Gazebo Simulator Research Articles

Model based Verification and Validation of LiDAR sensor

This research paper presents the study of modelling and validating the results of simulation of LiDAR sensor with actual sensor data. The model can be further utilized for virtual testing of various ADAS functions. The virtual sensor is a physics-based sensor capable of emulating the real sensor. Initially an in-depth study of Velodyne VLP-16 sensor was carried out in order to parameterize the virtual sensor model. The sensor is modelled virtually in Gazebo simulation environment. Gazebo is used along with ROS (Robotic Operating Interface) in order to record the sensor data. Data conversion from sensor message format to Cartesian coordinates has been carried out. This was done in order to have a common format of actual and virtual data for validation. Four different objects were selected for validating the sensor data. The validation methodology is based on comparison of actual recorded data generated by Velodyne VLP-16 LiDAR and virtually generated data by the virtual LiDAR sensor model. The validation proves that the in case of static scenarios, the virtual lidar sensor model is 99 % accurate in measuring the range to the object. Although there is slight deviation from the actual value, it is in the acceptable limits.

Multisensor Fusion SLAM Research Based on Improved RBPF‐SLAM Algorithm

Simultaneous localization and map construction (SLAM) technology provides the foundation for indoor robots to realize autonomous path planning. The Rao‐Blackwellized particle filtering (RBPF) algorithm is widely used to obtain information and perform map construction in unknown environments. This paper proposes a multisensor fusion algorithm that improves the RBPF‐SLAM algorithm by addressing the issues of particle distribution errors and degradation. To achieve this, the extended Kalman filter (EKF) is first adopted to effectively fuse odometry and inertial navigation (inertial measurement unit (IMU)) data as the initial positional information. Then, a high‐precision light detection and ranging (LIDAR) observation model is integrated to calculate the proposed distribution, and a threshold is introduced to determine the number of valid particles to simplify the resampling step. Finally, raster map construction experiments were carried out on both the Gazebo simulation environment and Robot Operating System (ROS)‐based mobile robot Keepbot platform in different scenarios. The experimental results demonstrate that the optimized RBPF‐SLAM algorithm generates a clearer and more complete map outline, which can effectively improve the accuracy of robot pose estimation, acquire a reliable 2D raster map with a reduced number of particles, and greatly reduces the computational effort.

외부 시스템을 이용한 Monocular camera-3D LiDAR 융합 시스템의 Non-overlapping 외부 교정 방법에 대한 연구

This study presents a novel framework for the extrinsic calibration of monocular camera and LiDAR with a non-overlapping field of view. The conventional non-overlapping extrinsic calibration utilizes a motion-based calibration method. However, motion-based calibration has a disadvantage in that the vehicle must be moving. In order to solve extrinsic calibration problems under such constraint configurations, the proposed solution uses external sensors that are located away from vehicle. First, the external camera and vehicle camera utilize an aruco marker. Second, the external LiDAR and vehicle LiDAR utilize a scan matching method. In this study, a Gazebo simulator was utilized to show the accuracy of the proposed method. The external LiDAR used Velodyne HDL 64ch, the vehicle LiDAR used Puck 16ch, and the camera used the Blackfly color sensor from FLIR.

Development of an unmanned aerial vehicle for remote live streaming on web dashboard

As the prominence of autonomous aerial vehicles continues to rise globally, the ability of surveillance carried out remotely is likely to become a key aspect. In large spaces, random security checks are performed by security guards and are quite inefficient since on-foot monitoring is extremely non-productive and slow. This paper proposes a remote surveillance solution integrated into an aerial vehicle that performs random inspections and streams the live video on a web dashboard which is a faster and more reliable solution. The paper presents how to design and implement an Unmanned Aerial Vehicle (UAV) with surveillance capabilities wherein the live stream will be remotely accessible on any device connected to the same network. The main purpose of this drone is to transmit live data/video to a web dashboard and hence run inferences to analyse the data. A custom-made drone is integrated with Raspberry Pi with a Universal Serial Bus (USB) camera. An attempt to stream drone video feeds on local devices has been made in this field. The proposed framework operates in the robot operating system (ROS) integrated with the gazebo simulator and is designed to enable the software to communicate with the hardware. Two primary methodologies are utilised; simulations and experimental. The expected outcome should satisfy the need to view the drone’s live video feed on a web dashboard for remote surveillance and computer vision application. At the resolution of 640 × 480, the latency in the live stream varied from 1 to 3 s with 10 frame rates per second. The analysis of the flight logs of the drone suggests that it meets the desired stability required for smooth video streaming. The total estimate calculated to implement this system is Rs. 70,000 including contingencies and spare parts required for experimentation. The proposed prototype appears to be a viable solution for security solutions required by multiple large space owners on the study's findings.

