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Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot's search behavior among four strategies: crosswind maneuver, Obstacle-Avoid Navigation, Vision-Based Navigation, and Olfaction-Based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-Based Navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms vision-only and olfaction-only methods, reducing average search time by 54% and 30%, respectively. [ABSTRACT FROM AUTHOR]

To tackle the challenges of weak sensing capacity for multi-scale objects, high missed detection rates for occluded targets, and difficulties for model deployment in detection tasks of intelligent roadside perception systems, the PDT-YOLO algorithm based on YOLOv7-tiny is proposed. Firstly, we introduce the intra-scale feature interaction module (AIFI) and reconstruct the feature pyramid structure to enhance the detection accuracy of multi-scale targets. Secondly, a lightweight convolution module (GSConv) is introduced to construct a multi-scale efficient layer aggregation network module (ETG), enhancing the network feature extraction ability while maintaining weight. Thirdly, multi-attention mechanisms are integrated to optimize the feature expression ability of occluded targets in complex scenarios, Finally, Wise-IoU with a dynamic non-monotonic focusing mechanism improves the accuracy and generalization ability of model sensing. Compared with YOLOv7-tiny, PDT-YOLO on the DAIR-V2X-C dataset improves mAP50 and mAP50:95 by 4.6% and 12.8%, with a parameter count of 6.1 million; on the IVODC dataset by 15.7% and 11.1%. We deployed the PDT-YOLO in an actual traffic environment based on a robot operating system (ROS), with a detection frame rate of 90 FPS, which can meet the needs of roadside object detection and edge deployment in complex traffic scenes. [ABSTRACT FROM AUTHOR]

This paper proposes the cooperative use of zero velocity update (ZU) in a decentralized extended Kalman filter (DEKF)-based localization algorithm for multi-robot systems. The filter utilizes inertial measurement unit (IMU), ultra-wideband (UWB), and odometer-based velocity measurements to improve the localization performance of the system in a GNSS-denied environment. In this work, we evaluate the benefits of using ZU in a DEKF-based localization algorithm. The algorithm was tested with real hardware in a video motion capture facility and a robot operating system (ROS)-based simulation environment for unmanned ground vehicles (UGVs). Both simulation and real-world experiments were performed to determine the effectiveness of using ZU in one robot to reinstate the localization of the others in a multi-robot system. Experimental results from GNSS-denied simulation and real-world environments revealed that using ZU in the DEKF together with simple heuristics significantly improved the three-dimensional localization accuracy. [ABSTRACT FROM AUTHOR]

Real-time flight controllers are becoming dependent on general-purpose operating systems, as the modularity and complexity of guidance, navigation, and control systems and algorithms increases. The non-deterministic nature of operating systems creates a critical weakness in the development of motion control systems for robotic platforms due to the random delays introduced by operating systems and communication networks. The high-speed operation and sensitive dynamics of UAVs demand fast and near-deterministic communication between the sensors, companion computer, and flight control unit (FCU) in order to achieve the required performance. In this paper, we present a method to assess communications latency between a companion computer and an RTOS open-source flight controller, which is based on an XRCE-DDS bridge between clients hosted in the low-resource environment and the DDS network used by ROS2. A comparison based on the measured statistics of latency illustrates the advantages of XRCE-DDS compared to the standard communication method based on MAVROS-MAVLink. More importantly, an algorithm to estimate latency offset and clock skew based on an exponential moving average filter is presented, providing a tool for latency estimation and correction that can be used by developers to improve synchronization of processes that rely on timely communication between the FCU and companion computer, such as synchronization of lower-level sensor data at the higher-level layer. This addresses the challenges introduced in GNC applications by the non-deterministic nature of general-purpose operating systems and the inherent limitations of standard flight controller hardware. [ABSTRACT FROM AUTHOR]

Predictive maintenance is a proactive approach to maintenance in which equipment and machinery are monitored and analyzed to predict when maintenance is needed. Instead of relying on fixed schedules or reacting to breakdowns, predictive maintenance uses data and analytics to determine the appropriate time to perform maintenance activities. In industrial applications, machine boxes can be used to collect and transmit the feature information of manufacturing machines. The collected data are essential to identify the status of working machines. This paper investigates the design and implementation of a machine box based on the ROS framework. Several types of communication interfaces are included that can be adopted to different sensor modules for data sensing. The collected data are used for the application on predictive maintenance. The key concepts of predictive maintenance include data collection, a feature analysis, and predictive models. A correlation analysis is crucial in a feature analysis, where the dominant features can be determined. In this work, linear regression, a neural network, and a decision tree are adopted for model learning. Experimental results illustrate the feasibility of the proposed smart machine box. Also, the remaining useful life can be effectively predicted according to the trained models. [ABSTRACT FROM AUTHOR]

The emergence of robotic technologies has made a significant contribution in industry. Robot Operating System (ROS) is becoming a standard framework for industrial systems uses as a middleware system with many versions. However, the initial design of ROS does not include cyber-security concepts. The intense interest in robot systems, the security concerns and vulnerabilities of these systems have started to attract the attention of attackers. One of these attacks is DoS attack that targeting system availability by slowing down or crashing a service rather than obtaining the information or system. In this study, the impact of DoS attack has been analyzed in various scenarios for both in application and transport layer of the ROS middleware. In the experiments four different volume of DoS attacks are performed in five different experiment scenarios on ROS. To understand the impact of DoS attack, network traffics are monitored using Tshark. The resulting effects measured with some Quality of Service parameters that are delay and packet loss. [ABSTRACT FROM AUTHOR]

Recently, underwater robotics has rapidly developed, and is often used in open-water exploration and underwater operations in known environments. However, there are still several problems in exploring the interiors of complex underwater environments, which are essential for scientific exploration and industrial applications, such as caves and shipwrecks. This study aims to complete the exploration of the environment of structures under water bodies. A real-time manipulative small underwater robot was designed and developed. The robot's autonomous depth control and linear motion-assisted control are also realized by real-time sensor data processing, which provides stability and operability to move in small areas and complex environments. The sonar system is used to construct a submap for small-area scanning. Finally, by combining the odometer algorithm and contour extraction, the submaps are stitched together to construct a complete map of the internal underwater environment. [ABSTRACT FROM AUTHOR]

Indoor navigation robots, which have been developed using a robot operating system, typically use a direct current motor as a motion actuator. Their control algorithm is generally complex and requires the cooperation of sensors such as wheel encoders to correct errors. For this study, an autonomous navigation robot platform named Owlbot was designed, which is equipped with a stepping motor as a mobile actuator. In addition, a stepping motor control algorithm was developed using polynomial equations, which can effectively convert speed instructions to generate control signals for accurately operating the motor. Using 2D LiDAR and an inertial measurement unit as the primary sensors, simultaneous localization, mapping, and autonomous navigation are realised based on the particle filtering mapping algorithm. The experimental results show that Owlbot can effectively map the unknown environment and realise autonomous navigation through the proposed control algorithm, with a maximum movement error being smaller than 0.015 m. [ABSTRACT FROM AUTHOR]

This study presents a relative localization estimation method for a group of low-cost underwater drones (l-UD), which only uses visual feedback provided by an on-board camera and IMU data. It aims to design a distributed controller for a group of robots to reach a specific shape. This controller is based on a leader–follower architecture. The main contribution is to determine the relative position between the l-UD without using digital communication and sonar positioning methods. In addition, the proposed implementation of the EKF to fuse the vision data and the IMU data improves the prediction capability in cases where the robot is out of view of the camera. This approach allows the study and testing of distributed control algorithms for low-cost underwater drones. Finally, three robot operating system (ROS) platform-based BlueROVs are used in an experiment in a near-realistic environment. The experimental validation of the approach has been obtained by investigating different scenarios. [ABSTRACT FROM AUTHOR]

This paper is concerned with the first work on the integration of digital twin (DT), 5G, cloud platform, and virtual reality (VR) technologies for unmanned aerial vehicles (UAVs) autonomy development. DT focuses on connecting the virtual and physical world as an emerging strategic technology. Initially, it was implemented through mirror models of physical objects to realize the monitoring of their whole life cycle in the manufacturing area. In recent years, DT technologies have been applied in different fields, and some typical DT solutions have been proposed to solve complex system problems. In this paper, we study the problem of how to combine the DT and other emerging technologies for UAV autonomy development and supervision, aiming to propose a basic DT framework to integrate DT and UAVs as reference rules for building DT systems, which includes four parts, that is, Virtual Space, Real Space, Service Center, and Data and Model Processing Center. Based on the proposed basic DT framework, a cloud-based DT system is then further constructed in which cloud platform, 5G, and VR are integrated seamlessly. The running and implementation processes of each subsystem are introduced in detail. Multiple experiments are conducted to verify the usefulness of proposed DT system, that is, real-time system monitoring and cloud processing, VR connection, human–robot interaction through VR technology, and so on. The experimental results show that the proposed DTUAV system can be used in the interaction of virtual and physical systems, remote supervision, intelligence integration of swarm of unmanned vehicles, and so on. The development in our work introduces the DT into unmanned system applications and can promote relevant research in this direction. All implementation codes of the system will be shared in https://github.com/DTUAV. [ABSTRACT FROM AUTHOR]

