Robot Operating Research Articles

Evaluation of Robot Motion Trajectory Based on Selected Mapping Algorithms

This paper presents the concept of a remotely controlled mobile robot that generates a two-dimensional map of its surroundings. The hardware platform developed relies on the LINUX operating system with the Robot Operating System (ROS) to function properly. The authors focused on discussing the robot's hardware and presenting the software used. In line with the assumptions made, the robot is capable of generating a two-dimensional digital map of its surroundings, as well as of recording images of those surroundings. The robot relies on lidar odometry for identifying its position, meaning that the developed algorithm calculates the vehicle's location based on data from a laser scanner. The main sources of environment-related information acquired by the robot include the following: RPLidar A3M1 laser scanner by Slamtec (generating a digital map) and a 5mpx HD OV5647 camera (capturing images of the surroundings). These devices are coupled with the Raspberry Pi 3B on-board computer via a CSI interface.

Developments of a centimeter-level precise muometric wireless navigation system (MuWNS-V) and its first demonstration using directional information from tracking detectors

Various positioning techniques such as Wi-Fi positioning system have been proposed to use in situations where satellite navigation is unavailable. One such system, the muometric positioning system (muPS), was invented for navigation which operates in locations where even radio waves cannot reach such as underwater or underground. muPS takes advantage of a key feature of its probe, cosmic-ray muons, which travel straightforwardly at almost a speed of light in vacuum regardless of the matter they traverse. Similar to other positioning techniques, muPS is a technique to determine the position of a client’s muPS receiver within the coordinate defined by reference detectors. This can be achieved either by using time-of-flight (ToF) or angle of arrival (AoA) measurements. The latter configuration (AoA), called the Vector-muPS has recently been invented and the present paper describes the developments of the first prototype of a vector muometric wireless navigation system (MuWNS-V) with this new vector-muPS concept and its demonstration. With MuWNS-V, the reference tracker and the receiver ran wirelessly with fully independent readout systems, and a positioning accuracy of 3.9 cm (RMS) has been achieved. We also evaluated the outcome of measuring continuous indoor localization of a moving receiver with this prototype. Our results indicated that further improvements in positioning accuracy will be attainable by acquiring higher angular resolution of the reference trackers. It is anticipated that “sub-cm level” navigation will be possible for muPS which could be applied to many situations such as future autonomous mobile robot operations.

Autonomous Driving System Architecture with Integrated ROS2 and Adaptive AUTOSAR

In the automotive industry, research is now underway to apply Adaptive Automotive Open System Architecture (AUTOSAR) to the development of next-generation mobility, such as autonomous driving and connected cars. However, research on autonomous driving is being predominantly conducted on the robotics platform ROS2 (Robot Operating System 2). This demonstrates a considerable distance between autonomous driving research and its application in actual vehicles. To bridge this gap, interoperability that leverages the strengths of the Adaptive AUTOSAR and ROS2 platforms and compensates for their weaknesses is required. Therefore, this study proposes an architecture for interoperability between the two platforms, named Autonomous Driving System with Integrated ROS2 and Adaptive AUTOSAR (ASIRA). The proposed architecture enables communication between each of the two platforms through the ROS2 SOME/IP Bridge and allows for the necessary data exchange. It validates them in autonomous driving scenarios and goes beyond vehicle development, testing, and prototyping to exploit the advantages of each platform. Additionally, the simulation of autonomous vehicles within the ASIRA architecture is demonstrated by interoperating the ROS2 representative open-source autonomous driving project, Autoware, with the Adaptive AUTOSAR simulator. This study contributes to the assimilation of ROS2 into the automotive industry and its application in real vehicles by linking ROS2 and Adaptive AUTOSAR.

