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For nonlinear systems with uncertain state time delays, an adaptive neural optimal tracking control method based on finite time is designed. With the help of the appropriate LKFs, the time-delay problem is handled. A novel nonquadratic Hamilton-Jacobi-Bellman (HJB) function is defined, where finite time is selected as the upper limit of integration. This function contains information on the state time delay, while also maintaining the basic information. To meet specific requirements, the integral reinforcement learning method is employed to solve the ideal HJB function. Then, a tracking controller is designed to ensure finite-time convergence and optimization of the controlled system. This involves the evaluation and execution of gradient descent updates of neural network weights based on a reinforcement learning architecture. The semi-global practical finite-time stability of the controlled system and the finite-time convergence of the tracking error are guaranteed.

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Road conditions, often degraded by insufficient maintenance or adverse weather, significantly contribute to accidents, exacerbated by the limited human reaction time to sudden hazards like potholes. Early detection of distant potholes is crucial for timely corrective actions, such as reducing speed or avoiding obstacles, to mitigate vehicle damage and accidents. This paper introduces a novel approach that utilizes perspective transformation to enhance pothole detection at different distances, focusing particularly on distant potholes. Perspective transformation improves the visibility and clarity of potholes by virtually bringing them closer and enlarging their features, which is particularly beneficial given the fixed-size input requirement of object detection networks, typically significantly smaller than the raw image resolutions captured by cameras. Our method automatically identifies the region of interest (ROI)-the road area-and calculates the corner points to generate a perspective transformation matrix. This matrix is applied to all images and corresponding bounding box labels, enhancing the representation of potholes in the dataset. This approach significantly boosts detection performance when used with YOLOv5-small, achieving a 43% improvement in the average precision (AP) metric at intersection-over-union thresholds of 0.5 to 0.95 for single class evaluation, and notable improvements of 34%, 63%, and 194% for near, medium, and far potholes, respectively, after categorizing them based on their distance. To the best of our knowledge, this work is the first to employ perspective transformation specifically for enhancing the detection of distant potholes.

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Supportive robotic solutions take over mundane, but essential tasks from human workforce in biomedical research and development laboratories. The newest technologies in collaborative and mobile robotics enable the utilization in the human-centered and low-structured environment. Their adaptability, however, is hindered by the additional complexity that they introduce. In our paper we aim to entangle the convoluted laboratory robot integration architectures. We begin by hierarchically decomposing the laboratory workflows, and mapping the activity representations to layers and components of the automation control architecture. We elaborate the framework in detail on the example of pick-and-place labware transportation - a crucial supportive step, which we identified as the number one area of interest among experts of the field. Our concept proposal serves as a reference architecture model, the key principles of which were used in reference implementations, and are in line with international standardization efforts.

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In mobile robotics, LASER scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due to their accuracy and precision. Most works that use this sensor have their own data representation and their own case-specific modeling strategies, and no common formalism is adopted. To address this issue, this manuscript presents an analytical approach for the identification and localization of objects using 2D LiDARs. Our main contribution lies in formally defining LASER sensor measurements and their representation, the identification of objects, their main properties, and their location in a scene. We validate our proposal with experiments in generic semi-structured environments common in autonomous navigation, and we demonstrate its feasibility in multiple object detection and identification, strictly following its analytical representation. Finally, our proposal further encourages and facilitates the design, modeling, and implementation of other applications that use LASER scanners as a distance sensor.

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Climbing manufacturing robots can create a revolutionary manufacturing paradigm for large and complex components, while the motion control of climbing manipulation-oriented robots (CMo-Rs) is still challenging considering anti-slippage problems. In this study, a CMo-R with full-scenery climbing capability and redundant load-bearing mobility is designed based on magnetic adsorption. A four-wheel kinematic model considering the slipping phenomenon is established. An adaptive kinematic control algorithm based on slip estimation using Lyapunov theory is designed for uncertain inclined planes. For comparison, the traditional PID-based algorithm without slip consideration is implemented as well. Numeric simulations are conducted to tackle the trajectory tracking problems for both circular and linear trajectories on the horizontal plane (HP), 50° inclined plane (50° IP), 60° inclined plane (60° IP), and vertical plane (VP). The results prove that our approach achieves better tracking accuracy. It demonstrated applicability in various climbing scenarios with uncertain inclined planes. The results of experiments also validate the feasibility, applicability, and stability of the proposed approach.

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The self-reconfigurable modular robotic system is a class of robots that can alter its configuration by rearranging the connectivity of their component modular units. The reconfiguration deformation planning problem is to find a sequence of reconfiguration actions to transform one reconfiguration into another. In this paper, a hybrid reconfiguration deformation planning algorithm for modular robots is presented to enable reconfiguration between initial and goal configurations. A hybrid algorithm is developed to decompose the configuration into subconfigurations with maximum commonality and implement distributed dynamic mapping of free vertices. The module mapping relationship between the initial and target configurations is then utilized to generate reconfiguration actions. Simulation and experiment results verify the effectiveness of the proposed algorithm.

