Autonomous Control System Research Articles

Mathematical modelling and PID control system implementation for quadcopter frame Tarot FY650

PurposeThe purpose of this study is to find the suitable trajectory path of the Numerical model of the Quadcopter. Quadcopters are widely used in various applications due to their compact size and ease of assembly. Because they are quite unstable, autonomous control systems would be used to overcome this problem. Modelling autonomous control is predominant as the research scope faces challenges because of its highly non-linear, multivariable system with 6 degree of freedom.Design/methodology/approachQuadcopters with antonym systems can operate in an unknown environment by overcoming unexpected disturbances. The first objective when designing such a system is to design an accurate mathematical model to describe the dynamics of the system. Newton’s law of motion was used to build the mathematical model of the system.FindingsEstablishment of the mathematical model and the physics behind a four propeller drone for the frame TAROT 650 carbon was done. Simulink model was developed based on the mathematical model for simulating the complete dynamics of the drone as well as location and gusts were included to check the stability.Originality/valueThe control response of the system was simulated numerically results are discussed. The trajectory path was found. The phases with their own parameters can be used to implement the mathematical model for another type of quadcopter model and achieve quick development.

Leveraging digital twin for autonomous docking of a container truck with stabilization control

AbstractThis paper describes the design, development, and implementation of a high‐precision autonomous docking control system for a container truck based on a digital twin approach. The digital twin is used to simulate the dynamic behavior of the physical truck and to design the controller by providing a virtual platform to test, validate, and optimize control strategies and algorithms before their deployment in the actual system. To this end, a cascade of a nonlinear observer and an unscented Kalman filter is used to estimate the state variables of the physical truck for point‐stabilization and orientation controls during the autonomous docking process. The docking motion involves two stabilization problems: point stabilization for smooth motion from the initial configuration to the docking slot, and orientation control to deliver the container truck to the final docking position with a margin of error of 5 cm for position and 0.0087 rad for orientation. The stability of both controllers is investigated, and simulations and experiments are conducted to demonstrate the accuracy of the proposed method in a container terminal environment.

Evaluation of a Voice-Enabled Autonomous Camera Control System for the da Vinci Surgical Robot

Robotic surgery involves significant task switching between tool control and camera control, which can be a source of distraction and error. This study evaluated the performance of a voice-enabled autonomous camera control system compared to a human-operated camera for the da Vinci surgical robot. Twenty subjects performed a series of tasks that required them to instruct the camera to move to specific locations to complete the tasks. The subjects performed the tasks (1) using an automated camera system that could be tailored based on keywords; and (2) directing a human camera operator using voice commands. The data were analyzed using task completion measures and the NASA Task Load Index (TLX) human performance metrics. The human-operated camera control method was able to outperform an automated algorithm in terms of task completion (6.96 vs. 7.71 correct insertions; p-value = 0.044). However, subjective feedback suggests that a voice-enabled autonomous camera control system is comparable to a human-operated camera control system. Based on the subjects’ feedback, thirteen out of the twenty subjects preferred the voice-enabled autonomous camera control system including the surgeon. This study is a step towards a more natural language interface for surgical robotics as these systems become better partners during surgery.

Towards Autonomous Control System in Brazilian Navy's USV-Lab using MOOS-IvP framework

This article aims to describe the implementation of an autonomous control system for the Brazilian Navy's Unmanned Surface Vehicle Laboratory (USV-Lab) using the MOOS-IvP framework. The main goal of this project is to enable an existing vehicle to be a laboratory for research of maritime autonomous systems, to perform unmanned maritime missions, following desired routes without human intervention or being remotely piloted. The paper starts with the importance of autonomous maritime vehicles and presents MOOS-IvP framework as a validated tool for autonomous maritime systems development. In succession, the main objectives are set, the physical system and components are shown and the proposed software and communication are structured. The work comprises the development of control interface, the integration of sensors and actuators, the tuning PID controllers, and specific software settings, along with several hardware modifications. The methodology consisted in progressive testing, from simulations to local operations as made in Guanabara Bay in Rio de Janeiro and Bay of All Saints in Bahia. Results revealed the system's effectiveness and robustness in maintaining configured routes, enabling its use in a Brazilian Navy military reconnaissance exercise, as well as serving as a laboratory for future research.

