This study introduces an adaptive restricted controller designed to address the path-tracking challenge encountered by an autonomous quadrotor aerial vehicle navigating through confined spaces. The quadrotor dynamics are represented mathematically using quaternions, facilitating the development of the adaptive control strategy. The proposed controller incorporates an adaptive state-dependent gain to regulate the quadrotor's motion within permissible airspace. Utilizing a gain auto-tuning approach, the controller ensures convergence of the quadrotor trajectory to a predefined reference path, effectively managing both the position and orientation of the vehicle under quaternion dynamics. The control design integrates a direct barrier dead-zone controlled Lyapunov function, explicitly considering state restrictions. This function validates convergence to the reference path and derives the necessary state-dependent gains for the adaptive controller. Numerical evaluations demonstrate the efficacy of the proposed adaptive controller in achieving superior tracking performance compared to conventional state feedback controllers. Furthermore, the controller ensures compliance with full-state constraints, offering a promising solution for real-world applications. Experimental validation in a setup with predefined state restrictions further confirms the effectiveness of the proposed approach, exhibiting improved tracking characteristics over the proprietary controller installed in the quadrotor.

Reforestation is pivotal in mitigating climate change, preserving biodiversity, and safeguarding ecosystems. Precision reforestation implies efficiently using materials and human resources to conduct this intensive task. In this sense, using unmanned aerial vehicles (UAVs), also known as drones, improves the accuracy of dispensed seeds while increasing coverage and reducing labor. However, drone-based reforestation still presents technological challenges that need to be addressed. An important challenge is the presence of disturbances during flights due to environmental conditions, primarily unexpected wind, and the unavoidable loss of mass presented by the vehicle caused by the dispensing task. This paper evaluates the use of an observer-based controller to reject the particular disturbances caused by the sowing activity. Through meticulous experimentation and analysis, the study demonstrates the observer’s adeptness in mitigating external disturbances, thereby enhancing the precision and stability of UAV operations. This technological advancement holds promise for diverse practical applications and has implications for environmental conservation efforts, particularly reforestation. The obtained experimental results confirm the viability of the proposed controller and observer framework, highlighting its potential to improve the robustness of environmental monitoring, conservation, and sustainable resource management practices.

This paper provides a review of research on robotics in search and rescue operations in caverns, underground coal mines, disaster zones, and environments where the Global Navigation Satellite System (GNSS) is unavailable. The majority of applications for Simultaneous Localization and Mapping (SLAM), despite its maturity, are still restricted to indoor structured scenes or outside conventional weather conditions. The standard SLAM technology will experience malfunctions and perhaps fail as the robot encounters increasingly complicated environments. Three main issues that robots encounter in search and rescue (SAR) environments are outlined in this review, along with an introduction to the classic SLAM framework and a range of sensors used for SLAM. These issues include robots encountering increasingly complex terrain, changing environments and visibility, and autonomous exploration requirements. A thorough analysis of methods for resolving these problems is also included; they include multi-sensor fusion, active SLAM, and SLAM on uneven terrain. Also, a prospective path for future study is presented.

The ability to perceive environments supports an important foundation for our self-developed robotic rat to improve kinematic performance and application potential. However, the most existing visual perception of quadruped robots suffers from poor perception accuracy in real-world dynamic environments. To mitigate the problem of erroneous data correlation, which is the main cause of low accuracy, the work presents an approach that combines leg odometry (LO) and IMU measurements with VSLAM to provide robust localization capabilities for small-scale quadruped robots in challenging scenarios by estimating the depth map and removing moving objects in dynamic environments. The method contains a depth estimation network with higher accuracy by combining the attention mechanism in the Transformer with the RAFT-Stereo depth estimation algorithm. Besides, the method combines target identification and segmentation with 3D projection of feature points to remove moving objects in dynamic environments. In addition, LO and IMU data are fused in the modified framework of ORB-SLAM3 to achieve highly accurate localization. The proposed approach is robust against erroneous data correlation due to moving objects and wobbles of quadruped robots. Evaluation results on multiple stages demonstrate that the system performs competitively in dynamic environments, outperforming existing visual perception methods in both public benchmarks and our costumed small-scale robotic rat.

