Achieving intelligence in mobility

This paper presents an integrated approach to the application of machine learning tasks that can be observed throughout a number of typical applications of mobile robots and puts those tasks into persepective with respect to both existing and newly developed learning techniques. The actual realization of the approach has been carried out on the two mobile robots PRIAMOS and TESEO, which are both operating in a real office environment. In this context, several experimental results are presented. This paper appeared in: IEEE-Expert: Special Track on Intelligent Robotic Systems, Vol. 10, No. 2, April 1995

Fuzzy behaviors for mobile robot navigation: design, coordination and fusion

AbstractThe implementation of complex behavior generation for artificial systems can be overcome by decomposing the global tasks into simpler, well-specified behaviors which are easier to design and can be tuned independently of each other. Robot behavior can be implemented as a set of fuzzy rules which mimic expert knowledge in specific tasks in order to model expert knowledge. These behaviors are included in the lowest level of a hybrid deliberative–reactive architecture which is aimed at an efficient integration of planning and reactive control. In this work, we briefly present the architecture and attention is focused on the design, coordination and fusion of the elementary behaviors. The design is based on regulatory control using fuzzy logic control and the coordination is defined by fuzzy metarules which define the context of applicability for each behavior. Regarding action fusion, two combination methods for fusing the preferences from each behavior are used in the experiments. In order to validate the system, several measures are also proposed, and thus the performance of the architecture and combination/arbitration algorithms have been demonstrated in both the simulated and the real world. The robot achieves every control objective and the trajectory is smooth in spite of the interaction between several behaviors, unexpected obstacles and the presence of noisy data. When the results of the experimentation from both methods are taken into account, the influence of the combination method appears to be of prime importance when attempting to achieve the best trade-off among the preferences of every behavior

Modeling and Simulation of Elementary Robot Behaviors using Associative Memories

International audienceToday, there are several drawbacks that impede the necessary and much needed use of robot learning techniques in real applications. First, the time needed to achieve the synthesis of any behavior is prohibitive. Second, the robot behavior during the learning phase is – by definition – bad, it may even be dangerous. Third, except within the lazy learning approach, a new behavior implies a new learning phase. We propose in this paper to use associative memories (self-organizing maps) to encode the non explicit model of the robot-world interaction sampled by the lazy memory, and then generate a robot behavior by means of situations to be achieved, i.e., points on the self-organizing maps. Any behavior can instantaneously be synthesized by the definition of a goal situation. Its performance will be minimal (not necessarily bad) and will improve by the mere repetition of the behavior

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