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In recent years, precision agriculture and smart farming have been deployed by leaps and bounds as arable land has become increasingly scarce. According to the Food and Agriculture Organization (FAO), by the year 2050, farming in the world should grow by about one-third above current levels. Therefore, farmers have intensively used fertilizers to promote crop growth and yields, which has adversely affected the nutritional improvement of foodstuffs. To address challenges related to productivity, environmental impact, food safety, crop losses, and sustainability, mobile robots in agriculture have proliferated, integrating mainly path planning and crop information gathering processes. Current agricultural robotic systems are large in size and cost because they use a computer as a server and mobile robots as clients. This article reviews the use of mobile robotics in farming to reduce costs, reduce environmental impact, and optimize harvests. The current status of mobile robotics, the technologies employed, the algorithms applied, and the relevant results obtained in smart farming are established. Finally, challenges to be faced in new smart farming techniques are also presented: environmental conditions, implementation costs, technical requirements, process automation, connectivity, and processing potential. As part of the contributions of this article, it was possible to conclude that the leading technologies for the implementation of smart farming are as follows: the Internet of Things (IoT), mobile robotics, artificial intelligence, artificial vision, multi-objective control, and big data. One technological solution that could be implemented is developing a fully autonomous, low-cost agricultural mobile robotic system that does not depend on a server.

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[This corrects the article DOI: 10.3389/frobt.2019.00046.].

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Plant phenotyping and production management are emerging fields to facilitate Genetics, Environment, & Management (GEM) research and provide production guidance. Precision indoor farming systems (PIFS), vertical farms with artificial light (aka plant factories) in particular, have long been suitable production scenes due to the advantages of efficient land utilization and year-round cultivation. In this study, a mobile robotics platform (MRP) within a commercial plant factory has been developed to dynamically understand plant growth and provide data support for growth model construction and production management by periodical monitoring of individual strawberry plants and fruit. Yield monitoring, where yield = the total number of ripe strawberry fruit detected, is a critical task to provide information on plant phenotyping. The MRP consists of an autonomous mobile robot (AMR) and a multilayer perception robot (MPR), i.e., MRP = the MPR installed on top of the AMR. The AMR is capable of traveling along the aisles between plant growing rows. The MPR consists of a data acquisition module that can be raised to the height of any plant growing tier of each row by a lifting module. Adding AprilTag observations (captured by a monocular camera) into the inertial navigation system to form an ATI navigation system has enhanced the MRP navigation within the repetitive and narrow physical structure of a plant factory to capture and correlate the growth and position information of each individual strawberry plant. The MRP performed robustly at various traveling speeds with a positioning accuracy of 13.0 mm. The temporal-spatial yield monitoring within a whole plant factory can be achieved to guide farmers to harvest strawberries on schedule through the MRP's periodical inspection. The yield monitoring performance was found to have an error rate of 6.26% when the plants were inspected at a constant MRP traveling speed of 0.2 m/s. The MRP's functions are expected to be transferable and expandable to other crop production monitoring and cultural tasks.

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The Laboratory Automation Plug & Play (LAPP) framework is an over-arching reference architecture concept for the integration of robots in life science laboratories. The plug & play nature lies in the fact that manual configuration is not required, including the teaching of the robots. In this paper a digital twin (DT) based concept is proposed that outlines the types of information that must be provided for each relevant component of the system. In particular, for the devices interfacing with the robot, the robot positions must be defined beforehand in a device-attached coordinate system (CS) by the vendor. This CS must be detectable by the vision system of the robot by means of optical markers placed on the front side of the device. With that, the robot is capable of tending the machine by performing the pick-and-place type transportation of standard sample carriers. This basic use case is the primary scope of the LAPP-DT framework. The hardware scope is limited to simple benchtop and mobile manipulators with parallel grippers at this stage. This paper first provides an overview of relevant literature and state-of-the-art solutions, after which it outlines the framework on the conceptual level, followed by the specification of the relevant DT parameters for the robot, for the devices and for the facility. Finally, appropriate technologies and strategies are identified for the implementation.