As the number of vehicles in traffic is increasing day by day, the accident rates and driving effort are significantly raising. For this reason, ensuring safety and driving comfort is becoming more and more important. Driver assistance systems are the most common systems that are adopted for this purpose. With the development of technology, lane tracking support and adaptive cruise control systems are now being sold as standard equipment. More advanced research is being done for fully autonomous driving. One of the most critical parts of autonomous driving is speed profile planning. In this paper, a curvature-based predictive speed planner is designed using a fuzzy logic strategy. In addition to the predictive curvature value, lateral error to the planned path is considered for the final decision. With the help of the proposed predictive nature, which is not included in classical curvature-based planners, the vehicle is able to reduce the speed before cornering. Similarly, it starts increasing the speed when it is still in a curve but very near to the straight part of the road. In order to illustrate the efficiency of the proposed method and compare it with other approaches, the simulations are performed using realistic vehicle dynamics models of CarMaker software. After the comparative analysis, the designed planner is utilized on a real 1/10 scaled autonomous vehicle platform to show its real-world performance. The results show that the proposed speed planner is an effective and promising algorithm for both the comfort and path tracking performance. [ABSTRACT FROM AUTHOR]

Fuzzy logic controllers can handle complex systems by incorporating expert's knowledge in the absence of formal mathematical models. Further, fuzzy logic controllers can effectively capture and accommodate uncertainties that are inherent in real-world controlled systems. On the other hand, Robot Operating System (ROS) has been widely used for many robotic applications due to its modular structure and efficient message-passing mechanisms for the integration of system's components. For this reason, Robot Operating System is an ideal tool for developing software stacks for robotic applications. This paper develops a generic and configurable Robot Operating System package for the implementation of fuzzy logic controllers, particularly type-1 and interval type-2, which are based on either Mamdani or Takagi-Sugeno-Kang fuzzy inference mechanisms. This is achieved by employing a systematic object-oriented approach using the Unified Model Language (UML) to implement the fuzzy inference system as a single class that is composed of fuzzifier, inference, and defuzzifier classes. The deployment of the developed Robot Operating System package is demonstrated by implementing an interval type-2 fuzzy logic control of an Unmanned Aerial Vehicle (UAV). [ABSTRACT FROM AUTHOR]

The Robot Operating System 2 (ROS 2) is rapidly impacting the intelligent machines sector — on space missions, large agriculture equipment, multi-robot fleets, and more. Its success derives from its focused design and improved capabilities targeting product-grade and modern robotic systems. Following ROS 2's example, the mobile robotics ecosystem has been fully redesigned based on the transformed needs of modern robots and is experiencing active development not seen since its inception. This paper comes from the desks of the key ROS Navigation maintainers to review and analyze the state of the art of robotics navigation in ROS 2. This includes new systems without parallel in ROS 1 or other similar mobile robotics frameworks. We discuss current research products and historically robust methods that provide differing behaviors and support for most every robot type. This survey consists of overviews, comparisons, and expert insights organized by the fundamental problems in the field. Some of these implementations have yet to be described in literature and many have not been benchmarked relative to others. We end by providing a glimpse into the future of the ROS 2 mobile robotics ecosystem. • ROS 2's Nav2 has a number of new algorithms and capabilities suitable for a large number of applications. • This survey analyzes ROS 2 algorithm's capabilities and benchmarks performance. • Explains systems provided to build robust autonomous navigation systems in dynamic environments. [ABSTRACT FROM AUTHOR]

As autonomous cars and complex features of them grow in popularity, ensuring that analyses and capabilities are reproducible and repeatable has taken on importance in education plans too. This paper describes a reproducible research plan on mini autonomous race car programming. This educational plan is designed and implemented as part of a summer internship program at Kocaeli University and it consists of theoretical courses and laboratory assignments. A literate programming approach with the Python language is used for programming the race car. To assess the educational program's impact on the learning process and to evaluate the acceptance and satisfaction level of students, they answered an electronic questionnaire after finishing the program. According to students' feedback, the reproducible educational program is useful for learning and consolidating new concepts of mini autonomous car programming. [ABSTRACT FROM AUTHOR]

With the development of deep learning, autonomous robot systems grow rapidly and require better performance. Robot Operating System 2 (ROS2) has been widely adopted as the main communication framework in autonomous robot systems. However, the performance of ROS2 has become the bottleneck of these real-time systems. From our observations, we find that it can take a large amount of time to serialize complex message in communication, especially for some high-level programming languages, including Python, Java and so on. To address this challenge, we propose a novel technique, called adaptive two-layer serialization algorithm, which can achieve good performance in communication for different kinds of messages. Experimental results show that our algorithm can achieve significant performance improvement over traditional methods in ROS2, up to 93% improvement in our framework. We have successfully applied our proposed techniques in a real autonomous robot system. [ABSTRACT FROM AUTHOR]