The integration of precise landing capabilities into UAVs is crucial for enabling autonomous operations, particularly in challenging environments such as the offshore scenarios. This work proposes a heterogeneous perception system that incorporates a multimodal fiducial marker, designed to improve the accuracy and robustness of autonomous landing of UAVs in both daytime and nighttime operations. This work presents ViTAL-TAPE, a visual transformer-based model, that enhance the detection reliability of the landing target and overcomes the changes in the illumination conditions and viewpoint positions, where traditional methods fail. VITAL-TAPE is an end-to-end model that combines multimodal perceptual information, including photometric and radiometric data, to detect landing targets defined by a fiducial marker with 6 degrees-of-freedom. Extensive experiments have proved the ability of VITAL-TAPE to detect fiducial markers with an error of 0.01 m. Moreover, experiments using the RAVEN UAV, designed to endure the challenging weather conditions of offshore scenarios, demonstrated that the autonomous landing technology proposed in this work achieved an accuracy up to 0.1 m. This research also presents the first successful autonomous operation of a UAV in a commercial offshore wind farm with floating foundations installed in the Atlantic Ocean. These experiments showcased the system's accuracy, resilience and robustness, resulting in a precise landing technology that extends mission capabilities of UAVs, enabling autonomous and Beyond Visual Line of Sight offshore operations.

The increasing use of mobile robots in everyday life has ne- cessitated the development of efficient path planning algo- rithms to enable safe and reliable navigation in both known and unknown environments. Path planning is the process of discovering an optimal and safe path for a robot to navigate from its starting point to its destination. Finding the ideal collision-free path is one of the most challenging aspects of mobile robot navigation. There are various path-planning techniques to obtain an optimal path in the presence of ob- stacles; an optimal path planning technique of mobile robot saves time and reduces capital investment. These techniques can be divided into two categories viz clas- sical and heuristic. This paper provides a comprehensive re- view of path planning techniques for mobile robot naviga- tion in known and unknown environments. Path planning becomes more challenging in unknown environments, where a robot does not have prior knowledge about the surround- ings. In contrast, path planning in known environments can utilize prior knowledge about the environment to find an op- timal path. When comparing classical and heuristic approaches, it has been found that heuristic approaches are more resilient and perform well in all environments because they can deal with uncertainties. Heuristic approaches are also employed as hybrid algorithms to increase the performance of the clas- sical approaches. Heuristic techniques are well-known and frequently used for mobile robot path planning. Key opti- mization criteria, including path length, smoothness and safety degree, are also critically assessed. The paper also tabulates the advantages, disadvantages, and comparison of classical and heuristic path-planning techniques. The paper finally concludes by discussing potential future research directions to provide a roadmap for future advancements in mobile robot path planning techniques

Several room disinfection robots using Ultraviolet C (UV-C) light have emerged recently, especially with the COVID-19 outbreak. This systematic review aims to identify the current development status of Ultraviolet Germicidal Irradiation (UVGI) disinfection robots, their limitations, and the technologies they use. An automated search was performed on the Scopus, ACM Digital Library, IEEE Xplore, and SpringerLink platforms for papers up to July 2023, followed by a snowballing search. Were found 96 studies, of which the majority were not concerned with the dose of UVGI applied or did not implement any technique to deliver the appropriate dose; the positioning of the lamps was carried out subjectively; and most of the works did not implement any technique to prevent accidents with UVGI. From the analysis of these studies, it was possible to propose a novel classification for the different types of robots based on their technological and readiness levels. The data shows that despite recent advances, the development of disinfection robots is still early, with many advances to be made.

Multi-arm harvesting robots offer a promising solution to the labor shortage in fruit harvesting, due to their ability to improve harvesting efficiency. However, multi-arm harvesters necessitate additional visual sensors to acquire distribution information of fruits within larger working spaces. Greater demands are consequently imposed on graphics computation, leading to increased costs in computing hardware of robot system. To balance the graphics computing cost and reduce energy consumption, distributed graphics computation frameworks for multi-arm robot vision system are proposed in this study. First, a host-edge framework is proposed to assign the tasks of image inference and depth alignment to host computer and edge computing modules through a decentralized mode of local connection. Moreover, to increase the endurance time of robot in application, the edge computing modules are reduced and the fifth generation mobile communication is integrated into robot graphics computing system to transfer on-board image processing to a remote computing server with MQTT protocol. To verify the effectiveness of the proposed framework, comprehensive experiments were performed, demonstrating that, compared with traditional computing framework, the proposed local distributed framework reduced 35.6% average time consumption, and over 20 FPS average processing speed can be achieve. The remote distributed framework has reduced the computational power consumption of the on-board system by approximately 23.1% while ensuring the performance is not lower than the local distributed framework. Finally, by discussing the two frameworks in terms of stability and cost, we present the commercial viability for the application of multi-arm harvesting robot.

In shallow water environments, traditional technologies face challenges in retrieving unexploded ordnance (UXO). We present an innovative and safe alternative robotic platform to replace traditional divers in this dangerous task. A waterproof eighteen-DOF legged robot MR. Crab has been developed specifically for the purpose of locating and recovering UXO. Utilizing walking legs for both locomotion and manipulation, the robot demonstrates the capability to pick up inert UXO weighing at 2.78kg (half robot’s mass) with one pair of walking legs and 10.5kg (184% of robot’s mass) when utilizing all three pairs of legs. To enhance our understanding of the gripping capacity, static analysis was conducted to predict the maximum weight of graspable UXO with respect to UXO radii. For easier control, a user-friendly GUI had been built for remote teleoperation. Additionally, an automatic gripping algorithm was developed to efficiently place the robot in the optimal gripping position and then grab the inert UXO.

