Robot Operating System Research Articles

DARLOS: a lightweight dual-arm robotic live-line operation system for autonomous high-voltage distribution grid maintenance

Purpose This paper aims to present a novel lightweight distribution grid operating robot system with focus on lightweight and multi-functionality, aiming for autonomous and live-line maintenance operations. Design/methodology/approach A ground-up redesign of the dual-arm robotic system with 12-DoF is applied for substantial weight reduction; a dual-mode operating control framework is proposed, with vision-guided autonomous operation embedded with real-time manual teleoperation controlling both manipulators simultaneously; a quick-swap tooling system is developed to conduct multi-functional operation tasks. A prototype robotic system is constructed and validated in a series of operational experiments in an emulated environment both indoors and outdoors. Findings The overall weight of the system is successfully brought down to under 150 kg, making it suitable for the majority of vehicle-mounted aerial work platforms, and it can be flexibly and quickly deployed in population dense areas with narrow streets. The system equips with two dexterous robotic manipulators and up to six interchangeable tools, and a vision system for AI-based autonomous operations. A quick-change tooling system ensures the robot to change tools on-the-go without human intervention. Originality/value The resulting dual-arm robotic live-line operation system robotic system could be compact and lightweight enough to be deployed on a wide range of available aerial working platforms with high mobility and efficiency. The robot could both conduct routine operation tasks fully autonomously without human direct operation and be manually operated when required. The quick-swap tooling system enables lightweight and durable interchangeability of multiple end-effector tools, enabling future expansion of operating capabilities across different tasks and operating scenarios.

Navigation system for orchard spraying robot based on 3D LiDAR SLAM with NDT_ICP point cloud registration

Autonomous navigation of orchard spraying robots can effectively improve operational efficiency and reduce workload. The conventional robot navigation solutions based on the Global Navigation Satellite System (GNSS) are susceptible to signal loss in orchards due to interference from the canopy of fruit trees. In this paper, an autonomous navigation scheme for orchard spraying robots based on multi-sensor devices and the three-dimensional (3D) LiDAR Simultaneous Localization and Mapping (SLAM) technology. The initial step of the scheme involved the construction of a 3D point cloud map of the orchard using the 3D LiDAR SLAM algorithm. Subsequently, a designed point cloud map transformation algorithm was used to convert the 3D point cloud map of the orchard into a two-dimensional (2D) grid map containing key feature information of the orchard. To enhance the mapping accuracy of the orchard point cloud, a point cloud registration algorithm combining Iterative Closet Point (ICP) and Normal Distributions Transform (NDT) was proposed. This algorithm initially utilized the NDT algorithm for coarse point cloud registration to obtain transformation parameters. Then, the ICP algorithm was applied in conjunction with the obtained parameters for fine registration. Regarding obstacle avoidance, a cooperative obstacle avoidance algorithm based on the Robot Operating System (ROS) multithreaded communication mechanism was introduced. Experimental trials conducted in a standardized spindle-shaped peach orchard validated the successful completion of the SLAM mapping trajectory, which was achieved through the utilization of the proposed NDT_ICP point cloud registration algorithm. The average absolute pose error between the mapped trajectory and the reference trajectory was 1.173 m, with a standard deviation of 0.498 m. The robot's positioning and navigation errors increased with higher speeds, with the largest positioning deviation observed at 1.2 m/s speed, with a maximum lateral positioning error of 7.04 cm. The robot's average lateral navigation error did not exceed 16 cm, and the average heading deviation was less than 8° at different speeds of 0.4, 0.8 and 1.2 m/s. Both the positioning and navigation accuracies of the robot were sufficient to meet the autonomous operational requirements of orchard spraying robots.

ROBUST: 221 bugs in the Robot Operating System

As robotic systems such as autonomous cars and delivery drones assume greater roles and responsibilities within society, the likelihood and impact of catastrophic software failure within those systems is increased. To aid researchers in the development of new methods to measure and assure the safety and quality of robotics software, we systematically curated a dataset of 221 bugs across 7 popular and diverse software systems implemented via the Robot Operating System (ROS). We produce historically accurate recreations of each of the 221 defective software versions in the form of Docker images, and use a grounded theory approach to examine and categorize their corresponding faults, failures, and fixes. Finally, we reflect on the implications of our findings and outline future research directions for the community.