To solve the problem of small redundancy of mobile robot’s operational area caused by indoor space limitation, indoor operation characteristics of mobile robots was analysed in this paper.The odometer model of mobile robots is established, and a path planning method for mobile robots based on differential evolution algorithm is proposed. The simulation results show that the differential evolution algorithm is superior to the A* algorithm currently used for industrial mobile robots. In order to further verify the practicability of the algorithm, the algorithm is transplanted in ROS system. The running results show that the improved path planning method has obvious advantages over the embedded A* algorithm in the number of iterations, the optimal value and the visual path of ROS system. [ABSTRACT FROM AUTHOR]

Presently the interaction of robots with human plays an important role in various social applications. Reliable tracking is an important aspect for the social robots where robots need to follow the moving person. This paper proposes the implementation of automated tracking and real time following algorithm for robotic automation. Occlusion and identity retention are the major challenges in the tracking process. Hence, a feature set based identity retention algorithm is used and integrated with robot operating system. The tracking algorithm is implemented using robot operating system in Linux and using OpenCV. The tracking algorithm achieved 85% accuracy and 72.30% precision. Further analysis of tracking algorithm corresponds to the integration of ROS and OpenCV is presented. The analysis of tracking algorithm concludes that ROS linking required 0.64% more time in comparison with simple OpenCV code based tracking algorithm. [ABSTRACT FROM AUTHOR]

To reduce the development complexity of underwater remote-operated vehicles and increase their adaptability and applications,a monitor system for underwater remote-operated vehicles based on ROS is developed.First,the hardware structure of the monitor system is introduced briefly.The software framework of the monitor system based on ROS is proposed.The compositions and functions of the perceptual execution,navigation control,and monitoring operation layers are introduced.The design of three functional modules,namely the motion control,human-robot interaction,and attitude control nodes,are described.Experiments show that the monitor system of the underwater remote-operated vehicle based on ROS can coordinate the work of each functional module.It is characterized by a short development cycle and good expansibility. [ABSTRACT FROM AUTHOR]

Abstract The Robot Operating System (ROS) is a popular and widely used software framework for building robotics systems. With the growth of its popularity, it has started to be used in multi-robot systems as well. However, the TCP connections that the platform relies on for connecting the so-called ROS nodes presents several issues regarding limited-bandwidth, delays, and jitter, when used in wireless multi-hop networks. In this paper, we present a thorough analysis of the problem and propose a new ROS node called Pound to improve the wireless communication performance by reducing delay and jitter in data exchanges, especially in multi-hop networks. Pound allows the use of multiple ROS masters (roscores), features data compression, and importantly, introduces a priority scheme that allows favoring more important flows over less important ones. We compare Pound to the state-of-the-art solutions through extensive experiments and show that it performs equally well, or better in all the test cases, including a control-over-network example. [ABSTRACT FROM AUTHOR]

Autonomous robots are used to perform necessary operations in areas that are dangerous or difficult to reach or accessible by people. In such a case, the robots must first know its surroundings and position in order to be able to do the necessary work. If it is a known environment, its map is loaded to autonomous robot. On the other hand, robot should simultaneously determine both the map of the environment and its own location. Simultaneous Localization and Mapping (SLAM) was performed by using Robot Operating System (ROS) in Linux. For this purpose, Turtlebot mobile robot, manufactured by Kobuki company, was used. In the first application, Turtlebot computer was manually controlled via USB interface. The Lidar sensor, which measures the laser distance, was used to extract the environment map and determine the position of the robot. Particle filter based on Kalman filter was used for SLAM. For this method, the Gmapping algorithm was loaded into the ROS. In the second application, Turtlebot was controlled autonomously using ROS and the experimental environment was also mapped by determining its position at the same time. All applications were successfully carried out. The obtained results were illustrated in figures. [ABSTRACT FROM AUTHOR]

Mobile radiation sensing techniques for surge deployment and wide area search are important for nuclear security applications. Radiation sensors were developed for the use with robotic platforms of unmanned aerial systems (UAS). Ambient temperature neutron and gamma CLYC sensor and high resolution CZT gamma sensor were integrated into the UAS as plug-and-play interchangeable components allowing easy attachment to a robot in field conditions. The time and position data were added via Robot Operating System to measured intensities of photon peaks and neutron rates enabling the input for the UAS flight control. The contour mapping of a radiation area by multiple UAS was simulated. [ABSTRACT FROM AUTHOR]