The precise localization of mobile robots in unstructured environments is of utmost importance for many industrial and field applications, especially when the mobile robot is part of a more complex kinematic chain, such as a mobile manipulator. Being able to precisely localize affects the outcome of tasks that rely on an open-loop kinematic computation, such as work-station docking procedures. To achieve a repeatable and precise localization and positioning, mobile robots generally rely on onboard sensors, most commonly 2D laser scanners, whose readings are subjected to noise and numerous disturbing factors (e.g., materials reluctance). Problems arise when precise localization is needed in dynamic and unstructured environments where generally applicable methods won’t perform adequately or might be time-consuming to set up. In this work, we propose a cloud-edge computing architecture to deploy a recurrent neural network (RNN) based registration system, which uses a pair of consecutive LiDAR readings to estimate a fixed transformation. The capability of RNNs to process contiguous inputs will help neglect errors embedded in punctual laser scanner readings and output a more precise registration estimation. In such a way, the RNN can estimate a displacement error based on multiple consecutive readings and act as a sensor to be employed in a closed-loop control scheme. To tackle the dynamic and unstructured environments, the model is firstly tuned on synthetic LiDAR data to embed rigid transformations into the deep learning model, for then rapidly fine-tuned on local scenarios. After model architecture and optimization of hyperparameters, the devised model is tested in different scenarios, comparing the precise positioning capability of the AMR(autonomous mobile robot) with that of a classical registration algorithm. The results suggest that an RNN model can greatly improve the registration precision of laser scanner signals and, consequently, the precise positioning efficiency of AMRs.

This paper studies the coverage path planning and path following problems for an underwater biomimetic robotic fish to finish the deep-sea net cage water quality monitoring. Firstly, with a focus on minimizing total path length, repetition rate, and turning occurrences to enhance path coverage efficiency and reduce energy consumption, we propose a novel coverage path planning strategy. This strategy incorporates DQN, a reward function, and a rewrite strategy inspired by RRT*. Secondly, a high-performance path-following method, which takes into account robot performance, is designed to cope with adverse conditions in net cages. Finally, the simulation and field experiments demonstrate significant improvements over existing methods and the effectiveness in practical applications, showcasing its applicability in aquaculture management. The proposed algorithm offers valuable insights into optimizing coverage path planning for underwater robots in practical scenarios.

This article presents the first ever fully autonomous UAV (Unmanned Aerial Vehicle) mission to perform gas measurements after a real blast in an underground mine. The demonstration mission was deployed around 40 minutes after the blast took place, and as such realistic gas levels were measured. We also present multiple field robotics experiments in different mines detailing the development process. The presented novel autonomy stack, denoted as the Routine Inspection Autonomy (RIA) framework, combines a risk-aware 3D path planning D + ∗ , with 3D LiDAR-based global relocalization on a known map, and it is integrated on a custom hardware and a sensing stack with an onboard gas sensing device. In the presented framework, the autonomous UAV can be deployed in incredibly harsh conditions (dust, significant deformations of the map) shortly after blasting to perform inspections of lingering gases that present a significant safety risk to workers. We also present a change detection framework that can extract and visualize the areas that were changed in the blasting procedure, a critical parameter for planning the extraction of materials, and for updating existing mine maps. As will be demonstrated, the RIA stack can enable robust autonomy in harsh conditions, and provides reliable and safe navigation behavior for autonomous Routine Inspection missions.

Over the last few decades, the agricultural industry has witnessed significant advancements in autonomous systems, primarily aimed at improving efficiency while reducing environmental impact. The critical role of complete coverage path planning cannot be overstated in this context. It involves determining an optimal path for tasks such as harvesting, mowing, and spraying, taking into account various factors like land topography, operational requirements, and robot characteristics. Our previous approach introduced a tree-based exploration method to generate potential solutions, coupled with an optimization process to select the best ones, considering field complexities and robot characteristics. Yet, despite its strengths, it had certain limitations, notably in computational time and number of examined driving directions. In this paper, we present a novel hybrid method that combines the comprehensive coverage benefits of our original approach with the computational efficiency of the Fields2Cover algorithm. Besides combining our previous approach and Fieds2Cover strengths for optimizing coverage area, overlaps, non-working path length and overall travel time, it significantly improves the computation process, enhances the flexibility of trajectory generation. It also takes into account the working trajectory inclinations for more advanced optimization to address soil erosion and energy consumption. In an effort to support this innovative approach, we have also created and made available a public dataset. This dataset includes both 2D and 3D representations of thirty agricultural fields located in France. This resource not only illustrates the effectiveness of our approach but also provides an invaluable data for future research in complete coverage path planning within the context of modern agriculture.

Aiming at the problem of weak yawing controllability of solar-powered Unmanned Aerial Vehicles (UAVs) with low-speed and high aspect-ratio Blended Wing Body (BWB) configuration, based on the large-scale aerodynamic characteristics of the biomimetic swallow tail, the influence of different opening methods and angles of the swallow tail on the aerodynamic characteristics and stability and controllability of an UAV was derived. The mechanism of the higher yawing control efficiency of the differential throttle compared to the conventional rudder is analyzed in order to significantly decrease the control efficiency at small throttle. We innovatively propose a yawing control method combining the opening of the swallow tail to a moderately separation state of local airflow and we validate it for the case of the landing control of a solar-powered UAV. The flight dynamics mode characteristics show that this control method has little impact on the lateral-directional stability of the UAV and that the control efficiency can be reserved to the same level of cruise state. Therefore, the control law of cruise state can be applied to landing state with swallow tail open directly, which enhances the lateral-directional control ability of the UAVs in a simple but efficiently way. Flight simulation and flight test results show that the proposed bionic control method of the swallow tail combined with differential throttle can effectively enhance the landing control ability of the UAVs, improve the response speed, and reduce the trajectory error.

The use of automation in mining can be found at all stages of the mining process, covering exploration, excavation, loading, transportation, mineral processing, and search and rescue missions in emergency situations. Due to the harsh conditions during an underground mine disaster, robots can be of great assistance to rescue teams by entering areas that are unsafe for human rescuers, locating trapped workers and collecting valuable data. The design and implementation of mine rescue robots is characterized by great complexity since it encompasses the consideration of a great range of components and requirements. However, comprehensive guidelines for design and deployment of robots in harsh underground environments have not been established. As a result, the developers of mine rescue robots (MRRs) are to exercise their own judgment on the development and evaluation of the platform functionalities. This review attempts an extensive discussion of design and functionality requirements based on the common practices and lessons learned from the existing MRRs. Common considerations, as well as current and future trends are identified with the aim of facilitating future research and development on mine search and rescue robots.

Agricultural robots offer a viable solution to the critical challenges of productivity and sustainability of modern agriculture. The widespread deployment of agricultural robotic fleets, however, is still hindered by the overall system’s complexity, requiring the integration of several non-trivial components for the operation of each robot but also the orchestration of robots working with each other and human workers. This paper proposes a topological map as the unifying representation and computational model to facilitate the smooth deployment of robotic fleets in agriculture. This topological abstraction of the system state results in an efficient representation of large-scale environments, but also offers the scalable and efficient operation of the entire fleet and allows for ex-situ modelling and analysis of operations. The practical use of the proposed framework is demonstrated in a horticultural use case with a fleet of robots supporting the work of human fruit pickers. The critical components of the system are analysed and evaluated in deployment in both realistic digital twin and real-life soft fruit farms of different scales, demonstrating the scalability and effectiveness of the proposed framework. The presented framework is general and should be easy to adopt in other multi-robot/multi-human scenarios such as warehouse logistics, cleaning and maintenance of public spaces.

Modern mobile robots require precise and robust localization and navigation systems to achieve mission tasks correctly. In particular, in the underwater environment, where Global Navigation Satellite Systems (GNSSs) cannot be exploited, the development of localization and navigation strategies becomes more challenging. Maximum A Posteriori (MAP) strategies have been analyzed and tested to increase navigation accuracy and take into account the entire history of the system state. In particular, a sensor fusion algorithm relying on a MAP technique for Simultaneous Localization and Mapping (SLAM) has been developed to fuse information coming from a monocular camera and a Doppler Velocity Log (DVL) and to consider the landmark points in the navigation framework. The proposed approach can guarantee to simultaneously locate the vehicle, thanks to the onboard sensors, and map the surrounding environment with the information extracted from the images acquired by a bottom-looking optical camera. Optical sensors can provide constraints between the vehicle poses and the landmarks belonging to the observed scene. The DVL measurements have been employed to solve the unknown scale factor and to guarantee the correct vehicle localization even in absence of visual features. After validating the solution through realistic simulations, an experimental campaign at sea was conducted in Stromboli Island (Messina), Italy. In conclusion, an algorithm, which works with the Poisson surface reconstruction method to obtain a smooth seabed surface, for mesh creation has been developed.

Published version:  Journal of Field Robotics https://doi.org/10.1002/rob.22329