Robotic Vehicle Research Articles

Military Purpose Robot

In modern world many countries are working on different technologies. Hence, safety of soldiers and army personnel who fight for national security of their respective country need to considered. During warfare many of the soldiers lose their lives in abandoned areas or at borders. Landmines are an explosive weapon which may be or may not be buried under the soil and activated by a mere pressure of 9kg when someone steps on it. It also has an adverse effect on environment such as damage to soil structure, reducing soil productivity and increase vulnerability of soil to water and wind erosion. Mines also kill innocent civilians and soldiers after war ends. In order to save the lives of soldiers and diffuse the mines this paper is proposing a robotic vehicle with a metal detector which is capable of sensing mine ahead of it. It will also detect position of landmine and send latitude, longitude coordinates using GPS module. and also, Video surveillance is the process of monitoring a situation, an area or a person. This generally occurs in a military scenario where surveillance of borderlines and enemy territory is essential to a country’s safety. Human surveillance is achieved by deploying personnel near sensitive areas in order to constantly monitor for changes. But humans do have their limitations, and deployment in inaccessible places is not always possible. We have developed a robot which can be used for video surveillance & monitoring which can be controlled through a GUI. The control mechanism is provided with a video transmission facility. The video transmission is practically achieved through high-speed image transmission. Initially, the robot will be equipped with a camera which will capture the scenes and transfer the images to the server on which the user will be controlling and watching the live feed. Key Words: Node MCU (ESP8266), GPS Module, Metal Detector, Motor Driver (L298N), ESP32-cam module, Surveillance Robot, landmine detection, fire detection, temperature and humidity sensing.

Ride comfort of patient transport robot chassis

The ride comfort is a significant property of patient transport robots, which is directly related to the performance of rescue instruments and the cause of secondary damage. Previous studies overlook the design optimization of patient transport robots to improve ride comfort in hospital environments. This study designs a mobility system with an improved chassis that enables a more comfortable ride for patients. The system incorporates a longitudinal arm-independent suspension, which has a regulating device that changes the shock absorber angle and longitudinal arm length. A four-wheel vibration coupling response model of the robot vehicle is established. An optimization model with variable stiffness and damping coefficients is built and analyzed. The effective and maximum values of the body’s vertical, pitch angular, and roll angular accelerations are used as evaluation criteria of the robot’s ride comfort, which is analyzed on random roads and roadblocks, comparing its performance before and after the optimization. The results show that the weighted acceleration of the optimized body reduces by 61.9%, and the optimized suspension system improves the overall robot’s ride comfort. The evaluation indices of the optimized robot are measured through experiments. The weighted acceleration in the three directions of the optimized body is 0.18[Formula: see text]m/s2, which is less than 0.315[Formula: see text]m/s2 as required by ISO 2631-1, indicating that the patient does not feel uncomfortable, rendering an excellent evaluation, and verifying the rationality of the suspension system modeling analysis. The establishment of the multi-objective optimization model provides a theoretical basis for improving the ride comfort of mobile robots.

Saliency-Induced Moving Object Detection for Robust RGB-D Vision Navigation Under Complex Dynamic Environments

