A fault detection and isolation system for cooperative manipulators

The problem of fault detection and isolation (FDI) in cooperative manipulators is addressed in this paper. Four FDI procedures are developed to deal with free-swinging joint faults, locked joint faults, incorrectly measured joint position, and incorrectly measured joint velocity. Free-swinging and locked joint faults are isolated via neural networks. For each arm, a Multilayer Perceptron (MLP) is used to reproduce the dynamics of the fault-free robot. The outputs of each MLP are compared to the actual joint velocities in order to generate a residual vector which is then classified by an RBF network. The remaining faults are isolated based on the kinematic constraints imposed on the cooperative system. Results obtained via simulations and via an actual cooperative manipulator robot are presented

Inverse Kinematics and Trajectory Planning Analysis of a Robotic Manipulator

In this work, we pretended to show and compare three methodologies used to solve the inverse kinematics of a 3 DOF robotic manipulator. The approaches are the algebraic method through Matlabreg; solve function, Genetic Algorithms (GAs), Artificial Neural Networks (ANNs). Another aspect considered is the trajectory planning of the manipulator, which allows the user to control the desired movement in the joint space. We compare polynomials of third, fourth and fifth orders for the solution of the chosen coordinates. The results show that the ANN method presented best results due to its configuration to show only feasible joint values, as also do the GA. In the trajectory planning the analysis lead to the fifth-order polynomial, which showed the smoothest solution

Development of an Adaptive Algorithm for Solving the Inverse Kinematics Problem for Serial Robot Manipulators

In order to overcome the drawbacks of some control schemes, which depends on modeling the system being controlled, and to overcome the problem of inverse kinematics which are mainly singularities and uncertainties in arm configuration. Artificial Neural Networks (ANN) technique has been utilized where learning is done iteratively based only on observation of input-output relationship. The proposed technique does not require any prior knowledge of the kinematics model of the system being controlled; the main idea of this approach is the use of an Artificial Neural Network to learn the robot system characteristics rather than having to specify an explicit robot system model.Since one of the most important problems in using Artificial Neural Networks, is the choice of the appropriate networks' configuration, two different networks' configurations were designed and tested, they were trained to learn desired set of joint angles positions from a given set of end effector positions. Experimental results have shown better response for the first configuration network in terms of precision and iteration. The developed approach possesses several distinct advantages; these advantages can be listed as follows :(First) system model does not have to be known at the time of the controller design, (Second) any change in the physical setup of the system such as the addition of a new tool would only involve training and will not require any major system software modifications, and (Third) this scheme would work well in a typical industrial set-up where the controller of a robot could be taught the handful of paths depending on the task assigned to that robot. The efficiency of the proposed algorithm is demonstrated through simulations of a general 6 D.O.F. serial robot manipulato

Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios