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The paper presents a novel method for solving the kinematics problems in real time for fast-moving robot manipulators, animation characters, and hexapod robots. The method uses certain properties of the kinematics map and is based on spatial decomposition, classification with fuzzy logic, and neural network representation of data that are performed during an off-line process. As a result of the preprocessing, the online time for computing the kinematics is extremely small making it possible to perform real-time operations. Examples are provided to demonstrate the performance of the method.

This work presents the kinematic and dynamic modeling of a human–wheelchair system which considers that its center of mass is not located in the middle of the wheel’s axle. Furthermore, a novel motion controller is presented for a human–wheelchair system, which is capable of performing positioning and path-following tasks in human-shared environments. This controller design is based on two cascaded subsystems: a kinematic controller, and a dynamic controller that compensates the dynamics of the human–wheelchair system. Additionally, an algorithm based on fuzzy-logic is proposed and incorporated in the aforementioned path-following control for pedestrian collision avoidance. This methodology considers to quantify heuristics social rules to make a balance between modulating velocity or direction during the avoidance. Three different interference cases, commonly found during walking events, are tested in a structured scenario. The experimental results demonstrate that the system is capable of overcoming many usual interference situations with human obstacles. A good performance of the path-following control is also verified.