Antonio Guerrero-González

A Neural Tactile Architecture Applied to Real-time Stiffness Estimation for a Large Scale of Robotic Grasping Systems

This paper presents a model for solving the problem of real-time neural estimation of stiffness characteristics for unknown objects. For that, an original neural architecture is proposed for a large scale robotic grasping systems applied for unknown object with unspecified stiffness characteristics. The force acquisition is based on tactile information from force sensors in robotic manipulator. The proposed model has been implemented on a robotic gripper with two parallel fingers and on a one d.o.f. robotic finger with opponent artificial muscles and angular displacements. This self-organized model is inspired of human biological system, and is carried out by means of Topographic Maps and Vector Associative Maps. Experimental results demonstrate the efficiency of this new approach.

A neural model for visual-tactile-motor integration in robotic reaching and grasping tasks

This paper presents a neural model to solve the visual-tactile-motor coordination problem in robotic applications. The proposed neural controller is based on the VAMC (Vector Associative Map) model. This algorithm is based on the human biological system and has the ability of learning the mapping that establishes the relationship between the spatial and the motor coordinates. These spatial inputs are composed of visual and force parameters. The LINCE stereohead carries out a visual detection process, detecting the positions of the object and of the manipulator. The artificial tactile skins placed over the two fingers of the gripper measure the force distribution when an object is touched. The neural controller has been implemented for robotic operations of reaching and object grasping. The reaching process is fed back in order to minimize the Difference Vector (DV) between the visual projections of the object and the manipulator. The stable grasping task processes the force distribution maps detected in the contact with the two surfaces of the gripper, in order to direct the object into the robotic fingers. Experimental results have demonstrated the robustness of the model and the accuracy of the final pick-and-place process.

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.

A neural controller for a robotic hand with artificial tactile skins in grasping tasks

In this paper, artificial tactile skins have been applied to a grasping task which require a certain precision in the determination of the object contact position with the surfaces and the pressure exercised in each point of the same. This design allows to process the force distribution maps in order to do precise maneuvers of grasping.

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 biologically inspired neural network for autonomous underwater vehicles

Autonomous underwater vehicles (AUVs) have great advantages for activities in deep oceans, and are expected as the attractive tool for near future underwater development or investigation. However, AUVs have various problems which should be solved for motion control, acquisition of sensors information, behavioral decision, navigation without collision, self-localization and so on. This paper proposes an adaptive biologically inspired neural controller for trajectory tracking of AUVs in nonstationary environment. The kinematic adaptive neuro-controller is an unsupervised neural network, which is termed Self-Organization Direction Mapping Network (SODMN). The network uses an associative learning system to generate transformations between spatial coordinates and coordinates of propellers velocity. The neurobiological inspired control architecture requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robots sensors. The SODMN proposed in this paper represents a simplified way to understand in part the mechanisms that allow the brain to collect sensory input to control adaptive behaviours of autonomous navigation of the animals. The efficiency of the proposed neurobiological inspired controller for autonomous intelligent navigation was implemented on an underwater vehicle capable of operating during large periods of time for observation and monitoring tasks.