Juan C. Grieco

A six-legged climbing robot for high payloads

This paper describes the design and control concepts of a wall-climbing robot. It has an hexapod configuration and it is able to manoeuvre on vertical surfaces carrying high payloads. Configuration and leg design criteria specific for climbing tasks are discussed. The controller architecture showing decentralised parallel control and hard real-time performance is outlined. New stability criteria for wall locomotion are introduced and a climbing gait using force distribution shows the working of our control scheme for wall gait generation. We call this four phase discontinuous sawing gait. This prototype is an example of a climber specifically tailored for industrial applications.

Towards the development of knee prostheses: review of current researches

Purpose – The aim is to set a state‐of‐the‐art in scientific research towards the development of knee prostheses for transfemoral amputees, by reviewing the literature in the field and by identifying different scientific research lines that have brought out through the years. Also, to provide the information about possible outcomes in the near future, and their links to cybernetics, given the present trends in the field.Design/methodology/approach – Literature related to scientific research carried out up‐to‐date in the field of knee prostheses, is reviewed in scientific articles, books and electronic sources. Then, different research lines are identified from the obtained information, and finally classified as presented in this work.Findings – Three scientific research lines regarding the development of knee prostheses were found, each one dealing with: the design of knee prostheses; the performance assessment of these prostheses; and the creation of control strategies for these prostheses which use elec...

Development of a low cost inertial measurement unit for UAV applications with Kalman Filter based attitude determination

This paper presents the development of an inertial measurement unit (IMU) specially designed for unmanned aerial vehicles (UAV) applications. The design was intended to be a low cost solution of high performance for robotic applications using 3-axis accelerometers and 3-axis gyroscopes MEMS sensors. We present simultaneous sampling to avoid the loss of orthogonality of the inertial measurements due to multiplexed data acquisition commonly used in low cost IMUs, as well as anti-aliasing processing. The IMU was implemented in two boards to separate the sensors from the processing hardware in order to be able to use it with different sets of sensors. The sensor fusion algorithm for attitude determination is based on the Kalman Filter. As testing process, the IMU was installed in a 2-DOF helicopter and the results were compared with those obtained from the encoders for the pitch and roll angles. We also present the results of the IMU installed in a T-REX 450 scale helicopter inside a motion analysis laboratory, using a custom-design safety stand that supports the helicopter allowing only its rotational 3-DOF (roll, pitch and yaw movements). Those demanding experiences tested the IMU performance under true UAV conditions and the results exhibited a maximum RMS error of 4°.

Through the development of a biomechatronic knee prosthesis for transfemoral amputees: Mechanical design and manufacture, human gait characterization, intelligent control strategies and tests

This paper presents the development of a biomechatronic knee prosthesis for transfemoral amputees. This kind of prostheses are considered ‘intelligent’ because they are able to automatically adapt their response at the knee axis, as a natural knee does. This behavior is achieved by characterizing the amputees gait through the signals captured with instrumentation of a prosthesis, which provides feedback about its current state along the gait cycle and therefore responds with the corresponding control action. In this case, unlike other commercially available intelligent knee prostheses, gait cycle characterization is based on accelerometers signals processed by an events detection algorithm. Two intelligent control strategies are presented: a bio-inspired approach, that consists of using a central pattern generator to generate a knee angle reference to be followed by the prosthesis during walking, and an adaptive scheme, that applies a control action proportional to the knee angle according to an auto-adaptive parameter dependant on gait speed. The mechanical design of the prosthesis is also presented, showing the knee joint mechanism and part of the manufacturing process. Results obtained from walking tests with both able body and amputees are shown, demonstrating the positive performance of the prosthesis in several aspects. Future works aimed at a finished product are also stated.

Vision-Based Dynamic Velocity Field Generation for Mobile Robots

A control strategy much studied in the last years is the velocity field control (VFC). In 1995, velocity fields are defined as a set of velocity vectors located at each possible position of the robot platform inside its workspace coding a specified task [1]. The employment of a control scheme whose reference is a velocity field has allowed to encourage different control problems rather than the conventional timed trajectory tracking, such as contour following, position control, etc.