Tailoring cloud support for each autonomous-driving application would require maintaining multiple infrastructures, potentially resulting in low resource utilization, low performance, and high management overhead. To address this problem, the authors present a unified cloud infrastructure with Spark for distributed computing, Alluxio for distributed storage, and OpenCL to exploit heterogeneous computing resources for enhanced performance and energy efficiency. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SIAM) has always been the key technology to achieve mobile robot autonomous navigation and obstacle avoidance. Traditional ID and 2D sensors such as ultrasonic sensor, sonar and laser range finder for area mapping is not precise. Because they can not detect information in vertical direction,it is likely to increase the probability of robot collision and inaccurate mapping. To compensate for the lack of 2D laser data in previous studies, this paper presented a method which collected 3D information from Kineet merged into 2D laser data. With the help of robot operating system (ROS) simulation analysis and practical tests, the verified results show that Kineet can make up the lack of ID and 2D sensor to collect information and it can also promote the integrity and reliability of the mapping. Kineet is suitable for mobile robot SLAM indoor realization. [ABSTRACT FROM AUTHOR]

Robots are an important part of urban search and rescue tasks. World wide attention has been given to developing capable physical platforms that would be beneficial for rescue teams. It is evident that use of multi-robots increases the effectiveness of these systems. The Robot Operating System (ROS) is becoming a standard platform for the robotics research community for both physical robots and simulation environments. Gazebo, with connectivity to the ROS, is a three-dimensional simulation environment that is also becoming a standard. Several simultaneous localization and mapping algorithms are implemented in the ROS; however, there is no multi-robot mapping implementation. In this work, two multi-robot mapping algorithm implementations are presented, namely multi-robot gMapping and multi-robot Hector Mapping. The multi-robot implementations are tested in the Gazebo simulation environment. Also, in order to achieve a more realistic simulation, every incremental robot movement is modeled with rotational and translational noise. [ABSTRACT FROM AUTHOR]

This paper presents a computationally inexpensive generic method to utilize the maximum velocity and acceleration of an omnidirectional mobile robot. The proposed method is based on the inverse kinematic and inverse dynamic models by defining novel velocity and acceleration reserve multipliers respectively. The defined multipliers enable a computationally inexpensive solution and give representative index numbers, showing the amount of utilized resources during online computation. The model was applied to control a kiwi drive mobile robot and validated by experimental measurements. An open-source Robot Operating System (ROS) catkin C + + package was published to enable the feasible implementation of the results. [ABSTRACT FROM AUTHOR]

This research examines how humans work with tele-operated unmanned mobile robots to perform interaction to do the inspection in industrial plant area to produce a 3D map for further critical evaluation. This experiment focuses on two parts, the way human-robot doing the remote interactions using robust method and the way robot perceives its surrounding environment as a 3D perspective map. Robot operating system (ROS) as a tool was utilized in the development and implementation of this research which comes up with robust data communication method in the form of messages and topics. RGBD simultaneous localization and mapping (SLAM) performs the visual odometry and mapping function simultaneously to construct 3D map using Kinect sensor. The results showed that the mobile robotbased tele-operated system utilizing RGBD SLAM for 3D inspection task under ROS environment are successful in extending human perspective in term of remote surveillance in the large area of the industrial plant. [ABSTRACT FROM AUTHOR]

In recent research activities, autonomous vehicles and self-driving technology have gained lot of attention among scientists. The idea of autonomous vehicles can be anticipated in the 1920s when the design of the first radio-controlled vehicles was in progress. Autonomous vehicles are going to be the trend of the future in this modern era of automation and technology. In this paper various autonomous driving aspects, highlighting the software stack and hardware components are discussed. The software architecture covers mainly robot operating system (ROS), machine learning (ML), deep learning (DL), and OpenCV frameworks, along with the calibration of sensors and cameras. The paper also discussed about simultaneous localization and mapping (SLAM) based-path tracking, computer vision-based controller, and intelligent object avoidance. Further, point cloud, ground, radius, and raycast filters was implemented to distinguish between the real-time objects, ground, and its own parts or obstacle shadows. The paper highlights the overall hardware modules responsible for controlling the car. • Future trend of Autonomous vehicles in modern era of automation and technology. • Simultaneous localization and mapping based-path tracking. • Computer vision-based controller, and intelligent object avoidance. • Discussion on Hardware modules responsible for controlling the car. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel real-time three-dimensional simulation system, ROSUnitySim, for local planning by miniature unmanned aerial vehicles (UAVs) in cluttered environments. Unlike commonly used simulation systems in robotic research—e.g., USARSim, Gazebo, etc.—in this work our development is based on a robot operation system (ROS) and with a different game engine, Unity3D. Compared with Unreal Engine, which is used in USARSim, Unity3D is much easier for entry level developers and has more users in the industry. On the other hand, as we know, ROS can provide a clear software structure and simultaneous operation between hardware devices for actual UAVs. By developing a data transmitting interface, a communication module and detailed environment and sensor modeling techniques, we have successfully glued ROS and Unity3D together for real-time UAV simulations. Another key point of our work is that we propose an efficient multi-UAV simulation structure and successfully simulate multiple UAVs, which is a challenging task, running 40Hz LIDAR (Light detection and ranging) sensing and communications in complex environments. The simulator structure is almost the same as real flight tests. Hence, by using the developed simulation system, we can easily verify develop flight control and navigation algorithms and save substantial effort in flight tests. [ABSTRACT FROM AUTHOR]