Localization in unknown environments is an essential requirement for vision navigation of robotic vehicles in intelligent transportation systems. However, moving objects in dynamic scenarios usually bring about great difficulty for robot localization, because motion estimation of robotic vehicles is disturbed by increasing feature outliers caused by moving objects. In order to improve the accuracy and robustness of robot localization, a novel saliency-induced moving object detection (SMOD) approach is proposed to filter out feature outliers for RGB-D-based simultaneous localization and mapping (SLAM) in complex dynamic workspaces. Firstly, three complementary motion saliency potentials, including motion energy (ME), spatiotemporal objectness (STO), and dynamic superpixels (DS), are modeled by fully analyzing spatial, temporal, appearance and depth cues in RGB-D inputs. They can be used to identify the dynamic objects effectively from diversely changing backgrounds. Then, a superpixel-level graph-based motion saliency (MS) measure is proposed to generate the MS map for reliable localization of the moving objects. The edge weights and background nodes on the graph are determined reasonably by fusing ME, STO, and DS, which is not vulnerable to background interferences. Furthermore, the SMOD approach is embedded into the front-end of ORB-SLAM3 as a pre-processing stage, in order to filter out feature outliers associated with the moving objects. Finally, the extensive experiments are performed to verify the accuracy and robustness of the proposed approach on the public dynamic datasets. The experimental results show that the SMOD method can detect the moving objects effectively in a variety of challenging dynamic environments, and separate the dynamic regions reliably from the irrelevant background. The data comparison demonstrates that the SMOD-SLAM navigation system can outperform other state-of-the-art dynamic visual SLAM (vSLAM) systems.

Object Detection Techniques: A Review

Humans can understand their surroundings clearly because they regularly notice objects in their environment. It is essential for the machine to perceive the surroundings similarly to how humans do in order to make it autonomous and capable of navigating in the human world. The machine can assess its surroundings and identify objects using object detection. This can simplify a number of tasks and enable the machine to recognize its surroundings. Making bounding boxes that surround the objects is essentially how object detection systems work to locate objects in an image. Object detection has applications such as autonomous robot navigation, surveillance, face detection, and vehicle

Advancements in Agricultural Automation: A Comprehensive Review of Artificial Intelligence and Humanoid Robotics in Farming

This paper discusses about the modern techniques implemented in the field of agriculture which shaped the traditional farming to the smart farming (Agriculture 4.0). The rapid rise in the global population is one among the main reasons, besides monitoring of crops health and yield requires a huge labor force, which is also another reason for promoting intelligent systems into agriculture sector. Conventional farming methods are not suitable to meet this demand, which led robotics to associate with on-field agriculture by means of robot-based technologies like wheeled robots, ground vehicles (manned and unmanned) and aerial vehicles (manned and unmanned) that led to explore the possible advancement in agriculture 3.0. Currently, the evolutionary techniques, such as Artificial Intelligence (AI) and IoT, are implemented in robotic vehicles to make them intelligent systems. Due to unprecedented climatic change and polluted ground water for the past few decades, the crops are being infested with new varieties of diseases. This requires new image processing techniques to classify the diseases based on color, texture and, shape of leaves. The incorporation of image processing technique into AI aids in deciding the appropriate amount of herbicide supplement to the plant based on the prediction of plant growth.

Global experience in the application of intelligent systems and technologies in the military sphere

This article analyzes technologies and modern means of military use with intelligent computer systems (platforms). Such means include "smart" bullets, rifles with augmented reality glasses, as well as ballistic computers, "smart" weapons (mortars), high-precision ammunition, robotic combat vehicles and other equipment, laser air defense systems for ground, air, as well as naval forces, unmanned aerial vehicles, etc. Examples of intellectual means of military use, their tactical and technical characteristics and fields of application are given. The advantages and disadvantages of using technologies and means with intelligent systems (platforms) are highlighted, as well as the justified expediency of their use for military purposes. The examples of tools with intelligent systems (platforms) listed above are effectively used in the leading countries of the world, namely: the USA, Great Britain, Japan, France, Germany, Israel, the Netherlands, etc. The experience of use by the military of these countries has shown that intelligent systems make it possible to simplify the performance of relevant tasks, measures to protect against accidents or excessive aggression of criminals (enemies), and also increase the safety of the civilian population. It was determined that the introduction of world achievements, as well as the development of domestic systems and means with intelligent systems, is one of the priority directions of the development of the military-industrial complex of Ukraine. This, in turn, requires close cooperation between scientists and the military, which prompts the study of the possibilities of using intelligent systems in various fields, in particular in the field of national security development of Ukraine. Further prospective studies in this direction are highlighted.