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Miniature untethered robots attract growing interest as they have become more functional and applicable to disruptive biomedical applications recently. Particularly, the soft ones among them exhibit unique merits of compliance, versatility, and agility. With scarce onboard space, these devices mostly harvest energy from environment or physical fields, such as magnetic and acoustic fields and patterned lights. In most cases, one device only utilizes one energy transmission mode (ETM) in powering its activities to achieve programmed tasks, such as locomotion and object manipulation. But real-world tasks demand multifunctional devices that require more energy in various forms. This work reports a liquid metal-elastomer composite with dual-ETM using one magnetic field for miniature untethered multifunctional robots. The first ETM uses the low-frequency (<100 Hz) field component to induce shape-morphing, while the second ETM employs energy transmitted via radio-frequency (20 kHz-300 GHz) induction to power onboard electronics and generate excess heat, enabling new capabilities. These new functions do not disturb the shape-morphing actuated using the first ETM. The reported material enables the integration of electric and thermal functionalities into soft miniature robots, offering a wealth of inspirations for multifunctional miniature robots that leverage developments in electronics to exhibit usefulness beyond self-locomotion.

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In hospitals, trained medical staff are often, in addition to performing complex procedures, spending valuable time on secondary tasks such as transporting samples and medical equipment; or even guiding patients and visitors around the premises. If these non-medical tasks were automated by deploying mobile service robots, more time can be focused on treating patients or allowing well-deserved rest for the potentially overworked healthcare professionals. Automating such tasks requires a human-aware robotic mobility system that can among other things navigate the hallways of the hospital; predictively avoid collisions with humans and other dynamic obstacles; coordinate task distribution and area coverage within a fleet of robots and other IoT devices; and interact with the staff, patients and visitors in an intuitive way. This work presents the results, lessons-learned and the source code of deploying a heterogeneous mobile robot fleet at the Tartu University Hospital, performing object transportation tasks in areas of intense crowd movement and narrow hallways. The primary use-case is defined as transporting time-critical samples from an intensive care unit to the hospital lab. Our work builds upon Robotics Middleware Framework (RMF), an open source, actively growing and highly capable fleet management platform which is yet to reach full maturity. Thus this paper demonstrates and validates the real-world deployment of RMF in an hospital setting and describes the integration efforts.

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For robots navigating using only a camera, illumination changes in indoor environments can cause re-localization failures during autonomous navigation. In this paper, we present a multi-session visual SLAM approach to create a map made of multiple variations of the same locations in different illumination conditions. The multi-session map can then be used at any hour of the day for improved re-localization capability. The approach presented is independent of the visual features used, and this is demonstrated by comparing re-localization performance between multi-session maps created using the RTAB-Map library with SURF, SIFT, BRIEF, BRISK, KAZE, DAISY, and SuperPoint visual features. The approach is tested on six mapping and six localization sessions recorded at 30 min intervals during sunset using a Google Tango phone in a real apartment.

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We study the collective motion of autonomous mobile agents in a ringlike environment. The agents' dynamics are inspired by known laboratory experiments on the dynamics of locust swarms. In these experiments, locusts placed at arbitrary locations and initial orientations on a ring-shaped arena are observed to eventually all march in the same direction. In this work we ask whether, and how fast, a similar phenomenon occurs in a stochastic swarm of simple locust-inspired agents. The agents are randomly initiated as marching either clockwise or counterclockwise on a discretized, wide ring-shaped region, which we subdivide into k concentric tracks of length n. Collisions cause agents to change their direction of motion. To avoid this, agents may decide to switch tracks to merge with platoons of agents marching in their direction. We prove that such agents must eventually converge to a local consensus about their direction of motion, meaning that all agents on each narrow track must eventually march in the same direction. We give asymptotic bounds for the expected time it takes for such convergence or "stabilization" to occur, which depends on the number of agents, the length of the tracks, and the number of tracks. We show that when agents also have a small probability of "erratic", random track-jumping behavior, a global consensus on the direction of motion across all tracks will eventually be reached. Finally, we verify our theoretical findings in numerical simulations.

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Population aging and pandemics have been shown to cause the isolation of elderly people in their houses, generating the need for a reliable assistive figure. Robotic assistants are the new frontier of innovation for domestic welfare, and elderly monitoring is one of the services a robot can handle for collective well-being. Despite these emerging needs, in the actual landscape of robotic assistants, there are no platforms that successfully combine reliable mobility in cluttered domestic spaces with lightweight and offline Artificial Intelligence (AI) solutions for perception and interaction. In this work, we present Marvin, a novel assistive robotic platform we developed with a modular layer-based architecture, merging a flexible mechanical design with cutting-edge AI for perception and vocal control. We focus the design of Marvin on three target service functions: monitoring of elderly and reduced-mobility subjects, remote presence and connectivity, and night assistance. Compared to previous works, we propose a tiny omnidirectional platform, which enables agile mobility and effective obstacle avoidance. Moreover, we design a controllable positioning device, which easily allows the user to access the interface for connectivity and extends the visual range of the camera sensor. Nonetheless, we delicately consider the privacy issues arising from private data collection on cloud services, a critical aspect of commercial AI-based assistants. To this end, we demonstrate how lightweight deep learning solutions for visual perception and vocal command can be adopted, completely running offline on the embedded hardware of the robot.

