be-set-by-IN-TECHIn order to build a map of an unknown environment autonomously, this work presents first a exploration and path planning method based on the Logarithm of the Extended Voronoi Transform and the Fast Marching Method.This Path Planner is called Voronoi Fast Marching (8).The Extended Voronoi Transform of an image gives a grey scale that is darker near the obstacles and walls and lighter when far from them.The Logarithm of the Extended Voronoi Transform imitates the repulsive electric potential in 2D from walls and obstacles.This potential impels the robot far from obstacles.The Fast Marching Method has been applied to Path Planning (34), and their trajectories are of minimal distance, but they are not very safe because the path is too close to obstacles and what is more important, the path is not smooth enough.In order to improve the safety of the trajectories calculated by the Fast Marching Method, avoiding unrealistic trajectories produced when the areas are narrower than the robot, objects and walls are enlarged in a security distance that assures that the robot does not collide and does not accept passages narrower than the robot's size.The last step is calculating the trajectory in the image generated by the Logarithm of the Extended Voronoi Transform using the Fast Marching Method.Then, the path obtained verifies the smoothness and safety considerations required for mobile robot path planning.The advantages of this method are the ease of implementation, the speed of the method and the quality of the trajectories.This method is used at a local scale operating with sensor information (sensor based planning).To build the environment map while the robot is carrying out the exploration task, a SLAM (Simultaneous Localization and Modelling) is implemented.The algorithm is based on the stochastic search for solutions in the state space to the global localization problem by means of a differential evolution algorithm.This non linear evolutive filter, called Evolutive Localization Filter (ELF) (23), searches stochastically along the state space for the best robot pose estimate.The set of pose solutions (the population) focuses on the most likely areas according to the perception and up to date motion information.The population evolves using the log-likelihood of each candidate pose according to the observation and the motion errors derived from the comparison between observed and predicted data obtained from the probabilistic perception and motion model.In the remainder of the chapter, the section 3 presents the state of the art referred to exploration and motion planning problems.Section 4 presents our Voronoi Fast Marching (VFM) Motion Planner.The SLAM algorithm is described briefly in Section 5.Then, section 6 describes the specific Exploration method proposed.Next, section 7 demonstrates the performance of the exploration strategy as it explores different environments, according to the two possible ways of working for the exploration task.And, finally the conclusions are summarized in section 8. Previous and related works Representations of the worldRoughly speaking there are two main forms for representing the spatial relations in an environment: metric maps and topological maps.Metric maps are characterized by a representation where the position of the obstacles are indicated by coordinates in a global frame of reference.Some of them represent the environment with grids of points, defining regions that can be occupied or not by obstacles or goals (22) (1).Topological maps represent the environment with graphs that connect landmarks or places with special features (19) (12).In our approach we choose the grid-based map to represent the environment.The clear advantage is that with grids we already have a discrete environment representation and ready to be used in conjunction with the Extended Voronoi Transform function and Fast Marching Method for path planning.The pioneer method for environment representation in 82