Decision Support System for autonomous vehicle maintenance management

Autonomous vehicles are complex objects equipped with the tools for monitoring the technical condition and transmitting data for diagnostics and prediction. The design of new models of autonomous and robotic vehicles is inextricably linked with the development of a maintenance system. The efficient functioning of the maintenance system is ensured by the use of intelligent technologies and digital twins. The article proposes an approach to designing a decision support system for the maintenance and repair of a fleet of autonomous vehicles. The decision support system is focused on robotic agricultural vehicles. The proposed structure of the system is of a general nature and can be used in transport and logistics enterprises in various sectors of the economy. The general architecture of the maintenance system is presented, including the neuro-digital twin of the vehicle, the analytics unit, simulation models of operation processes, and service centers. The components of the neuro-digital twin are digital twins of car units, a knowledge base, domain ontology, artificial neural networks, and a team of experts. The proposed approach is based on a combination of intelligent technologies and simulation modeling in collaboration with a team of experts. A set of original simulation models for the operation of a fleet of autonomous vehicles has been developed. New models based on stochastic timed colored Petri nets are proposed to analyze the processes during the autonomous vehicle operation. The developed decision support system can be used both at the stage of virtual commissioning and in the real operation of autonomous vehicles.

Disturbance rejection controller design based on adaptive nonlinear FOPID controller and chaotic WOA with a neuro-fuzzy approximation for URV robot

This paper presents an adaptive neuro-fuzzy-based nonlinear fractional order proportional integral derivative (ANF-NLFOPID) controller to stabilize six degrees of freedom (6-DOF) underwater robotic vehicle (URV). Generally, the stability of the URV system is jammed due to internal and external variations related to unknown uncertainties and disturbances in the URV model, which lead to trajectory tracking problems. Thus, to solve unknown uncertainties and disturbances problems, an ANF network is proposed to estimate unknown uncertainties and disturbances. At the same time, an NLFOPID controller is proposed to minimize the error effect generated by the variation of the URV model parameters. Considering the unknown and unpredictable behavior of the URV system in the presence of uncertainties and disturbances, the ANF-NLFOPID controller parameters are tuned continuously using an improved whale optimization algorithm based on chaotic theory (IWOA-CT). The main working principle of the IWOA-CT is to solve the local minimum (optimum) problem and achieve the global optimum points to deliver the best parameter values for the ANF-NLFOPID controller. A simulation and experimental results are conducted to measure the performance of the ANF-NLFOPID controller compared to other existing works. MATLAB software is used to simulate four trajectory tracking cases, where the first case proved the superiority of the ANF-NLFOPID controller by 52.559%, 70.028%, and 73.83%, the second case shows that the ANF-NLFOPID improves the trajectory tracking performance by 34.399%, 46.094%, and 59.143%, the third case demonstrates the effectiveness of the ANF-NLFOPID controller by 41.922%, 51.205%, and 62.252%, and the fourth case shows that the ANF-NLFOPID enhance the overall trajectory tracking efficiency by 29.356%, 38.413%, and 44.795% compared to adaptive neural network-based nonlinear FOPID (ANNFOPID), adaptive fuzzy-based nonlinear PID (AFNLPID), and disturbance rejection based on adaptive neural network (DR-ANN) controllers respectively. In the end, the experimental results were conducted using a real-world URV model and showed that the ANF-NLFOPID controller presents superior in solving trajectory tracking problems compared to the ANNFOPID controller.

A set-based approach for detecting faults of a remotely controlled robotic vehicle during a trajectory tracking maneuver

This paper addresses the problem of fault detection for a remotely controlled Differential Drive Mobile Robot (DDMR) during trajectory tracking maneuvers. The proposed solution involves two steps. The first step, performed offline, consists of generating a feasible trajectory for the DDMR subject to unknown but bounded external disturbances. To this end, the dynamics of the closed-loop trajectory tracking error are first described using an uncertain system with norm-bounded uncertainty. The optimal feasible trajectory is obtained by a recursive algorithm that involves solving some SDP optimization problems with LMIs constraints. In the second step, which is performed online, the feasibility property of the computed trajectory is exploited to detect faults during the tracking maneuvers. In order to validate the proposed approach, an extensive experimental test campaign was conducted using the Jaguar V4 Dr.Robot platform.