Walking Speed Control of Planar Bipedal Robot with Phase Control

In this study, we aim to provide the dynamic walking of a five-link planar bipedal robot without the need for optimization and regardless of initial conditions. Since bipedal robots are underactuated systems, classical control methods cannot be applied. Joint trajectories must be determined to control the robot. The trajectories that will allow the biped robot to walk can be derived with a trajectory optimization. Periodic stability and dynamic balance are provided by following the trajectories with virtual constraints method. However, determining the optimization-based trajectories before walking is the weakness of the virtual constraints method. As a solution, the method we call “phase control” is newly proposed. Thanks to the phase control method, the need for trajectory optimization is eliminated. Walking speed can be adjusted adaptively depending on user input without interrupting walking. Although the walking speed changes, it is shown with the limit cycle and Poincaré return map that the periodic stability is provided. In addition to mathematical model-based simulation, a physics engine-based Gazebo simulation is also performed to test the feasibility of the method. The advantage of the proposed phase control method is that the planned trajectories can provide limit cycles even if they are not optimized. This advantage provides freedom of trajectory selection that does not require optimization.

Image-based finite-time visual servoing of a quadrotor for tracking a moving target

This paper proposes an image-based visual servoing control method for a moving target of a quadrotor UAV (QUAV). Firstly, the dynamic image model with moving target parameters is established based on the image moment features in the virtual camera plane. For the unpredictability of the moving target in space, we use a high-order differentiator to estimate the state parameters of the moving target. In order to solve the problem of image depth information caused by a monocular camera, we derive a nonlinear finite-time linear velocity observer from the virtual image plane, which can not only estimate the linear velocity information of QUAV but also avoid the measurement of image depth. Based on the above information, we design the global finite-time controller and use Lyapunov theory to prove the finite-time stability of the system. Finally, the numerical simulations verify the convergence of the proposed control scheme, and the ROS gazebo simulations demonstrate the improved performance of the proposed control scheme in tracking error.

OCTUNE: Optimal Control Tuning Using Real-Time Data with Algorithm and Experimental Results

Autonomous robots require control tuning to optimize their performance, such as optimal trajectory tracking. Controllers, such as the Proportional-Integral-Derivative (PID) controller, which are commonly used in robots, are usually tuned by a cumbersome manual process or offline data-driven methods. Both approaches must be repeated if the system configuration changes or becomes exposed to new environmental conditions. In this work, we propose a novel algorithm that can perform online optimal control tuning (OCTUNE) of a discrete linear time-invariant (LTI) controller in a classical feedback system without the knowledge of the plant dynamics. The OCTUNE algorithm uses the backpropagation optimization technique to optimize the controller parameters. Furthermore, convergence guarantees are derived using the Lyapunov stability theory to ensure stable iterative tuning using real-time data. We validate the algorithm in realistic simulations of a quadcopter model with PID controllers using the known Gazebo simulator and a real quadcopter platform. Simulations and actual experiment results show that OCTUNE can be effectively used to automatically tune the UAV PID controllers in real-time, with guaranteed convergence. Finally, we provide an open-source implementation of the OCTUNE algorithm, which can be adapted for different applications.