Level 4 commercial autonomous vehicle control system transition to an open-source solution

Level 4 commercial autonomous vehicle control system transition to an open-source solution

Functional Safety and Performance Analysis of Autonomous Route Management for Autonomous Train Control System

Functional Safety and Performance Analysis of Autonomous Route Management for Autonomous Train Control System

Design and application of an autonomous Master Control System for a multi-layer magnetic and helioseismic telescope

Design and application of an autonomous Master Control System for a multi-layer magnetic and helioseismic telescope

Describing Function and Event Based Analysis of a General Class of Piecewise Feedback Control Systems

The analysis of nonlinear systems can be greatly improved using computer software. In this work the Sridhar nonlinearity is described, and a new parameterization is given that allows to systematically obtain a large number of the classical piecewise nonlinearities as particular cases. The calculation of limit cycles using the describing function (DF) approach for the case of autonomous nonlinear control systems and an event-based simulation method are presented and compared. Finally, a user-friendly, graphically oriented interactive tool based on Sysquake is presented. In this way the students can easily assimilate some concepts of introductory courses in nonlinear control such as the behavior of piecewise linear systems, stable and unstable limit cycles, event-based simulation and the DF method.

A hybrid technique for an autonomous vehicle control system to enhance the vehicle robustness: a HBA-RBFNN technique

A hybrid technique for an autonomous vehicle control system to enhance the vehicle robustness: a HBA-RBFNN technique

A hybrid technique for an autonomous vehicle control system to enhance the vehicle robustness: a HBA-RBFNN technique

A hybrid technique for an autonomous vehicle control system to enhance the vehicle robustness: a HBA-RBFNN technique

Safety-critical control for autonomous systems: Control barrier functions via reduced-order models

Modern autonomous systems, such as flying, legged, and wheeled robots, are generally characterized by high-dimensional nonlinear dynamics, which presents challenges for model-based safety-critical control design. Motivated by the success of reduced-order models in robotics, this paper presents a tutorial on constructive safety-critical control via reduced-order models and control barrier functions (CBFs). To this end, we provide a unified formulation of techniques in the literature that share a common foundation of constructing CBFs for complex systems from CBFs for much simpler systems. Such ideas are illustrated through formal results, simple numerical examples, and case studies of real-world systems to which these techniques have been experimentally applied.

Phy-Taylor: Partially Physics-Knowledge-Enhanced Deep Neural Networks via NN Editing.

Purely data-driven deep neural networks (DNNs) applied to physical engineering systems can infer relations that violate physics laws, thus leading to unexpected consequences. To address this challenge, we propose a physics-knowledge-enhanced DNN framework called Phy-Taylor, accelerating learning-compliant representations with physics knowledge. The Phy-Taylor framework makes two key contributions; it introduces a new architectural physics-compatible neural network (PhN) and features a novel compliance mechanism, which we call physics-guided neural network (NN) editing. The PhN aims to directly capture nonlinear physical quantities, such as kinetic energy, electrical power, and aerodynamic drag force. To do so, the PhN augments NN layers with two key components: 1) monomials of the Taylor series for capturing physical quantities and 2) a suppressor for mitigating the influence of noise. The NN editing mechanism further modifies network links and activation functions consistently with physics knowledge. As an extension, we also propose a self-correcting Phy-Taylor framework for safety-critical control of autonomous systems, which introduces two additional capabilities: 1) safety relationship learning and 2) automatic output correction when safety violations occur. Through experiments, we show that Phy-Taylor features considerably fewer parameters and a remarkably accelerated training process while offering enhanced model robustness and accuracy.

Scalable and cohesive swarm control based on reinforcement learning

Unmanned vehicles have seen a significant increase in a wide variety of fields such as for logistics, agriculture and other commercial applications. Controlling swarms of unmanned vehicles is a challenging task that requires complex autonomous control systems. Reinforcement learning has been proposed as a solution to this challenge. We propose an approach based on agent masking to enable a simple Deep Q-Network algorithm to scale on large swarms while training on relatively smaller swarms. We train our approach using multiple swarm sizes and learning rates and compare our results using metrics such as the number of collisions. We also compare the ability of our approach to scale on swarms ranging from five to 25 agents using metrics and visual analysis. Our proposed solution was able to guide a swarm of up to 100 agents to a target while keeping a good swarm cohesion and avoiding collision.