Targeted Sampling Dynamic Window Approach: A Path-Aware Dynamic Window Approach Sampling Strategy for Omni-Directional Robots

Abstract This paper describes a sampling strategy for the dynamic window approach (DWA), a local path planner, for omni-directional robot motion planning. An efficient local planner allows the robot to quickly respond to dynamic obstacles and ensures that commanded velocities meet the dynamic constraints of the robot. While typical DWA implementations sample the velocity space evenly, we propose that targeted sampling (TS) will result in a more fine-grained search of the relevant velocity space, leading to better control and performance in space-constrained environments. Our TS-DWA strategy is informed by the global planned path, allowing us to sample more velocities in the general path direction. We employ a polar velocity generator to selectively sample velocities and couple angular velocity samples to the path curvature. A bias for angular velocity is added for robots with a preferred heading, such as robots with forward-mounted sensors, to quickly turn toward the desired direction for better sensing. The strategy is implemented as a robot operating system (ROS) navigation stack local_planner plugin and tested in simulation with Gazebo using an omni-directional robot platform. Experiments show that as the space around the simulated robot gets smaller, our proposed sampling strategy results in more successful navigation trials to the goal in space-constrained environments compared to other commonly used methods like DWA and timed-elastic-band, where planning fails or oscillates. TS-DWA was also deployed on the Vision60 quadrupedal robot to demonstrate navigation through narrow corridors in the real world.

ROSIC: Enhancing secure and accessible robot control through open‐source instant messaging platforms

AbstractEnsuring secure communication and seamless accessibility remains a primary challenge in controlling robots remotely. The authors propose a novel approach that leverages open‐source instant messaging platforms to overcome the complexities and reduce costs associated with implementing a secure and user‐centred communication system for remote robot control named Robot Control System using Instant Communication (ROSIC). By leveraging features, such as real‐time messaging, group chats, end‐to‐end encryption and cross‐platform support inherent in the majority of instant messenger platforms, we have developed middleware that establishes a secure and efficient communication system over the Internet. By using instant messaging as the communication interface between users and robots, ROSIC caters to non‐technical users, making it easier for them to control robots. The architecture of ROSIC enables various scenarios for robot control, including one user controlling multiple robots, multiple users controlling one robot, multiple robots controlled by multiple users, and one user controlling one robot. Furthermore, ROSIC facilitates the interaction of multiple robots, enabling them to interoperate and function collaboratively as a swarm system by providing a unified communication platform that allows for seamless exchange of data and commands. Telegram was specifically chosen as the instant messaging platform by the authors due to its open‐source nature, robust encryption, compatibility across multiple platforms and interactive communication capabilities through channels and groups. Notably, the ROSIC is designed to communicate effectively with robot operating system (ROS)‐based robots to enhance our ability to control them remotely.

IMPACT OF IMPLEMENTING AI IN MEDICAL FIELD

Introduction: AI is a field of great scope and latest leading fame and coming to it in medical field it has been very useful and impactful. Objectives: we can say that the objectives of bringing in artificial intelligence in healthcare applications revolve around maximising AI's capabilities to enhance working capabilities, efficiency, and personalized care delivery to patient’s, ultimately leading to improved patient outcomes and healthcare operation. Review of Literature: A mixer of different papers regarding patient’s, medical field and related things Results: AI in the medical field is critical to the advancement of medical robotic surgery, IT operations, health tracking, and diagnostics using automation, predictive analytics, and data-driven insights to improve efficiency, accuracy, and patient care in a variety of healthcare and technology areas. Discussion: By the study and research we can observe that, artificial intelligence is transforming the healthcare industry in numerous areas giving it various wings. Implementation: Healthcare companies may boost operational effectiveness and implement AI strategies, improve patient outcomes, and provide more individualized and efficient care by utilizing AI technologies. Conclusion: The use of AI in medical field is useful as it acts as a good assistance and support, artificial intelligence (AI) allows healthcare businesses to give the best scope and power. Keywords: Artificial intelligence, predictive analytics, Patient , Clinical decision-making, Management, robotic surgery, healthcare, efficient care.