Kinematics, dynamics, and control of underwater robotic snake based on a rigid-soft unified model

ABSTRACT With the advancement of underwater biomimetic robots, many kinds of underwater snake robots have been developed to perform various marine inspection and maintenance operations. This paper introduces an innovative biomimetic extension to underwater robotic vehicles, an underwater rigid-soft snake robot, which is operated by a combination of rigid propulsion modules and soft joint modules. The essential point is how to jointly mathematically model soft joints and rigid modules with different physical property. The Constant Curvature assumption is widely used in modeling of soft module robots. However, the limitation of this assumption is that it can't accurately describe the deformation of soft robots when performing dynamic tasks. We propose a methodology, rigid-soft unified model, for kinematic and dynamic modeling by viewing soft joint as flexible universal joint. The motion analysis of the robot under the influence of water flow in different postures is completed. Furthermore, we develop a closed loop hierarchical trajectory controller for the snake robot. Especially in the part of dynamic control system, we compare the tracking performance of different controllers.

An intelligent transport system capable of collecting or foraging with many robotic vehicles: An intelligent computing paradigm

Self-driving vehicles are increasingly vital for various environmental tasks, such as data gathering, remote sensing, and mapping. They are particularly important in transportation, where advanced robotic vehicles can operate effectively without human oversight in challenging conditions. One exciting application is using collaborative autonomous robotic vehicles to locate specific points of interest like accidents, congestion, rain, fog, or icy roads. This article explores multi-robot systems for transportation tasks, considering interference that may impact the robot group’s performance. A mathematical model is analysed to quantify the influence of interference and transformed into a dimensionless form for analysis. The system is studied numerically, and a neural soft computing technique, LMA-NN, is proposed. The technique is established on a neural network. For performance evaluation of the LMA-NN technique, different performance benchmarks such as mean square error and absolute error with reference solution are employed. The results are also presented graphically.

Field Oriented Control of Brushless Direct Current Motor for Electric Vehicle Robot Obstacle Detection and Avoidance

This paper presents an approach to implement Field Oriented Control (FOC) brushless directcurrent motor using PID Controller for electric vehicle obstacle detection and avoidance. A state spacemodel of the BLDC motor with FOC was model and simulated using MATLAB/Simulink to validate thestability of the proposed system. The overall system was validated in real time implementation using microcontroller and Ultrasonic Sensor. The detection and recognition of objects, as well as making anautonomous decision to change its trajectory which is based on predetermined threshold distance of 5cmwere analyzed.

Collision-free output-feedback super-twisting control of robotic surface vehicles for coordinated path following with experiments

This paper addresses the coordinated path following (CPF) control of robotic surface vehicles (RSVs) in the presence of multiple obstacles and lumped disturbances over a resource-constrained wireless network. A collision-free output-feedback super-twisting control architecture is proposed for CPF without using velocity measurements. Specifically, robust exact differentiator observers are designed at first to recover the unmeasured velocities, and estimate the lumped disturbances, simultaneously. Next, anti-disturbance output-feedback super-twisting control laws are proposed to achieve individual path following in earth-fixed reference frame, and an adaptive potential field with a user-defined collision hazard degree is introduced to avoid the collisions with obstacles. To reduce the network traffic, self-triggered path updating laws are then designed for avoiding the continuous transmitting and listening of neighboring RSVs. The stability analysis shows that the closed-loop system is input-to-state stable outside the collision avoidance region, and Zeno behavior will not happen. Experiments are finally conducted to validate the efficacy of the proposed collision-free output-feedback super-twisting control method for the self-triggered CPF of RSVs.