Collision-free formation tracking control for multiple quadrotors under switching directed topologies: Theory and experiment

Multiple quadrotors are becoming increasingly attractive in many fields, and the formation tracking control of multiple quadrotors is the foundation for performing complex tasks. In this paper, the collision-free formation tracking control problem for multiple quadrotors under switching topology is studied, and the leader has the control command, which is unknown to the followers. A formation tracking control protocol is firstly constructed utilizing local neighboring information, in which the neighbors of the quadrotors can change. Then, inspired by the spring-damping system, a collision avoidance mechanism based on Hooke's law with damping is proposed to avoid collision of multiple quadrotors in a cluttered flight. By using the Lyapunov based stability analysis, it is proved that multiple quadrotors can track the dynamic leader in a dynamic formation using the proposed protocol under formation feasibility and the existence of spanning trees in communication topology. Finally, a lightweight communication protocol is designed in the experimental platform. Several groups of visual simulations are carried out based on the Gazebo simulator. The proposed method is compared with two potential field methods in two scenarios through quantitative studies of task efficiency and error analysis. Overall, the completion time is improved by 86.02% and 31.98%, and the minimum tracking error is improved by 57.24% and 46.20% in scenario 1. The completion time is improved by 86.88% and 30.34%, and the minimum tracking errors are improved by 82.20% and 42.62% in scenario 2. Outdoor experiments on formation tracking are performed to verify the effectiveness of the proposed methods. Furthermore, several outdoor experiments are conducted in the same scenarios to validate the proposed algorithm.

Research on Location Estimation for Coal Tunnel Vehicle Based on Ultra-Wide Band Equipment

Because the road surfaces of the underground roadways in coal mines are slippery, uneven, with dust and water mist, and the noise and light illumination effects are significant, global positioning system (GPS) signals cannot be received, which seriously affects the ability of the odometer, optical camera and ultrasonic camera to collect data. Therefore, the underground positioning of coal mines is a difficult issue that restricts the intellectualization of underground transportation, especially for automatic robots and automatic driving vehicles. Ultra-wide band (UWB) positioning technology has low power consumption, high performance and good positioning effects in non-visual environments. It is widely used in coal mine underground equipment positioning and information transmission. In view of the above problems, this research uses the WLR-5A mining unmanned wheeled chassis experimental platform; uses two UWB receivers to infer the position and yaw information of the vehicle in the underground roadway through the method of differential mapping; and tests the vehicle on the double shift line and quarter turn line in the GAZEBO simulation environment and on the ground simulation roadway to simulate the vehicle meeting conditions and quarter turning conditions in the underground roadway. The positioning ability of the method in these two cases is tested. The simulation and test results show that the vehicle position and attitude information deduced by two UWB receivers through the differential mapping method can basically meet the requirements of underground environments when the vehicle is traveling at low speeds.

Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm

Robots able to roll and jump are used to solve complex trajectories. These robots have a low level of autonomy, and currently, only teleoperation is available. When researching the literature about these robots, limitations were found, such as a high risk of damage by testing, lack of information, and nonexistent tools. Therefore, the present research is conducted to minimize the dangers in actual tests, increase the documentation through a platform repository, and solve the autonomous trajectory of a maze with obstacles. The methodology consisted of: replicating a scenario with the parrot robot in the gazebo simulator; then the computational resources, the mechanism, and the available commands of the robot were studied; subsequently, it was determined that the genetic micro-algorithm met the minimum requirements of the robot; in the last part, it was programmed in simulation and the solution was validated in the natural environment. The results were satisfactory and it was possible to create a parrot robot in a simulation environment analogous to the typical specifications. The genetic micro-algorithm required only 100 generations to converge; therefore, the demand for computational resources did not affect the execution of the essential tasks of the robot. Finally, the maze problem could be solved autonomously in a real environment from the simulations with an error of less than 10% and without damaging the robot.