Gait synthesis and modulation for quadruped robot locomotion using a simple feed-forward network

This paper describes a technique for statically stable gait synthesis for a quadruped robot using a simple Feed Forward Neural Networks (FFNN). A common approach for gait synthesis based on neural networks, is to use an implementation with Continuous Time Recurrent Neural Network (CTRNN) of arbitrary complex architecture as pattern generator for rhythmic limb motion. The preferred training method is implemented using genetic algorithms (GAs). However, to achieve the desired trajectory becomes an obstacle during the training process. This paper presents a much more simpler process converting a statically stable gait into actuators space via inverse kinematics; the training of the network is done with those references. By doing so, the training problem becomes a spatio-temporal machine learning problem. It is described a solution for trajectory generation combining a simple oscillator model with a Multilayer Feedforward Neural Network (MFNN) to generate the desired trajectory.

Object recognition for obstacle avoidance in mobile robots

In this paper is shown an obstacle avoidance strategy based on object recognition using an artificial vision application. Related works focus on the implementation of efficient algorithms for image processing. This work emphasizes in using minimum information from an image in order to generate free obstacles trajectories. The algorithm used is based on Pattern Matching for detection of the robot and Classification for the rest of objects. Each form of detection has its particular algorithm: Cross Correlation for Pattern matching and Nearest Neighbor for Classification. The objective pursued is to demonstrate that, with a very simple system, precise information can be provided to a navigation system in order to find free obstacle paths.

Gait Synthesis in Legged Robot Locomotion using a CPG-Based Model

Biology has always been a source of inspiration and ideas for the robotics community. Legged locomotion problem is not an exception, and many experiences have taken ideas from animals, both for morphological and behavioral issues. The first ideas for gait generation came from animal observation, but they were mainly focused on mimicking legs movements. It was not until the nineties that the first relevant works appeared trying to identify the principles behind the generation of those movements in animals. The proposed models were based on neurophysiologic principles, and most of them tried to include characteristics of animal locomotion by the addition of neural networks, dynamic oscillators, or using a set of “movement rules”. Although many models have been suggested, most of them share some common aspects: 1. Motion signals generation and processing are very slow and highly distributed processes. 2. The brain tends to perform high level feed-forward movement control and prediction. 3. The locomotion system has local feedback, from pressure sensors, force sensors, intramuscular sensors, etc. In some processes these characteristics are obvious, like in the heart beating or breathing. In these processes there is no need for the intervention of a complex processing unit like the brain, since most of the coordinated oscillatory behavior of the muscles is carried out locally and distributed. The oscillatory nature of locomotion patterns has attracted studies about the existence of a similar structure in charge of this problem. The biologic and electrochemical bases of the system in animals are fairly well explained in the works on neural networks by Hodgkin-Huxley (Hodgkin, 1952). Another important characteristic of animal systems is that biological neural networks can perform timing tasks through oscillatory networks, and also can modulate neuromuscular excitatory signals, thus giving the ability to

Analysis of Oscillators for the Generation of Rhythmic Patterns in Legged Robot Locomotion

This paper presents a study of several oscillators for the generation of rhythmic patterns that are used in the locomotion of legged robots using a central pattern generator (CPG). This work also shows a comparative analysis of these oscillators focused on their implementation as part of the CPG system, based on simulations made using software tools. Three models were implemented and simulated: the Hopf model as an example of a harmonic oscillator, a reflexive oscillator using the Van der Pol oscillator, and the ACPO (amplitude coupled phase oscillator) corresponding to the phase oscillators category. It is established that a phase oscillator is the best choice when working with CPGs offering a simple yet versatile way to connect the limbs.

Cybernetic knee prosthesis: application of an adaptive central pattern generator

Purpose – The purpose of this paper is to generate a virtual knee angle reference to be followed by a knee prosthesis control, using an adaptive central pattern generator (CPG). Also, to study the feasibility of this approach to implement a continuous control strategy on the prosthesis.Design/methodology/approach – A CPG based on amplitude controlled phase oscillators (ACPOs) to track the current percentage of gait cycle on the prosthesis is proposed. Then, the virtual knee angle reference is generated along gait cycle, by interpolation with the corresponding angle of a sound knee. The structure and coupling of the CPG, as well as the control strategy are presented.Findings – The coupling of the CPG with real gait on the prosthesis was proven, regardless of gait speed. Also, it was found that the maximum knee angle reached during walking is proportional to gait speed. Finally, generation of virtual knee angle reference to be followed by a prosthesis is demonstrated.Research limitations/implications – As o...