Comparison of optimizers for model predictive thermal control of buildings

Considering recent developments in the energy sector, further reduction of electricity cost and flattening of the electric power demand curve are needed. We have focused on an autonomous electric heater control system that can easily be implemented in existing buildings without strict comfort requirements. Examples are winter heating of warehouses and vacation homes, and heat drying of buildings under construction. We have set up a system that typically reduces electricity cost by about 40% on the basis of automatic weather and real time pricing forecasts. The system uses the building as an energy reservoir over periods with high electricity cost. Using a model predictive control system, we compare use of a genetic algorithm, a particle swarm optimization, and a neural network for heater control, all working in a closed loop to reduce the influence of modeling errors. We have simulated the performance of the systems using realistic data and found that all three optimizers give about the same performance, varying only a few percent in efficiency. However, the computational and memory requirements of the neural network are much lower than for the other optimizers, so it is preferable for use with inexpensive microcontrollers. We carried out a full-scale experiment at a residential house and found agreement with simulation results.

Sterowanie autonomicznym bezzałogowym statkiem powietrznym z wykorzystaniem uczenia przez wzmacnianie

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatable avoid trees and fly through the forest.

PPO-Based Autonomous Transmission Period Control System in IoT Edge Computing

PPO-Based Autonomous Transmission Period Control System in IoT Edge Computing

Modeling of an Autonomous Electric Propulsion Barge for Future Inland Waterway Transport

International trade is continuously rising, leading to an increase in the flow of goods passing through transportation hubs, including air and sea. In addition, the aging fleet of inland vessels necessitates renewal through the construction of new vessels, presenting opportunities for the adoption of modern transport technologies. Autonomous barges can transport bulk and containerized cargo between the central port of a specific region and smaller satellite ports, enabling the dispersal of goods over a wider area. Equipping autonomous barges with advanced sensors, such as LIDAR, computer vision systems that operate in visible light and thermal infrared, and incorporating advanced path finding and cooperation algorithms may enable them to operate autonomously, subject only to remote supervision. The purpose of this study is to explore the potential of autonomous electric propulsion barges in inland waterway transport. Given the increasing demand for efficient and sustainable transport solutions as a result of various new policies, which have set new ambitious goals in clean transportation, this study aims to develop a proposition of an electric propulsion hybrid drive inland waterway barge, and compare it to a conventional diesel-powered barge. The methodology involves the creation of a simulation model of an inland waterway class IV electric barge, equipped with advanced sensors and autonomous control systems. The barge’s navigation is managed through a multi-agent system, with evolutionary algorithms determining a safe passage route. This research also utilizes a proprietary networked ship traffic simulator, based on real inland vessel recorded routes, to conduct the autonomous navigation study. The energy consumption of the barge on a route resulting from the ship traffic simulation is then examined using the mathematical model using the OpenModelica package. As a result of the study, the proposed hybrid propulsion system achieved a 16% reduction in fuel consumption and CO2 emissions, while cutting engine operation time by more than 71%. The findings could provide valuable insights into the feasibility and efficiency of autonomous electric propulsion barges, potentially helping future developments in inland waterway transport.

