Smart-Cities urban mobility management architecture for electric vehicles

Improving efficiency is one of the most important objectives of the Smart Cities standards, and Electric Vehicles (EVs). TICS can help to soften one of their main limitations –autonomy– planning efficient driving strategies. This paper evaluates the physical variables that have an impact on the EV consumption and presents an electronic architecture to monitor them in an Experimental Ultralight Electric Vehicle. This system includes a set of very low cost sensors integrated with a data logger and a GPRS transmission system that can connect, in real time, with a control center where a route-finding reinforcement-learning algorithm that helps to find the most-effective route and reduce the time spent on urban environment tips.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Learning Control of Robotic Arm Using Deep Q-Neural Network

Enabling robotic systems for autonomous actions such as driverless systems, is a very complex task in real-world scenarios due to uncertainties. Machine learning capabilities have been quickly making their way into autonomous systems and industrial robotics technology. They found many applications in every sector, including autonomous vehicles, humanoid robots, drones and many more. In this research we will be implementing artificial intelligence in robotic arm to be able to solve a complex balancing control problem from scratch, without any feedback loop and using state of the art deep reinforcement learning algorithm named DQN. The benchmark problem that is considered as case study, is balancing an inverted pendulum upward using a six-degrees freedom robot arm. Very simple form of this problem has been solved recently using machine learning however under this thesis we made a very complex system of inverted pendulum and implemented in Robot Operating System (ROS) which is very realistic simulation environment. We have not only succeeded to control the pendulum but also added turbulences on the learned model to study its robustness. We observed how the initial learned model is unstable at the presence of turbulence and how random turbulences helps the system to transform to a more robust model. We have also used the robust model in different environment and showed how the model adopt itself with the new physical properties. Using orientation sensor on the tip of the inverted pendulum to get angular velocity, simulation in ROS and having inverted pendulum on ball joint are few highlighted novelties in this thesis in compare previous publications

A Mission Based Fault Reconfiguration Framework for Spacecraft Applications

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97079/1/AIAA2012-2403.pd

Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks

A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned

An Adaptive Control Strategy for Neural Network based Optimal Quadcopter Controllers

Developing optimal controllers for aggressive high-speed quadcopter flight is a major challenge in the field of robotics. Recent work has shown that neural networks trained with supervised learning can achieve real-time optimal control in some specific scenarios. In these methods, the networks (termed G&CNets) are trained to learn the optimal state feedback from a dataset of optimal trajectories. An important problem with these methods is the reality gap encountered in the sim-to-real transfer. In this work, we trained G&CNets for energy-optimal end-to-end control on the Bebop drone and identified the unmodeled pitch moment as the main contributor to the reality gap. To mitigate this, we propose an adaptive control strategy that works by learning from optimal trajectories of a system affected by constant external pitch, roll and yaw moments. In real test flights, this model mismatch is estimated onboard and fed to the network to obtain the optimal rpm command. We demonstrate the effectiveness of our method by performing energy-optimal hover-to-hover flights with and without moment feedback. Finally, we compare the adaptive controller to a state-of-the-art differential-flatness-based controller in a consecutive waypoint flight and demonstrate the advantages of our method in terms of energy optimality and robustness.Comment: 7 pages, 11 figure

Applied deep learning in intelligent transportation systems and embedding exploration

Deep learning techniques have achieved tremendous success in many real applications in recent years and show their great potential in many areas including transportation. Even though transportation becomes increasingly indispensable in people’s daily life, its related problems, such as traffic congestion and energy waste, have not been completely solved, yet some problems have become even more critical. This dissertation focuses on solving the following fundamental problems: (1) passenger demand prediction, (2) transportation mode detection, (3) traffic light control, in the transportation field using deep learning. The dissertation also extends the application of deep learning to an embedding system for visualization and data retrieval. The first part of this dissertation is about a Spatio-TEmporal Fuzzy neural Network (STEF-Net) which accurately predicts passenger demand by incorporating the complex interaction of all known important factors, such as temporal, spatial and external information. Specifically, a convolutional long short-term memory network is employed to simultaneously capture spatio-temporal feature interaction, and a fuzzy neural network to model external factors. A novel feature fusion method with convolution and an attention layer is proposed to keep the temporal relation and discriminative spatio-temporal feature interaction. Experiments on a large-scale real-world dataset show the proposed model outperforms the state-of-the-art approaches. The second part is a light-weight and energy-efficient system which detects transportation modes using only accelerometer sensors in smartphones. Understanding people’s transportation modes is beneficial to many civilian applications, such as urban transportation planning. The system collects accelerometer data in an efficient way and leverages a convolutional neural network to determine transportation modes. Different architectures and classification methods are tested with the proposed convolutional neural network to optimize the system design. Performance evaluation shows that the proposed approach achieves better accuracy than existing work in detecting people’s transportation modes. The third component of this dissertation is a deep reinforcement learning model, based on Q learning, to control the traffic light. Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. In the proposed model, the complex traffic scenario is quantified as states by collecting data and dividing the whole intersection into grids. The timing changes of a traffic light are the actions, which are modeled as a high-dimension Markov decision process. The reward is the cumulative waiting time difference between two cycles. To solve the model, a convolutional neural network is employed to map states to rewards, which is further optimized by several components, such as dueling network, target network, double Q-learning network, and prioritized experience replay. The simulation results in Simulation of Urban MObility (SUMO) show the efficiency of the proposed model in controlling traffic lights. The last part of this dissertation studies the hierarchical structure in an embedding system. Traditional embedding approaches associate a real-valued embedding vector with each symbol or data point, which generates storage-inefficient representation and fails to effectively encode the internal semantic structure of data. A regularized autoencoder framework is proposed to learn compact Hierarchical K-way D-dimensional (HKD) discrete embedding of data points, aiming at capturing semantic structures of data. Experimental results on synthetic and real-world datasets show that the proposed HKD embedding can effectively reveal the semantic structure of data via visualization and greatly reduce the search space of nearest neighbor retrieval while preserving high accuracy

Trends in the control of hexapod robots: a survey

The static stability of hexapods motivates their design for tasks in which stable locomotion is required, such as navigation across complex environments. This task is of high interest due to the possibility of replacing human beings in exploration, surveillance and rescue missions. For this application, the control system must adapt the actuation of the limbs according to their surroundings to ensure that the hexapod does not tumble during locomotion. The most traditional approach considers their limbs as robotic manipulators and relies on mechanical models to actuate them. However, the increasing interest in model-free models for the control of these systems has led to the design of novel solutions. Through a systematic literature review, this paper intends to overview the trends in this field of research and determine in which stage the design of autonomous and adaptable controllers for hexapods is.The first author received funding through a doctoral scholarship from the Portuguese Foundation for Science and Technology (FCT) (Grant No. SFRH/BD/145818/2019), with funds from the Portuguese Ministry of Science, Technology and Higher Education and the European Social Fund through the Programa Operacional Regional Norte. This work has been supported by the FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020