This paper proposes deep learning-based footstep planning using Generative Adversarial Networks (GANs) for the indoor navigation of humanoid robots. The GAN-based architecture presents an efficient and accurate path to plan the footsteps of a humanoid robot on Robot Operating System (ROS) based architecture. During navigation, humanoid robots must understand their surroundings and be able to generate footsteps accurately. Although some algorithms that are based on sampling, such as Rapidly Exploring Random Tree (RRT*) and A*, are widely used for path planning, they fail to perform in narrow paths, especially for the footstep generation of humanoid robots. The widely growing deep learning approaches such as GANs are now producing extremely surprising results in solving real-life problems. The experiments conclude that GAN based approach performs better than conventional Dijkstra's or A* algorithms. The accuracy of the generated footsteps from the GAN-based path planner comes out to be approximately 93%. • This paper proposes deep learning-based footstep planning using GANs for the indoor navigation of humanoid robots. • The GAN-based architecture presents an efficient path to plan the footsteps of a humanoid robot on ROS based architecture. • During navigation, humanoid robots must understand their surroundings and be able to generate footsteps accurately. • The experiments conclude that GAN based approach performs better than conventional Dijkstra's A* algorithms. • The accuracy of the generated footsteps from the GAN-based path planner comes out to be approximately 93%. [ABSTRACT FROM AUTHOR]

Daily life assistance is one of the most important applications for service robots. For comfortable assistance, service robots must recognize the surrounding conditions correctly, including human motion, the position of objects, and obstacles. However, since the everyday environment is complex and unpredictable, it is almost impossible to sense all of the necessary information using only a robot and sensors attached to it. In order to realize a service robot for daily life assistance, we have been developing an informationally structured environment using distributed sensors embedded in the environment. The present paper introduces a service robot system with an informationally structured environment referred to the ROS–TMS. This system enables the integration of various data from distributed sensors, as well as storage of these data in an on-line database and the planning of the service motion of a robot using real-time information about the surroundings. In addition, we discuss experiments such as detection and fetch-and-give tasks using the developed real environment and robot. [ABSTRACT FROM AUTHOR]

In order to implement the mobile robot simultaneous localization and mapping in unknown environments, this paper presented an improved particle filter algorithm. To decrease the large number of particles that needed in normal particle filter, it integrated the observed information of laser range finder into importance sampling. When severe particle impoverishment occured after resampling, the algorithm took Markov Chain Monte Carlo moving process to reduce particle impoverishment effect. It tested the method on a Pioneer3-DX robot with robot operating system. Actual experiment results show that the method can generate high-precision grid map in real time. [ABSTRACT FROM AUTHOR]

This paper presents a performance analysis of two open-source, laser scanner-based Simultaneous Localization and Mapping (SLAM) techniques (i.e., Gmapping and Hector SLAM) using a Microsoft Kinect to replace the laser sensor. Furthermore, the paper proposes a new system integration approach whereby a Linux virtual machine is used to run the open source SLAM algorithms. The experiments were conducted in two different environments; a small room with no features and a typical office corridor with desks and chairs. Using the data logged from real-time experiments, each SLAM technique was simulated and tested with different parameter settings. The results show that the system is able to achieve real time SLAM operation. The system implementation offers a simple and reliable way to compare the performance of Windows-based SLAM algorithm with the algorithms typically implemented in a Robot Operating System (ROS). The results also indicate that certain modifications to the default laser scanner-based parameters are able to improve the map accuracy. However, the limited field of view and range of Kinect's depth sensor often causes the map to be inaccurate, especially in featureless areas, therefore the Kinect sensor is not a direct replacement for a laser scanner, but rather offers a feasible alternative for 2D SLAM tasks. [ABSTRACT FROM AUTHOR]

In order to solve the problem in which most currently existing research focuses on the binary tactile attributes of objects and ignores identifying the strength level of tactile attributes, this paper establishes a tactile data set of the strength level of objects' elasticity and hardness attributes to make up for the lack of relevant data, and proposes a multi-scale convolutional neural network to identify the strength level of object attributes. The network recognizes the different attributes and identifies differences in the strength level of the same object attributes by fusing the original features, i.e., the single-channel features and multi-channel features of the data. A variety of evaluation methods were used for comparison with multiple models in terms of strength levels of elasticity and hardness. The results show that our network has a more significant effect in accuracy. In the prediction results of the positive examples in the predicted value, the true value has a higher proportion of positive examples, that is, the precision is better. The prediction effect for the positive examples in the true value is better, that is, the recall is better. Finally, the recognition rate for all classes is higher in terms of f1_score. For the overall sample, the prediction of the multi-scale convolutional neural network has a higher recognition rate and the network's ability to recognize each strength level is more stable. [ABSTRACT FROM AUTHOR]