Computer simulation and hands-on labs: A case study of teaching robotics and AI

When teaching robotics, instructors face the challenge of finding an effective approach to bridge theoretical concepts and practical applications. Both computer simulations and hands-on laboratory experiments provide learners with opportunities for active, immersive, and experiential learning. As students progress from introductory to advanced topics and from theory to practice, their performance is contingent upon earlier knowledge and may increase, remain unchanged, or decrease. The question that arises is whether computer simulation can serve as a viable foundation for fostering an understanding of theory that enables the subsequent grasp of advanced practical concepts in robotics. Put another way, when students are introduced to the field of robotics through computer simulation, how will they perform when presented with advanced hands-on tasks involving the construction of physical robots to solve problems in physical space? To answer this question, we examined undergraduate student performance ( n = 107) across two robotics courses—an introductory course using computer simulation (Robot Operating System, Rviz, and GAZEBO) and an advanced course using physical hardware (Puzzlebot), leveraging the hardware's capability for AI tasks such as machine vision (Nvidia Jetson Nano development kit). Our findings suggest that student performance increased as they progressed from using computer simulation to engaging with hardware in the physical environment, further suggesting that teaching with computer simulations provides an adequate foundation to learn and complete more advanced tasks.

Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.

Dynamic path planning via Dueling Double Deep Q-Network (D3QN) with prioritized experience replay

Path planning is a key requirement for mobile robots employed for different tasks such as rescue or transport missions. Conventional methods such as A* or Dijkstra to tackle path planning problem need a premise map of the robot's environment. Nowadays, dynamic path planning is a popular research topic, which drives mobile robots without prior static requirements. Deep reinforcement learning (DRL), which is another popular research area, is being harnessed to solve dynamic path planning problem by the researchers. In this study, Deep Q-Networks, which is a subdomain of DRL are opted to solve dynamic path planning problem. We first employ well known techniques Double Deep Q-Networks (D2QN) and Dueling Double Deep Q-Networks (D3QN) to train a model which can drive a mobile robot in environments with static and dynamic obstacles within 3 different configurations. Then we propose D3QN with Prioritized Experience Replay (PER) extension in order to further optimize the DRL model. We created a test bed to measure the performance of the DRL models against 99 randomly generated goal locations. According to our experiments, D3QN-PER method performs better than D2QN and D3QN in terms of path length and travel time to the goal without any collisions. Robot Operating System and Gazebo simulation environment is utilized to realize the training and testing environments, thus, the trained DRL models can be deployed to any ROS compatible robot seamlessly.

Autonomous Robotic System to Prune Sweet Pepper Leaves Using Semantic Segmentation with Deep Learning and Articulated Manipulator.

This paper proposes an autonomous robotic system to prune sweet pepper leaves using semantic segmentation with deep learning and an articulated manipulator. This system involves three main tasks: the perception of crop parts, the detection of pruning position, and the control of the articulated manipulator. A semantic segmentation neural network is employed to recognize the different parts of the sweet pepper plant, which is then used to create 3D point clouds for detecting the pruning position and the manipulator pose. Eventually, a manipulator robot is controlled to prune the crop part. This article provides a detailed description of the three tasks involved in building the sweet pepper pruning system and how to integrate them. In the experiments, we used a robot arm to manipulate the pruning leaf actions within a certain height range and a depth camera to obtain 3D point clouds. The control program was developed in different modules using various programming languages running on the ROS (Robot Operating System).

Integrated Route-Planning System for Agricultural Robots

Within the transition from precision agriculture (task-specific approach) to smart farming (system-specific approach) there is a need to build and evaluate robotic systems that are part of an overall integrated system under a continuous two-way connection and interaction. This paper presented an initial step in creating an integrated system for agri-robotics, enabling two-way communication between an unmanned ground vehicle (UGV) and a farm management information system (FMIS) under the general scope of smart farming implementation. In this initial step, the primary task of route-planning for the agricultural vehicles, as a prerequisite for the execution of any field operation, was selected as a use-case for building and evaluating this integration. The system that was developed involves advanced route-planning algorithms within the cloud-based FMIS, a comprehensive algorithmic package compatible with agricultural vehicles utilizing the Robot Operating System (ROS), and a communicational and computational unit (CCU) interconnecting the FMIS algorithms, the corresponding user interface, and the vehicles. Its analytical module provides valuable information about UGVs’ performance metrics, specifically performance indicators of working distance, non-working distance, overlapped area, and field-traversing efficiency. The system was demonstrated via the implementation of two robotic vehicles in route-execution tasks in various operational configurations, field features, and cropping systems (open field, row crops, orchards). The case studies showed variability in the operational performance of the field traversal efficiency to be between 79.2% and 93%, while, when implementing the optimal route-planning functionality of the system, there was an improvement of up to 9.5% in the field efficiency. The demonstrated results indicate that the user can obtain better control over field operations by making alterations to ensure optimum field performance, and the user can have complete supervision of the operation.