Lyapunov-Based Nonlinear Model Predictive Control for Attitude Trajectory Tracking of Unmanned Aerial Vehicles

This paper presents a new Lyapunov-based nonlinear model predictive controller (LNMPC) for the attitude control problem of unmanned aerial vehicles (UAVs), which is essential for their functioning operation. The controller is designed based on a quadratic cost function integrating UAV dynamics and system constraints. An additional contraction constraint is then introduced to ensure closed-loop system stability. That constraint is fulfilled via a Lyapunov function derived from a sliding mode controller (SMC). The feasibility and stability of the LNMPC are finally proved. Simulation and comparison results show that the proposed controller guarantees the system stability and outperforms other state-of-the-art nonlinear controllers such as the backstepping controller (BSC) and SMC. In addition, the proposed controller can be integrated into an existing UAV model in the Gazebo simulator to perform software-in-the-loop tests. The results show that the LNMPC is better than the built-in PID controller of the UAV, which confirms the validity and applicability of our proposed approach.

Design of UVC Surface Disinfection Robot with Coverage Path Planning Using Map-Based Approach At-The-Edge

In response to the issue of virus contamination in the cold-chain warehouse or hospital environment under the influence of the COVID-19, we propose the design work of a disinfection robot based on the UVC radiation mechanism using the low-computational path optimization at-the-edge. To build a surface disinfection robot with less computing power to generate a collision-free path with shorter total distance in studies, a 2D map is used as a graph-based approach to automatically generate a closed-loop disinfection path to cover all the accessible surfaces. The discrete disinfection points from the map are extracted with effective disinfection distances and sorted by a nearest-neighbor (NN) search over historical trajectory data and improved A * algorithm to obtain an efficient coverage path to all accessible boundaries of the entire area. The purpose of improved A * algorithm with NN is not to find the optimal path solution but to optimize one with reasonable computing power. The proposed algorithm enhances the path-finding efficiency by a dynamically weighted heuristic function and reduces the path turning angles, which improves the path smoothness significantly requiring less computing power. The Gazebo simulation is conducted, and the prototype disinfection robot has been built and tested in a real lab environment. Compared with the classic A * algorithm, the improved A * algorithm with NN has improved the path-finding efficiency and reduced the path length while covering the same area. Both the simulation and experimental results show that this approach can provide the design to balance the tradeoffs among the path-finding efficiency, smoothness, disinfection coverage, and computation resources.

Binocular visual pendulum angle detection and anti-swing control of quadrotor suspension system

Different from the pendulum angle detection by the optical capture system in the indoor environment, it is difficult to detect the pendulum angle of quadrotor suspension system in the outdoor environment. This paper presents a pendulum angle vision measurement scheme based on the model of the quadrotor suspension system, and applied to the quadrotor anti-swing control scheme with feedforward controller. The theoretical expression of the coordinate relationship between the pendulum angle and the spherical symbol is obtained by establishing the mathematical model of the quadrotor suspension system. The coordinates of spherical symbol fixed to the payload are detected by the visual algorithm. Simulations and experiments show that proposed method can accurately identify spherical symbol in sufficient light, and the maximum measurement error of [Formula: see text] angle is −0.024 rad and [Formula: see text] angle is −0.068 rad in the Gazebo simulation. Meanwhile, the results in MATLAB simulation indicate the anti-swing control scheme with feedforward controller can effectively restrain the payload’s swing.

MR-TopoMap: Multi-Robot Exploration Based on Topological Map in Communication Restricted Environment

Multi-robot exploration in unknown environments is a fundamental task for a multi-robot system, involving inter-robot communication through messages among the robots. However, in a restricted communication environment, the limited communication resources become the system's bottleneck due to a large amount of data in the occupancy grid map. Hence, to enhance multi-agent exploration in communication-constrained environments, this letter develops a method to build topological maps while the robot moves in the environment and an exploration strategy based on the created topological map. The latter map comprises a set of vertices and edges connecting the vertices, where each vertex represents a specific area embedded with a descriptor extracted by visually observing this area and recognizing it utilizing descriptors. Each robot has its local grid map stored for path planning, not shared between them. Considering the exploration task, a robot's ability to choose a proper direction depends on the other robot's locations and the unexplored areas. Our exploration framework is evaluated on the Gazebo simulator and real robots, increasing the exploration efficiency by 23% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 77%. Compared with the occupancy grid map scheme, our method's data transfer is reduced by 84% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 90%.