The Robot Operating System promises to make designing robotic software easier and less expensive. [ABSTRACT FROM AUTHOR]

A successful application of low-cost 3D cameras in combination with artificial intelligence (AI)-based 3D object detection algorithms to outdoor mobile mapping would offer great potential for numerous mapping, asset inventory, and change detection tasks in the context of smart cities. This paper presents a mobile mapping system mounted on an electric tricycle and a procedure for creating on-street parking statistics, which allow government agencies and policy makers to verify and adjust parking policies in different city districts. Our method combines georeferenced red-green-blue-depth (RGB-D) imagery from two low-cost 3D cameras with state-of-the-art 3D object detection algorithms for extracting and mapping parked vehicles. Our investigations demonstrate the suitability of the latest generation of low-cost 3D cameras for real-world outdoor applications with respect to supported ranges, depth measurement accuracy, and robustness under varying lighting conditions. In an evaluation of suitable algorithms for detecting vehicles in the noisy and often incomplete 3D point clouds from RGB-D cameras, the 3D object detection network PointRCNN, which extends region-based convolutional neural networks (R-CNNs) to 3D point clouds, clearly outperformed all other candidates. The results of a mapping mission with 313 parking spaces show that our method is capable of reliably detecting parked cars with a precision of 100% and a recall of 97%. It can be applied to unslotted and slotted parking and different parking types including parallel, perpendicular, and angle parking. [ABSTRACT FROM AUTHOR]

Utility pipes on the façades of high-rise buildings are susceptible to the harsh environment. Damaged pipes are conventionally replaced manually, which is inconvenient to customers and leads to material wastage and worker safety concerns. This work proposes a different approach by developing a robotic solution in a preventive maintenance manner. The entire robotic system consists of a service robot working at heights and a remote ground station. This paper presents the work process and the major robotic modules: a manipulator arm, a two-finger gripper with force feedback, a sparkless polishing tool with contact feedback, a painting tool with a dip replenish mechanism, and on-board visual monitoring. Field deployment test results validate the effectiveness of the concept. The proposed system saves time from eliminating scaffold preparation setup and reduces material wastage compared with conventional methods. The developed robotic system and process have a potential application in relevant high-rise utility operations. • This paper highlights the design of a comprehensive teleoperated robotic system for the maintenance of high-rise utilities. • The robot is equipped with a laser-guided alignment and a force-feedback gripper for pose stabilization in mid-air. • The end effector can remove rust and apply anti-rust coating on the pipe surface in situ using contact feedback control. • The maintenance workflow is demonstrated through field tests on the live gas service riser in actual housing estates. • This paper contributes to the work safety improvement, especially addressing concerns on working-at-height. [ABSTRACT FROM AUTHOR]

Developing robot control software systems is difficult because of a wide variety of requirements, including hardware systems and sensors, even though robots are demanding nowadays. Middleware systems, such as Robot Operating System (ROS), are being developed and widely used to tackle this difficulty. Streaming data Sharing Manager (SSM) is one of such middleware systems that allow developers to write and read sensor data with timestamps using a Personal Computer (PC). The timestamp feature is essential for the robot control system because it usually uses multiple sensors with their own measurement cycles, meaning that measured sensor values with different timestamps become useless for the robot control. Using SSM allows developers to use measured sensor values with the same timestamps; however, SSM assumes that only one PC is used. Thereby, if one process consumes CPU resources intensively, other processes cannot finish their assumed deadlines, leading to the unexpected behavior of a robot. This paper proposes an SSM middleware, named Distributed Streaming data Sharing Manager (DSSM), that enables distributing processes on SSM to different PCs. We have developed a prototype of DSSM and confirmed its behavior so far. In addition, we apply DSSM to an existing real SSM based robot control system that autonomously controls an unmanned vehicle robot. We then reveal its advantages and disadvantages via several experiments by measuring resource usages. [ABSTRACT FROM AUTHOR]