Research on LOAM algorithm in terrain reconnaissance of robot

This study investigates how the Lidar Odometer and Mapping (LOAM) algorithm, a kind of optimized SLAM algorithm, help robots build the terrain accurately. Some experiment results from different researches and papers were used to prove that the LOAM algorithm can successfully construct the map in 3D and make the researchers clear about complex terrain and make the robots move autonomously and safely without encountering the obstacles. In this paper, we refer other researchers papers to support our viewpoints and make it clear to convey the LOAM algorithm how to run and the principle of Odometry and Mapping process. We do not go into too much details about the content of the code, our main goal is to introduce and explain the advantages and disadvantages of the algorithm, and its efficiency and accuracy in creating maps. Our analysis showed that the LOAM algorithm can build topographic maps to achieve the goal of automating robot operation. We conclude that LOAM algorithm is an effective tool to help robots run autonomously and scan terrain.

Compact rover surveying and laser scanning for BIM development.

This paper presents a custom made small rover based surveying, mapping and building information modeling solution. Majority of the commercially available mobile surveying systems are larger in size which restricts their maneuverability in the targeted indoor vicinities. Furthermore their functional cost is unaffordable for low budget projects belonging to developing markets. Keeping in view these challenges, an economical indigenous rover based scanning and mapping system has developed using orthogonal integration of two low cost RPLidar A1 laser scanners. All the instrumentation of the rover has been interfaced with Robot Operating System (ROS) for online processing and recording of all sensorial data. The ROS based pose and map estimations of the rover have performed using Simultaneous Localization and Mapping (SLAM) technique. The perceived class 1 laser scans data belonging to distinct vicinities with variable reflective properties have been successfully tested and validated for required structural modeling. Systematically the recorded scans have been used in offline mode to generate the 3D point cloud map of the surveyed environment. Later the structural planes extraction from the point cloud data has been done using Random Sampling and Consensus (RANSAC) technique. Finally the 2D floor plan and 3D building model have been developed using point cloud processing in appropriate software. Multiple interiors of existing buildings and under construction indoor sites have been scanned, mapped and modelled as presented in this paper. In addition, the validation of the as-built models have been performed by comparing with the actual architecture design of the surveyed buildings. In comparison to available surveying solutions present in the local market, the developed system has been found faster, accurate and user friendly to produce more enhanced structural results with minute details.

Non-linearity Measure for POMDP-based Motion Planning

Motion planning under uncertainty is essential for reliable robot operation. Despite substantial advances over the past decade, the problem remains difficult for systems with complex dynamics. Most state-of-the-art methods perform search that relies on a large number of forward simulations. For systems with complex dynamics, this generally requires costly numerical integrations, which significantly slows down the planning process. Linearization-based methods have been proposed that can alleviate the above problem. However, it is not clear how linearization affects the quality of the generated motion strategy, and when such simplifications are admissible. To answer these questions, we propose a non-linearity measure, called Statistical-distance-based Non-linearity Measure (SNM), that can identify where linearization is beneficial and where it should be avoided. We show that when the problem is framed as the Partially Observable Markov Decision Process, the value difference between the optimal strategy for the original model and the linearized model can be upper-bounded by a function linear in SNM. Comparisons with an existing measure on various scenarios indicate that SNM is more suitable in estimating the effectiveness of linearization-based solvers. To test the applicability of SNM in motion planning, we propose a simple online planner that uses SNM as a heuristic to switch between a general and a linearization-based solver. Results on a car-like robot with second order dynamics and 4-DOFs and 7-DOFs torque-controlled manipulators indicate that SNM can appropriately decide if and when a linearization-based solver should be used.

Development of Portable Magnetic Adsorption Amphibious Robot

In this study, a portable magnetic adsorption amphibious robot which can operate on and below the waterline is developed for special curved environments, such as the pile legs of offshore platforms and the outer walls of ships. An open robot integrated control system based on a domestic chip is developed, and two operating modes of local control operation and remote wireless operation are realized. A permanent magnet adsorption scheme combining a magnetic adsorption track and a synchronous belt wheel is designed, static and dynamic analysis of the wall-climbing operation of the robot is carried out, and a kinematic model of the underwater robot is established. The experimental results show that the robot can effectively complete amphibious tasks and can realize the accurate control of attitude in water, proving it to be an effective tool for amphibious tasks, such as operating on the pile legs of offshore platforms and the outer walls of ships.

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.

