Leonardo Fermín

Fuzzy logic based speed planning for autonomous navigation under Velocity Field Control

Velocity fields techniques are often used in the path planning and control problem in autonomous navigation. Velocity fields are compounded by vectors that can be expressed in polar form, i.e. V<inf>x</inf> = |V→| · cos(α) and V<inf>y</inf> = |V→| · sin(α). In the proposed technique, the problem is divided into two separate problems: the calculation of the orientation angle α, which was treated in a previous work, and the calculation of the linear velocity |V→|. This paper addresses the calculation of |V→| given a trajectory vector field that encodes α, thus generating the speed reference for a Velocity Field Controller. The linear velocity |V→| is generated through a Mamdani-type Fuzzy Inference System, that considers the curl of the trajectory vector field, and an extension of the Voronoi Diagram. Results obtained in both simulations and tests with a real robot show that an adequate intelligent velocity behavior, and a smooth obstacle avoidance are achieved.

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°.

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.

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...

Dynamic Velocity Field Angle Generation for Obstacle Avoidance in Mobile Robots Using Hydrodynamics

In this work, a strategy for the generation of the angle reference for a Velocity Field Controller is presented. For an arbitrary trajectory given in its parametric form, it is possible to create a desired velocity field, that could be modified if obstacles were detected by an artificial vision system. Velocity fields are composed by vectors that can be expressed in polar form, i.e.

Optical Flow Based Velocity Field Control

V_x=|\overrightarrow{V}|\cdot\cos(\alpha)

CONTROL SYSTEMS SIMULATOR FOR WHEELED ROBOTS USING EASY JAVA SIMULATIONS

and

Object Recognition for Obstacles-free Trajectories Applied to Navigation Control

V_y=|\overrightarrow{V}|\cdot\sin(\alpha)