Medium-Scale UAVs: A Practical Control System Considering Aerodynamics Analysis

Unmanned aerial vehicles (UAVs) have drawn significant attention from researchers over the last decade due to their wide range of possible uses. Carrying massive payloads concurrent with light UAVs has broadened the aeronautics context, which is feasible using powerful engines; however, it faces several practical control dilemmas. This paper introduces a medium-scale hexacopter, called the Fan Hopper, alimenting Electric Ducted Fan (EDF) engines to investigate the optimum control possibilities for a fully autonomous mission carrying a heavy payload, even of liquid materials, considering calculations of higher orders. Conducting proper aerodynamic simulations, the model is designed, developed, and tested through robotic Gazebo simulation software to ensure proper functionality. Correspondingly, an Ardupilot open source autopilot is employed and enhanced by a model reference adaptive controller (MRAC) for the attitude loop to stabilize the system in case of an EDF failure and adapt the system coefficients when the fluid payload is released. Obtained results reveal less than a 5% error in comparison to desired values. This research reveals that tuned EDFs function dramatically for large payloads; meanwhile, thermal engines could be substituted to maintain much more flight endurance.

Gazebo 시뮬레이터를 활용한 고층 건물에서의 이동 로봇 실내 자율주행 알고리즘 개발

Robots need a safe ride to the elevator to navigate in a multi-layered environment. When passing through a narrow space, it is difficult for the robot to enter the elevator door because it senses the risk of collision and cannot pass. Many studies and experiments are needed for robots to safely board elevators. However, conducting a study using robots using elevators in real-world environments is challenging because of collision problems with dynamic obstacles such as people. Therefore, we must use a simulator to create a simulation environment similar to the actual environment. In this paper, we propose a method to make a multi-layered environment using the Gazebo simulator and an algorithm to board an elevator safely using an IMU sensor, LiDAR, and a camera.

Autonomous underwater vehicle formation control and obstacle avoidance using multi-agent generative adversarial imitation learning

Autonomous underwater vehicle (AUV) is widely used in complex underwater missions such as bottom survey and data collection. Multiple AUVs can cooperatively complete tasks that single AUV cannot accomplish. Recently, multi-agent reinforcement learning (MARL) has been introduced to improve multi-AUV control in uncertain marine environments. However, it is very difficult and even unpractical to design effective and efficient reward functions for various tasks. In this paper, we implemented multi-agent generative adversarial imitation learning (MAGAIL) from expert demonstrated trajectories for formation control and obstacle avoidance of multi-AUV. In addition, decentralized training with decentralized execution framework was adopted to alleviate the communication problem in underwater environments. Moreover, to facilitate the discriminator to accurately judge the quality of AUV’s trajectory in the two tasks and increase the convergence speed, we improved upon MAGAIL by dividing the state–action pairs of expert trajectory for each AUV into two groups and updating discriminator by randomly selecting equal number of state–action pairs from both groups. Our experimental results on a simulated AUV system modeling Sailfish 210 of our lab in the Gazebo simulation environment show that MAGAIL allows control policies of multi-AUV to obtain a better performance than traditional multi-agent deep reinforcement learning from fine-tuned reward function — IPPO. Moreover, control policies trained via MAGAIL in simple tasks can generalize better to complex tasks than those trained via IPPO.

ОЦЕНКА СИСТЕМ КООРДИНАТНЫХ МЕТОК С ИСПОЛЬЗОВАНИЕМ БОРТОВОЙ КАМЕРЫ МОБИЛЬНОГО РОБОТА «СЕРВОСИЛА ИНЖЕНЕР» В УСЛОВИЯХ ЗАКРЫТЫХ ПОМЕЩЕНИЙ