A ball-jointed tendon-driven continuum robot with multi-directional operability for grasping objects

A continuum robot, inspired by biological features, flexibly bends its body and conforms to various shapes. The high compliance and low stiffness, however, causes low rigidity and ambiguity in control when it is applied to grasping and manipulating an object. In this study, we develop a ball-jointed tendon-driven continuum robot that can bend to arbitrary directions for manipulating and grasping an object. Discs are connected by ball joints, together with coil springs coupled with tendon threads, and form a backbone of the robotic body. The continuum robot is operated to bend to an arbitrary direction by pulling the tendons using three servomotors, and returns to the original straight shape by releasing the pulling force of the contracted coil springs. The robot is constructed by using 3D-printed parts. In the experiment, the ability of the multi-directional operation was tested by bending the continuum body to specific directions, and then performing arc-following motion. Furthermore, the manipulator's grasping performance was demonstrated by preparing five objects that have various shapes, sizes, and softness. The robot successfully grasped all the objects by wrapping around them and adapting its shape to the object's shapes. The experiments verified the satisfactory operability of the continuum robot.

Research on Radiation Damage and Reinforcement of Control and Sensing Systems in Nuclear Robots

This study investigates the radiation damage and radiation reinforcement of the control and sensing systems of nuclear robots. Radiation experiments were conducted on key electronic devices to study their radiation resistance, and a shielding structure for radiation reinforcement was designed to meet the radiation resistance performance requirements of the system. The results show that at doses exceeding 1300 Gy, Hall sensors, pressure transducers, and temperature transducers exhibit radiation damage. At doses exceeding 170 Gy, transformers and controllers also show radiation damage. Lithium batteries remain largely unaffected, but packs experience voltage decline. When using Pb and W as shielding materials for Super MC simulation, it was found that at a thickness of 15 mm, the shielding efficiency of the controller and transformer under Pb shielding increased by approximately 84.99% and 52.00%, respectively, compared to 92.23% and 74.47% under W, which had the best shielding effect benefits. By adopting radiation-resistant shielding reinforcement, we can effectively improve the radiation resistance of the controller and transformer. This is crucial for ensuring the reliable operation of nuclear robots in high-radiation environments and providing important data and theoretical support for the development of related technologies.

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.

Performance Evaluation of a Miniature and Disposable Endovascular Robotic Device.

The LIBERTY® Robotic System is a miniature, single-use device designed to facilitate remote-controlled navigation to intravascular targets. We aim to evaluate the robot's performance to manipulate a range of microguidewires and microcatheters during percutaneous endovascular procedures. Six interventional radiologists performed selective robotic-assisted catheterization of eight pre-determined vascular targets in a pig model. The navigation time from the guiding catheter tip to the target vessel was recorded. Each physician with a clinical experience of 20years completed a questionnaire to evaluate the ease of use, accuracy, and safety of the robotic operation. Most of the physicians reached the vascular targets in less than one minute. There was no angiographic evidence of vascular injury such as artery laceration or contusion. All physicians reported consensus about the high performance of the robot. The miniature disposable robot is effective at reaching a range of vessels in a porcine model. Physicians found the device intuitive and easy to operate remotely.

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.

Fault-tolerant heading control for a bionic gliding robotic dolphin with flipper faults

This paper presents a fault-tolerant heading-control method for a bionic gliding robotic dolphin operating in the presence of flipper faults. To achieve both high manoeuvrability and long endurance, a bionic gliding robotic dolphin that integrates the propulsion modes of dolphins and underwater gliders is introduced. The operational conditions of the flippers are considered meticulously, and common fault types that can arise during the gliding process, such as stuck, failure, and efficiency-loss faults, are analysed. These faults can affect the heading capabilities of the gliding robotic dolphin. To ensure normal navigation in the presence of flipper faults, a fault-tolerant heading control method is devised for the gliding robotic dolphin. This method applies backstepping sliding control to establish control laws based on a dynamic model. Subsequently, a flipper angle solver is designed to convert the controller outputs into deflection angles for the flippers. Simulation results obtained under three typical flipper faults demonstrate the effectiveness of the proposed fault-tolerant control method. This fault-tolerant control method ensures the safety of gliding robotic dolphins under complex deep-sea conditions.

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.