Simplified Maneuvering Strategies for Rendezvous in Near-Circular Earth Orbits

The development of autonomous guidance control and navigation systems for spacecraft would greatly benefit applications such as debris removals or on-orbit servicing, where human intervention is not practical. Within this context, inspired by Autonomous Vision Approach Navigation and Target Identification (AVANTI) demonstration, this work presents new guidance algorithms for rendezvous and proximity operations missions. Analytical laws are adopted and preferred over numerical methods, and mean relative orbital elements are chosen as state variables. Application times, magnitudes and directions of impulsive controls are sought to minimize propellant consumption for the planar reconfiguration of the relative motion between a passive target spacecraft and an active chaser one. In addition, simple and effective algorithms to evaluate the benefit of combining in-plane and out-of-plane maneuvers are introduced to deal with 3D problems. The proposed new strategies focus on maneuvers with a dominant change in the relative mean longitude (rarely addressed in the literature), but they can also deal with transfers where other relative orbital elements exhibit the most significant variations. A comprehensive parametric analysis compares the proposed new strategies with those employed in AVANTI and with the global optimum, numerically found for each test case. Results are similar to the AVANTI solutions when variations of the relative eccentricity vector dominate. Instead, in scenarios requiring predominant changes in the relative mean longitude, the required ΔV exhibits a 49.88% reduction (on average) when compared to the original methods. In all the test cases, the proposed solutions are within 3.5% of the global optimum in terms of ΔV. The practical accuracy of the presented guidance algorithms is also tested with numerical integration of equations of motion with J2 perturbation.

DEVELOPMENT OF CONTROL LAWS OF UNMANNED AERIAL VEHICLES FOR PERFORMING GROUP FLIGHT AT THE STRAIGHT-LINE HORIZONTAL FLIGHT STAGE

The article proposes an improved approach to controlling groups of unmanned aerial vehicles (UAVs) aimed at increasing the overall efficiency and flexibility of the control process. The use of a heterogeneous external field, which varies both in magnitude and direction, allows achieving greater adaptability and accuracy in controlling a group of UAVs. A vector field for unmanned aerial vehicles determines the direction and intensity of the vehicles' movement in space. Such vector fields can be used to develop UAV control laws, including determining optimal flight paths, controlling speed, avoiding obstacles, and ensuring coordination of a group of UAVs. The subject of the study is the methods of controlling groups of autonomous UAVs, where each vehicle may have different speeds and flight directions. To solve this problem, various methods of using a heterogeneous field have been developed and proposed. Instead of using a homogeneous field that provides a constant flight speed, a vector field is used that adapts to different conditions and characteristics of the vehicles in the group. This method allows for effective group management, ensuring the necessary coordination and interaction between the vehicles. An analysis of recent research and publications in the field of autonomous system control indicates the feasibility of using machine learning, vector fields, and a large amount of data to successfully coordinate the movement of autonomous systems. These approaches make it possible to create efficient and reliable control systems. The aim of the study is to develop laws for controlling the movement of a group of autonomous unmanned aerial vehicles at the stage of straight-line horizontal flight based on natural analogues to improve the efficiency and reliability of their coordinated movement in different conditions. The main conclusions of the research are that the proposed method of controlling groups of UAVs based on a heterogeneous field can be implemented. It takes into account a variety of vehicle characteristics and environmental conditions that are typical for real-world use scenarios. This work opens up prospects for further improving the management of UAV groups and their use in various fields of activity. The article emphasises the relevance of technology development for autonomous unmanned systems, especially in the context of autonomous transport systems.

Towards self‐organizing logistics in transportation: a literature review and typology

AbstractDeploying self‐organizing systems is a way to cope with the logistics sector's complex, dynamic, and stochastic nature. In such systems, automated decision‐making and decentralized or distributed control structures are combined. Such control structures reduce the complexity of decision‐making, require less computational effort, and are therefore faster, reducing the risk that changes during decision‐making render the solution invalid. These benefits of self‐organizing systems are of interest to many practitioners involved in solving real‐world problems in the logistics sector. This study, therefore, identifies and classifies research related to self‐organizing logistics (SOL) with a focus on transportation. SOL is an interdisciplinary study across many domains and relates to other concepts, such as agent‐based systems, autonomous control, and decentral systems. Yet, few papers directly identify this as self‐organization. Hence, we add to the existing literature by conducting a systematic literature review that provides insight into the field of SOL. The main contribution of this paper is two‐fold: (i) based on the findings from the literature review, we identify and synthesize 15 characteristics of SOL in a typology, and (ii) we present a two‐dimensional SOL framework alongside the axes of autonomy and cooperativity to position and contrast the broad range of literature, thereby creating order in the field of SOL and revealing promising research directions.