Energy consumption estimation and management of the maritime Unmanned Surface Vehicles (USV) is an important issue to deal with energy minimization techniques such as path planning, tasks scheduling, etc. In this paper, we introduce the energy consumption parameter in USV simulation through three contributions: 1) An analytic USV's energy consumption model is developed based on the three-degrees-of-freedom dynamic model of surface vessels. 2) A reverse engineering approach is proposed to identify the previously used dynamic model parameters based on a set of scenarios executed within a recent simulation environment. 3) The simulator engine is enriched with the consumption modelling tools such that the power absorbed by the USV is instantaneously calculated and returned; thus, the required energy of any predefined scenario is available as a new simulation result. • The unmanned maritime drones' autonomy is limited by their battery capacities. • Several available approaches are used to reduce their consumption. • The existing solutions are not feasible in complicated environments or huge scenarios. • The problem is solved by developing a more realistic power model of the marine drones. • A robust simulator is used to manage and estimate the consumption of marine drones. [ABSTRACT FROM AUTHOR]

In order to save people's shopping time and reduce labor cost of supermarket operations, this paper proposes to design a supermarket service robot based on deep convolutional neural networks (DCNNs). Firstly, according to the shopping environment and needs of supermarket, the hardware and software structure of supermarket service robot is designed. The robot uses a robot operating system (ROS) middleware on Raspberry PI as a control kernel to implement wireless communication with customers and staff. So as to move flexibly, the omnidirectional wheels symmetrically installed under the robot chassis are adopted for tracking. The robot uses an infrared detection module to detect whether there are commodities in the warehouse or shelves or not, thereby grasping and placing commodities accurately. Secondly, the recently-developed single shot multibox detector (SSD), as a typical DCNN model, is employed to detect and identify objects. Finally, in order to verify robot performance, a supermarket environment is designed to simulate real-world scenario for experiments. Experimental results show that the designed supermarket service robot can automatically complete the procurement and replenishment of commodities well and present promising performance on commodity detection and recognition tasks. [ABSTRACT FROM AUTHOR]

Low cost and accurate 3D surface profiling can help in numerous industry applications including inspection tasks, cleaning, minimizing bumps in navigation of non-uniform terrain, aid navigation, and road/pavement condition analysis. However, most of the available systems are costly or inaccessible for widespread use. This research presents investigation into the capability of cheap and accessible sensors to capture the floor surface profile information. A differential drive robotic platform has been developed to perform testing and conduct the research. 2D localization methods are extrapolated into 3D for the floor capturing process. Two different types of sensors, a 2D laser scanner and an RGB-D camera, are used for comparison of the floor profile capture ability. The robotic system is able to successfully capture the floor surface profile of a number of different type floors such as carpet, asphalt, and a coated floor. A key finding is that the surface itself is a significant factor on the measured profile, i.e., dirt or other materials can cause false height measurements. Overall, the methodology has proved a successful real time solution for creating a point cloud of the floor surface. [ABSTRACT FROM AUTHOR]

This research aims to develop a farm management emulation tool that enables agrifood producers to effectively introduce advanced digital technologies, like intelligent and autonomous unmanned ground vehicles (UGVs), in real-world field operations. To that end, we first provide a critical taxonomy of studies investigating agricultural robotic systems with regard to: (i) the analysis approach, i.e., simulation, emulation, real-world implementation; (ii) farming operations; and (iii) the farming type. Our analysis demonstrates that simulation and emulation modelling have been extensively applied to study advanced agricultural machinery while the majority of the extant research efforts focuses on harvesting/picking/mowing and fertilizing/spraying activities; most studies consider a generic agricultural layout. Thereafter, we developed AgROS, an emulation tool based on the Robot Operating System, which could be used for assessing the efficiency of real-world robot systems in customized fields. The AgROS allows farmers to select their actual field from a map layout, import the landscape of the field, add characteristics of the actual agricultural layout (e.g., trees, static objects), select an agricultural robot from a predefined list of commercial systems, import the selected UGV into the emulation environment, and test the robot's performance in a quasi-real-world environment. AgROS supports farmers in the ex-ante analysis and performance evaluation of robotized precision farming operations while lays the foundations for realizing "digital twins" in agriculture. [ABSTRACT FROM AUTHOR]

This paper presents the mechatronic system design and development of a four-wheel drive/steer (4WD4S) mobile robot as a non-holonomic omnidirectional robot, MARIO—Mobile Autonomous Robot for Intelligent Operations. An innovative integrated application of CAD/CAM/CAE and RP has been employed for rapid development of the robot chassis and other mechanical parts by using different software tools. Most of the parts were designed by 3D CAD software which allows further CAE analysis including structural and motion analysis. To reduce the manufacturing time and cost, CAM and RP have been used to manufacture the main parts. These master parts are manufactured by workshop machining and 3D printing. Robot Operating System (ROS) alongside Gazebo simulator have been utilized for modelling, simulation, software development and visualization which enabled to develop the optimum embedded system to control the robot. With this integrated approach, a prototype of MARIO has been developed with a reasonably low manufacturing cost and time. [ABSTRACT FROM PUBLISHER]