Francisco García-Córdova

Intelligent Navigation for a Solar Powered Unmanned Underwater Vehicle

In this paper, an intelligent navigation system for an unmanned underwater vehicle powered by renewable energy and designed for shadow water inspection in missions of a long duration is proposed. The system is composed of an underwater vehicle, which tows a surface vehicle. The surface vehicle is a small boat with photovoltaic panels, a methanol fuel cell and communication equipment, which provides energy and communication to the underwater vehicle. The underwater vehicle has sensors to monitor the underwater environment such as sidescan sonar and a video camera in a flexible configuration and sensors to measure the physical and chemical parameters of water quality on predefined paths for long distances. The underwater vehicle implements a biologically inspired neural architecture for autonomous intelligent navigation. Navigation is carried out by integrating a kinematic adaptive neuro-controller for trajectory tracking and an obstacle avoidance adaptive neuro- controller. The autonomous underwater vehicle is capable of operating during long periods of observation and monitoring. This autonomous vehicle is a good tool for observing large areas of sea, since it operates for long periods of time due to the contribution of renewable energy. It correlates all sensor data for time and geodetic position. This vehicle has been used for monitoring the Mar Menor lagoon.

Design of an anthropomorphic finger using shape memory alloy springs

We present the design of an anthropomorphic finger prototype. In this artificial finger, the actuators are electric pistons, whose main component is a shape memory alloy (SMA) spring. The artificial finger presents three independent degrees of freedom (DOF) for the metacarpophalangeal, proximal interphalangeal, and distal interphalangeal joints, respectively. The paper outlines the kinematic and structural characteristics of the finger. The main goal pursued during the development of the finger has been that of designing a small and lightweight dextrous gripper with anthropomorphic kinematics, which could be easily ported and installed even on small robot hands. We propose to use a physical anthropomorphic finger to demonstrate and validate a neural controller based on biological models. The neural controller applies a strategy of trajectory control using the vector integration to endpoint (VITE) model, which exhibits key kinematic properties of human movements, including asymmetric bell-shaped velocity profiles. The VITE model is used to compute the desired joint movement trajectories by smoothly interpolating between initial and final muscle length commands for the antagonist muscles involved in the movement. The rate of interpolation is controlled by the product of a difference vector which continuously computes the difference between the desired and present position of the finger, and a volitional movement gating signal. Experimental performance results in the time domain are presented, and directions for future research are discussed.

Position control based on static neural networks of anthropomorphic robotic fingers

A position neurocontroller for robot manipulators with a tendon-driven transmission system has been developed allowing to track desired trajectories and reject external disturbances. The main problem to control tendons proceeds from the different dimensions between the joint and the tendon spaces. In order to solve this problem we propose a static neural network in cascade with a torque resolutor. The position controller is built as a parametric neural network by using basis functions obtained directly from the finger structure. This controller insure that the tracking error converges to zero and the weights of the network are bounded. The implementation has been improved partitioning the neural network into subnets and using the Kronecker product. Both control and weight updating laws have been designed by means of a Lyapunov energy function. In order to improve the computational efficient of the neural network, this has been split up into subnets to compensate inertial, Coriolis/centrifugal and gravitational effects. The NN weights are initialised at zero and tuned on-line with no ”off-line learning phase”. This scheme has been applied to an anthropomorphic robotic finger with a transmission system based on tendons.

Emulation of the animal muscular actuation system in an experimental platform

This research work involves the design and implementation of an efficient biomechanical model of the animal muscular actuation system. In order to build the biomechanical system to have mechanical properties as close as possible to the human or animal arm, auto-reversible DC motors with appropriate planetary gearboxes and multi-radial flexible couplings (in order to pull and to be pushed), force and position sensors, and tendons are integrated in the system. In this system the implementation of mathematical models of muscle in a whole skeletal muscle force generation on DC motors was carried out. Experimental results show the actuation system has the basic properties of the animal musculoskeletal system. This properties are the force-length and force-velocity relationships.