Structural analysis of different materials used in ornithopter for better strength and application

An ornithopter is a biomimicry of a flying bird or insect which is power-driven by an electronic powered battery and mechanical components such as a gearbox and spars. These bio-inspired flying robotic vehicles are being inspected and developed for various applications in their field but are currently not at that level to have a successful flight. It is not easy to select a such material for better achievement and performance of an ornithopter which must withstand not only aerodynamically forces applied on it but also damages occur during its performance segments just like as take-off, cruising, different altitude, landing, or any activities enrolled by ornithopter. The core objective of this paper is to provide a material for an ornithopter that gives a better stand respect to applied load i.e., stress and varying strain limit from the initial low to its fracture point with respect to materials like Aluminium alloy, E-glass fibre-honeycomb, Titanium alloy, Carbon fibre etc. A FEA analysis is done to understand these problems and to analyse the various elements to make sure suitable material has been chosen.

Analytical and Numerical Study of Underwater Tether Cable Dynamics for Seabed Walking Robots Using Quasi-Static Approximation

In this study, the dynamics of tether cables (TCs) that connect a surface ship and a walking robotic vehicle on the seabed are numerically investigated. The main aim of this study is to develop a reliable prediction model for the dynamic behavior of TCs attached to a seabed walking robot. This system consists of a surface ship, underwater manned seabed walking robot (UMSWR), TC, and winch. The study is comprised of mathematical modeling and numerical simulations of the developed governing equations for the TC dynamics. A relatively simple and efficient mathematical analysis method is proposed to determine the configuration and forces on the TC under a steady state. The problem is solved using the quasi-static technique and lumped mass parameters for discretizing and modeling the dynamics of the TC. Based on the static analysis of the Morrison equation and finite segment method, analytical formulas of the steady-state equation of TC were obtained and solved. The effects of variable water density and variable underwater currents are included in the cable behavior. Consequently, the two-dimensional TC profile and axial tension were estimated in a steady-state configuration. The developed equations were simulated in MATLAB software. Several numerical simulation examples were worked out, demonstrating the accurate performance of the method. Various input parameters of the system and their relationships with the output values were investigated, thereby demonstrating the versatility of the method.

Robust Visual Odometry On $\text{SE}(3)$: Design and Verification

Visual odometry is a crucial technique for estimating a robotic vehicle's trajectory by analyzing images captured by its onboard camera when the vehicle's attitude cannot be retrieved. However, uncertainties such as modeling errors, measurement noise, misidentification of feature marks, and the switching output arising from visual geometric constraints can all hinder accurate estimations. To address these challenges, this article proposes a robust visual odometry that can be implemented in a sampled-data structure. Comprehensive simulations and experiments are conducted to demonstrate the effectiveness of the proposed design and to explore the relationship between design parameters and estimation performance. In addition, tuning guidelines for visual odometry parameters are provided to help address these uncertainties effectively.

Research and Implementation of Intelligent Control System for Grassland Grazing Robotic Vehicle

In response to the complex animal husbandry environment, wide livestock range, and labor shortage in Inner Mongolia grasslands, this study designed an intelligent control system for robotic vehicles used in natural grazing grassland environments. The control system consists of software and hardware components and motion control algorithms. Based on the application characteristics of different mechanisms of the robotic vehicle, the motion control of the robotic vehicle is decomposed into longitudinal speed control and lateral steering control. The longitudinal speed control adopts the traditional Proportional-Integral-Differential (PID) control method. Since the hardware composition and internal angle calculation method of the steering system are complex and prone to deviations caused by external terrain, a lateral adaptive fuzzy PID controller was constructed with lateral deviation and deviation change rate as input variables and parameters Kp, Ki, and Kd as outputs. The field test results show that when the longitudinal speed was set to 0.5 m/s, the adjustment time was 1.95 s, and the steady-state error was about 0. When the longitudinal speed was the same, the lateral adaptive fuzzy PID controller reduced the rise time by 0.77 s compared to traditional control methods, reduced overshoot and steady-state error by 2%, and quickly recovered to a steady state after being disturbed. The following test results show that the designed intelligent control system can achieve real-time tracking of targets and that the motion controllers can effectively control speed and steering angle. Therefore, the intelligent control system designed in this paper can effectively achieve stable and real-time control of the robotic vehicle.

