Semantic Segmentation for Aerial Mapping

Mobile robots commonly have to traverse rough terrains. One way to find the easiest traversable path is by determining the types of terrains in the environment. The result of this process can be used by the path planning algorithms to find the best traversable path. In this work, we present an approach for terrain classification from aerial images while using a Convolutional Neural Networks at the pixel level. The segmented images can be used in robot mapping and navigation tasks. The performance of two different Convolutional Neural Networks is analyzed in order to choose the best architecture

Real-Time Neural Classifiers for Sensor and Actuator Faults in Three-Phase Induction Motors

The main steps involved in a fault-tolerant control (FTC) scheme are the detection of failures, isolation and reconfiguration of control. Fault detection and isolation (FDI) is a topic of interest due to its importance for the controller, since it provides the necessary information to adjust and mitigate the effects of the fault. Generally, the most common failures occur in the actuator or in sensors, so this article proposes a novel model-free scheme for the detection and isolation of sensor and actuator faults of induction motors (IM). The proposed methodology performs the task of detecting and isolating faults over data streams just after the occurrence of the failure of an induction motor (IM), by the occurrence of either disconnection, degradation, failure, or connection damage. Our approach proposes deep neural networks that do not need a nominal model or generate residuals for fault detection, which makes it a useful tool. In addition, the fault-isolation approach is carried out by classifiers that differentiate characteristics independently of the other classifiers. The long short-term memory (LSTM) neural network, bidirectional LSTM, multilayer perceptron and convolutional neural network are used for this task. The proposed sensors’ and actuator’s fault detection and isolation scheme is simple. It can be applied to various problems involving fault detection and isolation schemes. The results show that deep neural networks are a powerful and versatile tool for fault detection and isolation over data streams

A Metaheuristic Optimization Approach to Solve Inverse Kinematics of Mobile Dual-Arm Robots

This work presents an approach to solving the inverse kinematics of mobile dual-arm robots based on metaheuristic optimization algorithms. First, a kinematic analysis of a mobile dual-arm robot is presented. Second, an objective function is formulated based on the forward kinematics equations. The kinematic analysis does not require using any Jacobian matrix nor its estimation; for this reason, the proposed approach does not suffer from singularities, which is a common problem with conventional inverse kinematics algorithms. Moreover, the proposed method solves cooperative manipulation tasks, especially in the case of coordinated manipulation. Simulation and real-world experiments were performed to verify the proposal’s effectiveness under coordinated inverse kinematics and trajectory tracking tasks. The experimental setup considered a mobile dual-arm system based on the KUKA® Youbot® robot. The solution of the inverse kinematics showed precise and accurate results. Although the proposed approach focuses on coordinated manipulation, it can be implemented to solve non-coordinated tasks