Autonomous Navigation System for a Differential Drive Mobile Robot

Abstract Mobile robotics is a rapidly expanding field of research because of its large number of applications. Autonomous mobile robots (AMRs) are used in multiple areas, such as industrial automation, logistics and warehouse management, museums, hospitals and restaurant assistance, space and ocean exploration, and many others. In this work, a navigation system for a low-cost mobile robot is proposed. It relies on state-of-the-art software suited for designing automated systems, robot applications, and computer vision algorithms. The robot is driven by two wheels powered by Beckhoff motors, controlled using Beckhoff’s TwinCAT 3 automation software in an industrial computer. A separate small-sized Raspberry Pi computer handles the environment perception by processing the information acquired by a Lidar module and a webcam and the localization and planning through the Robot Operating System (ROS). Communication between the two computers yields a complete robot navigation system. This system is tested and subsequently tuned to achieve good performance. Experimental tests show that the navigation system is globally effective, and some limitations related to the robot’s design and its navigation subsystem are discussed. The approach proposed in this work can be extended, adjusted, and used in other types of mobile robots.

Autonomous navigation system for hexa-legged search and rescue robot using LiDAR

This study proposes an autonomous navigation system for hexapod robots, promising in complex rescue scenarios. The system is tested in simulations under three environments: rocky, cracked flooring, and inclined surfaces. utilizing light detection and ranging (LiDAR) and simultaneous localization and mapping (SLAM), the robot recognizes positions and constructs environmental maps. Implemented via robot operating system, the research successfully applies navigation and mapping using hector_slam. LiDAR mapping yields satisfactory accuracy, with average errors of 0.21% for general mapping and 5.34% for circular paths. Within a 2-meter range, navigation achieves good accuracy, averaging 1.2% error on the x-axis and 0.011% on the y-axis during linear motion. Navigational repeatability improves, with reliable results showing an average error of 4.33 cm on the x-axis and 0.5 cm on the y-axis when returning to starting points. Arena testing with varied obstacles demonstrates successful obstacle traversal. However, in the second test, limitations in hardware, notably the Raspberry Pi 4 CPU usage reaching 97% during navigation, hindered reaching the third target.

Cable-driven agribot prototype: Enabling precision agriculture through innovative design

The ongoing depopulation of rural areas and the increasing demand for high-quality food, along with the need to improve the quality of work in terms of stress, injuries, and social disputes, needs significant attention towards rethinking and organizing agricultural practices to meet these needs and improve the quality of life. These changes call for the implementation of new reforms, incentive systems, and the exploration of innovative systems for field operations. In this context, the research, design, and commercialization of electrified autonomous and collaborative systems capable of precision agriculture applications, known as agribots, are gaining paramount importance. This article presents an innovative multi-robot for smart farming. The agribot is composed by a lightweight cart designed to traverse crop field rows through a cable-way system installed on two heavy carts equipped with a power train system and PV rooftop panels. The agribot is conceived to achieve a high level of autonomy, reduce energy consumption, and minimize soil compaction while working on flat fields with low-height crops. The article is structured in two parts: the first part describes the agribot's ICT network, the heavy carts electric grid and the power systems, providing an energy consumption analysis related to the traversing of a crop field row.The second part focuses on the control, actions coordination, and supervision system (supervisor), implemented using Robot Operating System (ROS). The supervisor system, driven by a decision-making system based on a finite state machine, oversees the execution of path planning for the agribot, providing high modularity and flexibility to adapt to various system actions.