Model-Free Adaptive Disturbance Rejection Control of An RSV With Hardware-in-The-Loop Experiments

This paper reports a model-free adaptive disturbance rejection control method for the surge velocity tracking of a robotic surface vehicle (RSV) in the presence of unknown external disturbances, internal dynamics, and control coefficient. Specifically, reduced-order and full-order filtering adaptive extended state observers (FAESOs) are proposed at first, for estimating the unknown control coefficient, and the lumped disturbance consisting of the unknown external disturbances and internal dynamics, simultaneously. Based on the FAESO, a model-free adaptive disturbance rejection surge velocity tracking control law is derived. A key advantage of the proposed method is that it results in uniform control performance within large variation range of control coefficient regardless of the fully unknown model. The efficacy of the developed model-free adaptive disturbance rejection control method for the surge velocity tracking of RSV is substantiated by comparing simulations and hardware-in-the-loop experiments.

Improving the method of controlling the search for surface and underwater objects by a hybrid robotic complex

One of the important scientific tasks of organising the control of searching for surface and underwater objects is to ensure the efficiency and trouble-free movement of a hybrid robotic complex in the modes of transition and search operation. An analysis of modern methods of controlling the search for surface and underwater objects by robotic vehicles in search operations, scientific and environmental research, monitoring, inspection and military underwater operations shows the need to use hybrid robotic systems. As part of the intellectualisation of the route planning process, it is relevant to solve the problem of synthesising an optimal trajectory to ensure that the search is carried out in all designated areas of the operation. The paper solves the problem of organising the management of searching for surface and underwater objects by a hybrid robotic complex, namely, building a route in the area of interest as an optimal spline trajectory of a moving object. The possibility of constructing a navigation function through the effective use of the curvature of the spline trajectory as a reproductive template for constructing an axonometric projection is determined. To accomplish this task, the paper improves the method of controlling the search for surface and underwater objects by a hybrid robotic complex by formalising the processes of planning the route of the hybrid robotic complex and the processes of information processing, forecasting the navigation situation and choosing a strategy for preventing dangerous situations by combining intelligent and numerical methods. The developed algorithm for constructing a search route for a hybrid robotic complex allows automated formation of spline trajectories in real time with synchronous submission of information about the parameters of the route to the control system of the hybrid robotic complex. This approach makes it possible to automate the search operation process and apply it in the CoCoRo and e-Navigation concepts

Unsteady Propulsion of a Two-Dimensional Flapping Thin Airfoil in a Pulsating Stream

The cruising velocity of animals, or robotic vehicles, that use flapping wings or fins to propel themselves is not constant but oscillates around a mean value with an amplitude usually much smaller than the mean, and a frequency that typically doubles the flapping frequency. Quantifying the effect that these velocity fluctuations may have on the propulsion of a flapping and oscillating airfoil is of great relevance to properly modeling the self-propelled performance of these animals or robotic vehicles. This is the objective of the present work, where the force and moment that an oscillating stream exerts on a two-dimensional pitching and heaving airfoil are obtained analytically using the vortical impulse theory in the linear potential flow limit. The thrust force of the flapping airfoil in a pulsating stream in this limit is obtained here for the first time. The lift force and moment derived here contain new terms in relation to the pioneering work by Greenberg (1947), which are shown quantitatively unimportant. The theoretical results obtained here are compared with existing computational data for flapping foils immersed in a stream with velocity oscillating sinusoidally about a mean value.