Modern tasks of special areas of robotics, including search and rescue operations in urbanareas, face a number of obstacles to mobile robotics, where the automatic performance of various functionsby mobile robots remains a key task. One of the important requirements for the algorithms andsoftware of the robot is the possibility of autonomous decision-making and automatic performance bythe robot of various functions, both low and high levels based on the embedded algorithms and informationreceived from the on-board sensors of the robot. To date, the most common on-board robot sensorsare cameras of various types, due to their technical capabilities and lower cost relative to lidarsand other sensors that provide visual information in the form of digital images. Camera calibration is anecessary process for extracting accurate information from digital images. This process is necessary toobtain an exact correspondence between the three-dimensional object space and the pixel space of theimage, for the possibility of subsequent use of computer vision algorithms, aggregation, and informationprocessing. Calibration of digital cameras is an integral part of a number of practical tasks of machinevision such as navigation of mobile robotic systems, medicine, reconstruction of dense and sparse threedimensionalmaps of the environment, video surveillance and visual inspection, visual simultaneouslocalization and mapping, etc. The urgency of the problem of camera calibration is defined by the presenceof many different methods of calibration and calibration templates. Each individual solution issuitable only for special conditions, e.g., lack of lighting, bad weather conditions, the presence of thirdpartyobjects blocking visibility, etc. In most cases, each calibration method uses a specific calibrationpattern. Camera calibration is usually associated with the use of special calibration templates. Theyallow to achieve the most accurate results due to a previously known geometric structure. Currently, theprocedure for camera calibrating of robotic systems is carried out in laboratory conditions using theclassic “chessboard” method. In addition, there are only a few alternative approaches that are in their infancy state both in Russia and abroad. On the other hand, research into camera calibration methodscontinues and new alternatives for camera calibration are emerging. One of the new directions is theuse of fiducial marker systems as a reference object. A variety of parameters such as the size of the calibrationtemplate, the dimension of the calibration data set, the distribution of distances from the camerato objects on the stage, etc. creates a vast area for experimental testing of optimal camera calibrationparameters. This paper presents a research of automatic camera calibration using fiducial marker systems(FMS), which are located on the surface of the robot. Based on the results of virtual experimentswith FMS in the Gazebo simulation environment of the robotic operating system ROS, two differenttypes of FMS were selected that are optimal relative to other types of FMS covered by our previousstudies in terms of the resistance of FMS to systematic occlusion of the marker area and the effect ofmarker size on quality of its recognition. The selected FMS were tested using the onboard camera of theRussian mobile robot Servosila Engineer in indoor settings to assess the correlation of results in virtualand real environments.

Autonomous Driving Control Based on the Perception of a Lidar Sensor and Odometer

Recently, many artificial intelligence applications in smart cars have been utilized in real life. Making an unmanned ground vehicle (UGV) capable of moving autonomously has become a critical topic. Hence, in this work, a novel method for a UGV to realize path planning and obstacle avoidance is presented using a deep deterministic policy gradient approach (DDPG). More specifically, the lidar sensor mounted on the vehicle is utilized to measure the distance between the vehicle and the surrounding obstacles, and the odometer measures the mileage of the vehicle for the purpose of estimating the current location. Then, the above sensed data are treated as the training data for the DDPG training procedure, and several experiments are performed in different settings utilizing the robot operating system (ROS) and the Gazebo simulator with a real robot module, TurtleBot3, to present a comprehensive discussion. The simulation results show that using the presented design and reward architecture, the DDPG method is better than the classic deep Q-network (DQN) method, e.g., taking fewer steps to reach the goal, less training time to find the smallest number of steps for reaching the goal, and so on.

Gazebo – Simulink Framework for Trajectory Tracking in a Multi-Quadcopter Environment

Physical testing of quadcopter controllers is challenging as the quadcopters are vulnerable to damages, and the cost needed for setting up a flying arena is enormous. This work aims to establish a simulation framework that can be used to analyze and verify the performance of the control algorithms developed for quadcopters before actually deploying them in the actual quadcopters. The framework seamlessly integrates the controller models developed in MATLAB/Simulink and the physics-based Gazebo simulator through Robot Operating System. The framework is extended to simulate multiple quadrotors and to test the performance of each of their control algorithms. In this work, two independent Proportional, Integral, Derivative (PID) controllers developed in Simulink have been used to control two different Crazyflie quadcopters to make them follow the desired trajectories. The framework can be used for pedagogy and by the researchers for developing and testing the performance of control algorithms developed for any of the physics-based quadcopter models in the Gazebo simulator.