Weather-adaptive slam using unmanned ground vehicles

This paper presents a weather-adaptive SLAM system for Unmanned Ground Vehicles (UGVs) equipped with lidar, radar, and RGBD-camera sensors. These sensors and functionalities are built in Robot Operating System (ROS) Noetic. The system dynamically selects the best sensor combination along with the corresponding slam strategies based on real-time weather data acquired through the Python web crawler. Our work ensures accurate mapping and localization in various and fast-changing weather conditions, enhancing SLAM reliability and accuracy.

BI-RRT*: An improved path planning algorithm for secure and trustworthy mobile robots systems

The optimal RRT in elliptic space sampling (Informed-RRT*) is an extension of RRT that provides asymptotic optimality, however, it experiences gradual progress and close to obstacles. In the paper, we propose a novel path planning algorithm guided bidirectional Informed-RRT* (BI-RRT*), that introduces extension range, dual-direction exploration, and refinement in trajectory design. The growth range refers to maintaining an additional area from the obstacle to enhance the dependability of the path through preventing impacts. Bidirectional search is a search strategy using both start and target points for a initial solution. Smoothing improves path robustness by using cubic spline. Furthermore, simulation tests for the BI-RRT* algorithm are executed, and the efficacy of the suggested algorithm is confirmed through its application in a robot operating system (ROS). Simulations and experimental tests verify that the proposed algorithm improves the path planning capability. We emphasize the importance of safety, privacy, and reliability in the deployment of AI systems. Our algorithm ensures that the planned paths maintain a safe distance from obstacles, reducing the risk of collisions. Additionally, we prioritize privacy by adhering to data protection regulations and implementing secure communication protocols within the AI system. Moreover, we have applied rigorous testing and validation processes to enhance the reliability of our algorithm, ensuring consistent and accurate path planning outcomes.

ARSIP: Automated Robotic System for Industrial Painting

This manuscript addresses the critical need for precise paint application to ensure product durability and aesthetics. While manual work carries risks, robotic systems promise accuracy, yet programming diverse product trajectories remains a challenge. This study aims to develop an autonomous system capable of generating paint trajectories based on object geometries for user-defined spraying processes. By emphasizing energy efficiency, process time, and coating thickness on complex surfaces, a hybrid optimization technique enhances overall efficiency. Extensive hardware and software development results in a robust robotic system leveraging the Robot Operating System (ROS). Integrating a low-cost 3D scanner, calibrator, and trajectory optimizer creates an autonomous painting system. Hardware components, including sensors, motors, and actuators, are seamlessly integrated with a Python and ROS-based software framework, enabling the desired automation. A web-based GUI, powered by JavaScript, allows user control over two robots, facilitating trajectory dispatch, 3D scanning, and optimization. Specific nodes manage calibration, validation, process settings, and real-time video feeds. The use of open-source software and an ROS ecosystem makes it a good choice for industrial-scale implementation. The results indicate that the proposed system can achieve the desired automation, contingent upon surface geometries, spraying processes, and robot dynamics.

Navigation Path Following Platform for a Greenhouse Shuttle Robot Using the State-flow Method

Localization is an important method for autonomous indoor robots to recognize their positions. Generally, the navigation of a mobile robot is conducted using a camera, Lidar, and global positioning system. However, for an indoor environment, GPS is unavailable. Therefore, a, state-trajectory tracking method is utilized based on a Lidar map. This paper presents the path following of an autonomous indoor mobile robot, that is, a shuttle robot, using a state-flow method via a robot operating system network. MATLAB and Linux high-level computers and an inertial measurement unit sensor are used to obtain the Cartesian coordinate information of a bicycle-type mobile robot. The path following problem can be solved in the state-flow block by setting appropriate time and linear and angular velocity variables. After the predetermined time, the linear and angular velocities are set based on the length of the path and radius of the quarter-circle of the left and right turns in the state-flow block, path planning, which can execute the work effectively, is established using the state-flow algorithm. The state-flow block produces time-series data that are sent to Linux system, which facilitates real-time mobile platform path following scenario. Several cases within the path-following problem of the mobile robot were considered, depending on the linear and angular velocity settings: the mobile robot moved forward and backward, turned in the right and left directions on the circular path. The effectiveness of the method was demonstrated using the desktop-based indoor mobile robot control results. Thus, the paper focuses on the application of the state-flow algorithm to the shuttle robot specifically in the narrow indoor environment.