Design and Implementation of an Adaptive Neuro-controller for Trajectory Tracking of Nonholonomic Wheeled Mobile Robots

A kinematic adaptive neuro-controller for trajectory tracking of nonholonomic mobile robots is proposed. The kinematic adaptive neuro-controller is a real-time, unsupervised neural network that learns to control a nonholonomic mobile robot in a nonstationary environment, which is termed Self-Organization Direction Mapping Network (SODMN), and combines associative learning and Vector Associative Map (VAM) learning to generate transformations between spatial and velocity coordinates. The transformations are learned in an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The robot learns the relationship between these velocities and the resulting incremental movements. The neural network requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robots sensors. The efficacy of the proposed neural architecture is tested experimentally by a differentially driven mobile robot.

A neural network for target reaching with a robot arm using a stereohead

A self-organizing neural controller for stereohead-robot arm coordination is presented. This neural controller is coupled with a stereohead which implements several neural networks for target representation and control. This control algorithm is based in the DIRECT algorithm which has been developed from a biological inspiration. With this controller a solution to the motor equivalence problem is given. During the initial phase, the model endogenously generates movement commands and activates a correlation process between visual, spatial and motor information that are used to learn its internal coordinate transformations. After learning occurs, the controller is capable of making reaching movements of the arm to prescribed spatial targets using many different combinations of joints. Properties of the controller are compared with psychophysical data on human reaching movements.

A multirobot platform based on autonomous surface and underwater vehicles with bio-inspired neurocontrollers for long-term oil spills monitoring

This paper describes the BUSCAMOS-Oil monitoring system, which is a robotic platform consisting of an autonomous surface vessel combined with an underwater vehicle. The system has been designed for the long-term monitoring of oil spills, including the search for the spill, and transmitting information on its location, extent, direction and speed. Both vehicles are controlled by two different types of bio-inspired neural networks: a Self-Organization Direction Mapping Network for trajectory generation and a Neural Network for Avoidance Behaviour for avoiding obstacles. The systems’ resilient capabilities are provided by bio-inspired algorithms implemented in a modular software architecture and controlled by redundant devices to give the necessary robustness to operate in the difficult conditions typically found in long-term oil-spill operations. The efficacy of the vehicles’ adaptive navigation system and long-term mission capabilities are shown in the experimental results.

A neural estimator of object stiffness applied to force control of a robotic finger with opponent artificial muscles

We present a solution for real-time neural estimation of the stiffness characteristics of objects which are pressed with a predefined force threshold by an anthropomorphic robotic finger provided with opponent movement of their artificial muscles. The proposed architecture links three neural models in order to satisfy the requirements in our control system. This model based on adaptive learning allows the controller to grasp any object with different stiffness characteristics in a smooth way and with the desired final force.

A Biologically Inspired Neural Network for Solar Powered Autonomous Surface Vehicles

This paper describes a neural network model for the reactive behavioural navigation of an autonomous surface vehicle (ASV) in which an innovative, neurobiological inspired sensing control system and a hardware architectures are being implemented. The ASV is used to power and support for a Unmanned Underwater Vehicle (UUV), which incorporates several types of environmental and oceanographic instruments such as CTD sensors, chlorophyll, turbidity, optical dissolved oxygen (YSI V6600 sonde) and nitrate analyzer (SUNA) together with ADCP, side scan sonar and video camera. The ASV gets its energy through solar photovoltaic modules, also has automatic devices for the deployment and collection of underwater robots. Navigation system contains accelerometers, gyroscopes, magnetometers and GPS, to reach an appropriate level of spatial location at all times, and corrects trajectory using a neural control algorithm to process the corresponding corrections.

Re-construction of an AUV as a platform for oceanographic research and a test bed for implementation of control systems based on neurobiological networks

This work considers the re-design of an autonomous underwater vehicle (AUV) in which an innovative, neurobiological inspired sensorization control system is being implemented. Hardware architecture and sensorization control software are being developed to allow autonomous navigation procedures for submarine vehicles. After the refurbishment of the vehicle and the update of its control system, the ROV is able to load CTD sensors, chlorophyll, turbidity, optical dissolved oxygen (YSI V6600 sonde) and nitrate analyzer (SUNA) together with ADCP, side scan sonar and video camera, in a flexible configuration to provide a water quality monitoring platform with mapping capabilitiesi.