Flight-schedule using Dijkstra's algorithm with comparison of routes findings

<span>The Dijkstra algorithm, also termed the shortest-route algorithm, is a model that is categorized within the search algorithms. Its purpose is to discover the shortest-route, from the beginning node (origin node) to any node on the tracks, and is applied to both directional and undirected graphs. However, all edges must have non-negative values. The problem of organizing inter-city flights is one of the most important challenges facing airplanes and how to transport passengers and commercial goods between large cities in less time and at a lower cost. In this paper, the authors implement the Dijkstra algorithm to solve this complex problem and also to update it to see the shortest-route from the origin node (city) to the destination node (other cities) in less time and cost for flights using simulation environment. Such as, when graph nodes describe cities and edge route costs represent driving distances between cities that are linked with the direct road. The experimental results show the ability of the simulation to locate the most cost-effective route in the shortest possible time (seconds), as the test achieved 95% to find the suitable route for flights in the shortest possible time and whatever the number of cities on the tracks application.</span>

Deep reinforcement learning based control for Autonomous Vehicles in CARLA

Nowadays, Artificial Intelligence (AI) is growing by leaps and bounds in almost all fields of technology, and Autonomous Vehicles (AV) research is one more of them. This paper proposes the using of algorithms based on Deep Learning (DL) in the control layer of an autonomous vehicle. More specifically, Deep Reinforcement Learning (DRL) algorithms such as Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) are implemented in order to compare results between them. The aim of this work is to obtain a trained model, applying a DRL algorithm, able of sending control commands to the vehicle to navigate properly and efficiently following a determined route. In addition, for each of the algorithms, several agents are presented as a solution, so that each of these agents uses different data sources to achieve the vehicle control commands. For this purpose, an open-source simulator such as CARLA is used, providing to the system with the ability to perform a multitude of tests without any risk into an hyper-realistic urban simulation environment, something that is unthinkable in the real world. The results obtained show that both DQN and DDPG reach the goal, but DDPG obtains a better performance. DDPG perfoms trajectories very similar to classic controller as LQR. In both cases RMSE is lower than 0.1m following trajectories with a range 180-700m. To conclude, some conclusions and future works are commented.

Decision making at unsignalized inner city intersections using discrete events systems

Autonomous driving is a promising technology to, among many aspects, improve road safety. There are however several scenarios that are challenging for autonomous vehicles. One of these are unsignalized junctions. There exist scenarios in which there is no clear regulation as to is allowed to drive first. Instead, communication and cooperation are necessary to solve such scenarios. This is especially challenging when interacting with human drivers. In this work we focus on unsignalized T-intersections. For that scenario we propose a discrete event system (DES) that is able to solve the cooperation with human drivers at a T-intersection with limited visibility and no direct communication. The algorithm is validated in a simulation environment, and the parameters for the algorithm are based on an analysis of typical human behavior at intersections using real-world data.

Design of a Labriform-Steering Underwater Robot Using a Multiphysics Simulation Environment

Bio-inspired solutions devised for Autonomous Underwater Robots are currently investigated by researchers as a source of propulsive improvement. To address this ambitious objective, the authors have designed a carangiform swimming robot, which represents a compromise in terms of efficiency and maximum velocity. The requirements of stabilizing a course and performing turns were not met in their previous works. Therefore, the aim of this paper is to improve the vehicle maneuvering capabilities by means of a novel transmission system capable of transforming the constant angular velocity of a single rotary actuator into the pitching–yawing rotation of fish pectoral fins. Here, the biomimetic thrusters exploit the drag-based momentum transfer mechanism of labriform swimmers to generate the necessary steering torque. Aside from inertia and encumbrance reduction, the main improvement of this solution is the inherent synchronization of the system granted by the mechanism’s kinematics. The system was sized by using the experimental results collected by biologists and then integrated in a multiphysics simulation environment to predict the resulting maneuvering performance.

Electronically Adjustable Grounded Memcapacitor Emulator Based on Single Active Component with Variable Switching Mechanism

New current mode grounded memcapacitor emulator circuits are reported in this paper, based on a single voltage differencing transconductance amplifier-VDTA and two grounded capacitors. The proposed circuits possess a single active component matching constraint, while the MOS-capacitance can be used instead of classical capacitance in a situation involving the simulator working within a high frequency range of up to 50 MHz, thereby offering obvious benefits in terms of realization utilising an IC-integrated circuit. The proposed emulator offers a variable switching mechanism—soft and hard—as well as the possibility of generating a negative memcapacitance characteristic, depending on the value of the frequency of the input current signal and the applied capacitance. The influence of possible non-ideality and parasitic effects was analysed, in order to reduce their side effects and bring the outcome to acceptable limits through the selection of passive elements. For the verification purposes, a PSPICE simulation environment with CMOS 0.18 μm TSMC technology parameters was selected. An experimental check was performed with off-the-shelf components-IC MAX435, showing satisfactory agreement with theoretical assumptions and conclusions.

Performance Evaluation of a Maneuver Classification Algorithm Using Different Motion Models in a Multi-Model Framework

Environment perception is one of the major challenges in the vehicle industry nowadays, as acknowledging the intentions of the surrounding traffic participants can profoundly decrease the occurrence of accidents. Consequently, this paper focuses on comparing different motion models, acknowledging their role in the performance of maneuver classification. In particular, this paper proposes utilizing the Interacting Multiple Model framework complemented with constrained Kalman filtering in this domain that enables the comparisons of the different motions models’ accuracy. The performance of the proposed method with different motion models is thoroughly evaluated in a simulation environment, including an observer and observed vehicle.

Intelligent Resource Allocation for Massive Internet of Things Traffic

The Internet of Things (IoT) is considered a major trend in computing and in specific areas such as Smart Cities, Smart Grid, Industry 4.0, and mobile applications based on 5G. Typically, this set of technologies requires the orchestration of heterogeneous resources that are allocated over distinct infrastructures such as Cloud Computing, Cloud of Things, Datacenters, and network backbones. Consistent with this demand, the PSIoT-Orch framework was designed to orchestrate massive IoT traffic and to allocate network resources between Aggregators and Consumers in a Publish / Subscribe strategy. This dissertation aims to build an intelligent module for PSIoT-Orch that is capable of handling data types with different transmission requirements, aiming at the efficient use of a limited communication link. The proposed component uses Reinforcement Learning, more specifically, the SARSA algorithm to dynamically adjust the available bandwidth according to transmission priority. This solution, named PSIoT-SARSA, is validated in a simulation environment under the statistical methods of Analysis of Variance and Response Surface Analysis and, at the end of the study, it is observed that it obtained promising results. The contributions are focused on gathering an approach that allows allocating bandwidth in an intelligent way, allowing efficient scheduling of the IoT flow, in the scenario of the Smart Grid.