Robotic system for indoor illuminance map generation

The paper describes Romulux, a mobile robot equipped with 3D LiDAR carrying an illuminance meter and dedicated to measure indoor lighting quantity. The aim is to check compliance with lighting standards in terms of level and uniformity. The general architecture of the robot using the Robot Operating System (ROS) is presented, and technical choices are explained according to the different constraints. The space localization of illuminance data is computed with Synchronous Localization And Mapping (SLAM) algorithms. Then measurements are compared to standards requirements and interpolated into a user-defined grid to generate a dense illuminance map. Two experiments, one in a sport hall and one in an ice rink, are presented and results are exposed. The paper will follow the workflow from designing a robot to a functional system able to generate illuminance maps, compare with standards and save energy in case of over lighting.

The Navigation of Home Service Robot Based on Deep Learning and Machine Learning

This paper discusses how to improve the accuracy of navigation for home service robot based on the deep learning and machine learning. First, the crawling programing is applied to collect enough images of fridge and washing machine on the web; a deep learning framework is proposed that can distinguish fridge and washing machine more accurately. Following, the data come from the robot operating system topics are collected and cleaned, the linear regression, decision tree, and linear SVR algorithms are applied and compared to predict the power consumption of the robot, and a conclusion is obtained that liner movement will consume more power, which provides a reference for the path planning of the robot. Lastly, the conclusions are proposed that a novel methodology is applied to distinguish different home appliances, which is useful for the accurate navigation of the robot; the liner movement will consume more power compared to turning left or right, which supplies a reference for the optimized path planning for the robot.

Autonomous Quadcopter for Surveillance

The use of drone technology is becoming increasingly popular. Finding applications, across various industries. In agriculture drones are employed for tasks such as applying pesticides and fertilizers monitoring crops and creating maps. The construction sector utilizes drones to inspect job sites and track progress. Additionally, the film industry benefits from photography using drones while search and rescue operations rely on them to reach areas. Overall drone technology has the potential to revolutionize industries by offering solutions to previously challenging problems. We propose the development of an AI based autonomous drone system that can be deployed in areas for applications including surveillance and warehouse management. This system incorporates a PI CAM V2 camera mounted on a quadcopter equipped with GPS technology. The drone operates autonomously without the need for a pilot thanks to software, like Robot Operating System (ROS) other cutting-edge technologies

A Portable Artificial Robotic Nose for CO2 Concentration Monitoring

The technological advancements in sensory systems and robotics over the past decade have facilitated the innovation of centralized systems for optimizing resource utilization and monitoring efficiency in inspection applications. This paper presents a novel system designed for gas concentration sensing in environments by implementing a modular artificial nose (emulating the inhalation and exhalation process) equipped with a strategically designed air capture centralization system based on computational fluid dynamics analysis (CFD). The system incorporates three gas identification sensors distributed within the artificial nose, and their information is processed in real-time through embedded systems. The artificial nose is hardware–software integrated with a quadruped robot capable of traversing the environment to collect samples, maximizing coverage area through its mobility and locomotion capabilities. This integration provides a comprehensive perspective on gas distribution in a specific area, enabling the efficient detection of substances in the surrounding environment. The robotic platform employs a graphical interface for real-time gas concentration data map visualization. System integration is achieved using the Robot Operating System (ROS), leveraging its modularity and flexibility advantages. This innovative robotic approach offers a promising solution for enhanced environmental inspection and monitoring applications.

Design and analysis of an autonomous warehouse robot system with 6-DOF manipulator

With the increasing need for efficiency and accuracy in warehouse operations, the functions and market demands of automated warehouse robots are constantly increasing. This study presents the design, simulation, and implementation of a warehouse robot, showcasing effective automation solution. Leveraging the Robot Operating System (ROS) and Gazebo, a robot with a six-degree-of-freedom robotic arm for diverse manipulation tasks and a differential drive base for broad-spectrum navigation was designed. The simulation environment in Gazebo faithfully replicates real-world warehouse conditions, enabling comprehensive path planning and real-time modifications, powered by move_base. A camera sensor serves as the robot's safety system, designed to detect moving obstacles and initiate appropriate responses, contributing to the enhancement of warehouse safety standards. Simulation results demonstrate the robot's effectiveness in performing pick-and-place tasks while successfully navigating through the environment, indicating the significant potential for real-world warehouse automation applications. Therefore, this work provides a foundation reference for future research aimed at optimizing and expanding the capabilities of autonomous warehouse robots.