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Mechatronic systems, like mobile robots, are fairly complex. They are composed of electromechanical actuation components and sensing elements supervised by microcontrollers running complex embedded software. This paper proposes a novel approach to aid mobile robotics developers in adopting a rigorous development process to design and verify the robot's detection and mitigation capabilities against random hardware failures affecting its sensors or actuators. Unfortunately, assessing the interactions between the various safety/mission-critical subsystem is quite complex. The failure mode effect analysis (FMEA) alongside an analysis of the failure detection capabilities (FMEDA) are the state-of-the-art methodologies for performing such an analysis. Various guidelines are available, and the authors decided to follow the one released by AIAG&VDA in June 2019. Since the robot's behavior is based on embedded software, the FMEA has been integrated with the hardware/software interaction analysis described in the ECSS-Q-ST-30-02C manual. The core of this proposal is to show how a simulation-based approach, where the mechanical and electrical/electronic components are simulated alongside the embedded software, can effectively support FMEA. As a benchmark application, we considered the mobility system of a proof-of-concept assistance rover for Mars exploration designed by the D.I.A.N.A. student team at Politecnico di Torino. Thanks to the adopted approach, we described how to develop the detection and mitigation strategies and how to determine their effectiveness, with a particular focus on those affecting the sensors.

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The inspection and maintenance of large-scale industrial structures are major challenges that require time-efficient and reliable solutions to ensure the healthy condition of structures during operation. Autonomous robots may provide a promising solution for this purpose. In particular, they could lead to faster and more reliable inspection and maintenance without direct intervention from human operators. In this paper, we present a custom magnetic crawler system, and sensor suit and sensing modalities to enable such robotic operation. We first describe a localization framework based on a mesh created from a point cloud coupled with Inertial Measurement Unit (IMU) and Ultra-Wide Band (UWB) readings. Next, we introduce a mapping framework that relies on a 3D laser, and explicitly state how autonomous navigation and obstacle avoidance can be developed. Lastly, we present how ultrasonic guided waves (UGWs) are integrated into the system to provide accurate robot localization and structural feature mapping by relying on acoustic reflections in combination with the other systems. It is envisioned that long-range inspection capabilities that are not yet available in current industrial mobile platforms could emerge from the designed robotic system.

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Currently, systems installed on large-scale aerospace structures are manually equipped by trained operators. To improve current methods, an automated system that ensures quality control and process adherence could be used. This work presents a mobile robot capable of autonomously inspecting aircraft systems and providing feedback to workers. The mobile robot can follow operators and localise the position of the inspection using a thermal camera and 2D lidars. While moving, a depth camera collects 3D data about the system being installed. The in-process monitoring algorithm uses this information to check if the system has been correctly installed. Finally, based on these measurements, indications are shown on a screen to provide feedback to the workers. The performance of this solution has been validated in a laboratory environment, replicating a trailing edge equipping task. During testing, the tracking and localisation systems have proven to be reliable. The in-process monitoring system was also found to provide accurate feedback to the operators. Overall, the results show that the solution is promising for industrial applications.

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Increasing the level of automation in pharmaceutical laboratories and production facilities plays a crucial role in delivering medicine to patients. However, the particular requirements of this field make it challenging to adapt cutting-edge technologies present in other industries. This article provides an overview of relevant approaches and how they can be utilized in the pharmaceutical industry, especially in development laboratories. Recent advancements include the application of flexible mobile manipulators capable of handling complex tasks. However, integrating devices from many different vendors into an end-to-end automation system is complicated due to the diversity of interfaces. Therefore, various approaches for standardization are considered in this article, and a concept is proposed for taking them a step further. This concept enables a mobile manipulator with a vision system to "learn" the pose of each device and - utilizing a barcode - fetch interface information from a universal cloud database. This information includes control and communication protocol definitions and a representation of robot actions needed to operate the device. In order to define the movements in relation to the device, devices have to feature - besides the barcode - a fiducial marker as standard. The concept will be elaborated following appropriate research activities in follow-up papers.

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The automation of processes in all areas of the life sciences will continue to increase in the coming years due to an ever increasing number of samples to be processed, an increasing need to protect laboratory personnel from infectious material and increasing cost pressure. Depending on the requirements of the respective application, different concepts for automation systems are available, which have a different degree of automation with regard to data handling, transportation tasks, and the processing of the samples. Robots form a central component of these automation concepts. Classic stationary robots from the industrial sector will increasingly be replaced by new developments in the field of light-weight robots. In addition, mobile robots will also be of particular importance in the automation of life science laboratories in the future, especially for transportation tasks between different manual and (partially) automated stations. With an increasing number of different, highly diverse processes, the need for special devices and system components will also increase. This applies to both, the handling of the labware and the processing of the samples. In contrast to previous automation strategies with a highly parallel approach, future developments will increasingly be characterized by individual sample handling.

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