Neuro-Fuzzy Motion Planning for Robotic Manipulators

On-going research efforts in robotics aim at providing mechanical systems, such as robotic manipulators and mobile robots, with more intelligence so that they can operate autonomously. Advancing in this direction, this thesis proposes and investigates novel manipulator path planning and navigation techniques which have their roots in the field of neural networks and fuzzy logic. Path planning in the configuration space makes necessary a transformation of the workspace into a configuration space. A radial-basis-function neural network is proposed to construct the configuration space by repeatedly mapping individual workspace obstacle points into so-called C-space patterns. The method is extended to compute the transformation for planar manipulators with n links as well as for manipulators with revolute and prismatic joints. A neural-network-based implementation of a computer emulated resistive grid is described and investigated. The grid, which is a collection of nodes laterally connected by weights, carries out global path planning in the manipulator’s configuration space. In response to a specific obstacle constellation, the grid generates an activity distribution whose gradient can be exploited to construct collision-free paths. A novel update algorithm, the To&Fro algorithm, which rapidly spreads the activity distribution over the nodes is proposed. Extensions to the basic grid technique are presented. A novel fuzzy-based system, the fuzzy navigator, is proposed to solve the navigation and obstacle avoidance problem for robotic manipulators. The presented system is divided into separate fuzzy units which individually control each manipulator link. The competing functions of goal following and obstacle avoidance are combined in each unit providing an intelligent behaviour. An on-line reinforcement learning method is introduced which adapts the performance of the fuzzy units continuously to any changes in the environment. All above methods have been tested in different environments on simulated manipulators as well as on a physical manipulator. The results proved these methods to be feasible for real-world applications