Bioinspired Flexible Capacitive Sensor for Robot Positioning in Unstructured Environments.

Lens arrays enable us to easily achieve inexpensive light field acquisition in comparison with massive camera arrays. However, their micro-lenses often cause insufficient exposure to obtain high quality multi-view videos like camera arrays. In this paper, we propose a novel virtual camera array acquiring dynamic light fields as multi-view videos robustly and inexpensively, where mirror arrays consisting of tangent planes to a paraboloid are effectively combined with a single video camera. First, we demonstrate multi-view imaging of not an object but ordinary scenes with sufficient exposure by using a simple horizontal mirror array implemented as a prototype. Next, we theoretically reveal that there exists smooth extension of our mirror array to the vertical direction, that can be implemented easily by 3D printing, CNC machines, and so on. Finally, our design of light field acquisition is improved for effectively utilizing the whole of its mirror array by introducing an one-way mirror to prevent the camera itself from appearing on multi-view images.

It is known that ultra-high temperature drilling fluids are still quite a challenge to drilling engineering. When bottomhole temperatures exceed 200°C, the design and formulation of drilling fluids can present a host of problems. Drilling fluids destabilize under such extreme conditions, possibly causing wellbore instability, well control problems, and ultimately loss of the well. Drilling buried hill hydrocarbon reservoir in offshore oilfield around Bohai Sea Bay Basin in China has often resulted in failure to reach desired objectives. Bottomhole temperatures up to 220°C, however, the pressures requiring drilling fluid weights just to 1.04 to 1.10 g/cm3 equivalent place severe limitations on the performance of drilling fluids and often contribute in failure to reach the desired drilling objectives. Polymers as viscosifying agent that control rheological properties and fluid loss create major challenges for drilling fluid performance and stability when temperatures over 200°C are reached. Additionally, the gelation of drilling fluids containing low solids content and high fluid loss can become difficult to control under ultra-high temperature conditions. Both of these issues are compounded when the ultra-high temperature is coupled with a requirement for low drilling fluids density. The wildcat well NP3-81 and NP3-82 were drilled in this offshore oilfield around Bohai Sea Bay Basin on January 2014. TD of 6066 meters and 6037 meters were attained, respectively. Both of the bottomhole static temperatures were exceed 220°C and the pore pressure was 1.04 to 1.10 g/cm3 equivalent. Logs were run to bottom without incident and drilling fluids related problems and the primary drilling objectives achieved. The success is attributed to the new technology, fit-for-purpose drilling fluids and rigorous pre-well planning. The paper describes the laboratory development and field application of a new technology of environmentally acceptable and seawater discharged drilling fluids designed for ultra-high temperature environments, highlighting the viscosifying agent which has excellent ultra-high temperature resistance and salt resistance viscosifying property, thermal stability and thermo sensitive characteristics. The apparent viscosity retention rate of novel viscosifier after aging at 220°C after 16 h were 90.81% and 95.95% respectively and the EC50 value was 15.529×104 mg/L in both fresh water based drilling fluid and salt-water based drilling fluid. Field application data shows that this new polymer viscosifying agent could play a critical role even though at harsh conditions such as ultra-high temperature and low density drilling fluids system which decreasing daily performance maintenance problems at the expense of the large consumption of fluid additive including conventional sulfonate copolymer.

Intrinsically soft and fully recyclable robotic sensors with quadruple sensing functions for reliable human-robot interactions.

Flexible and wearable strain sensors for human-computer interaction, health monitoring, and soft robotics have drawn widespread attention to promising applications in the next generation of artificial intelligence devices. However, conventional semiconductor sensors are difficult to meet the requirements of flexibility and stretchability. Here, we reported a kind of novel and simple sensor based on layer-by-layer (LBL) method. Carbon nanotubes (CNTs) layer provides high ductility and stability in the process of tension sensing, while silver layer provides low initial resistance and fast reflecting in the process of tension sensing. LBL method ensures the uniformity of the conductive layer. The sensor has superior sheet resistance of 9.44 Ω/sq., high elongation at break of 104%. For sensing capability, the sensor has wide reflecting range of 60%, high gauge factor (GF) of 1000 up to 60% strain, fast reflecting time of 165 ms. Excellent reliability and stability have also been verified. It is also worth mentioning that the entire process does not require any expensive equipments, complicated processes or harsh experimental conditions. The above features provide an idea for large-scale application of flexible stretchable sensors.

This study focuses on the design of intelligent equipment for efficient operation in unstructured environments, concentrating on the construction and performance validation of a “mechanical execution-intelligent control” collaborative system. It emphasizes embedded control, sensor integration, and multi-scenario data-driven performance optimization solutions. First, an intelligent control system centered on embedded control is designed, comprising: - A long-range remote control module (utilizing MC6C/MC7RB communication units based on the 2.4GHz ISM band, enabling interference-resistant command transmission and status feedback over 800 meters) - An identification spotlight module (integrating the K210 visual recognition module, performing typical target detection via CNN Accelerator, and featuring high-efficiency LED lighting for auxiliary perception) and a scanning ranging module (utilizing the N10 LiDAR for 360° two-dimensional scanning based on TOF principles, with a 20,000 measurements/second frequency ensuring ±3cm accuracy in environmental data acquisition). All modules form a “perception-decision-execution” data closed-loop via standardized communication protocols. Second, hardware-intelligent control adaptation design was completed. This involved optimizing power unit hardware interfaces (drive adaptation between brushless DC motors and duct structures), modular partitioning of the control cabin (embedded layout integrating SLAM LiDAR and vision modules), and quick-release connection structures. These provide stable physical carriers for intelligent modules, ensuring efficient command execution. Finally, multi-scenario physical testing and data validation were conducted. Data from various operating conditions—collected via infrared speed measurement, GPS positioning, and attitude sensors—was used to calibrate metrics including water resistance, speed, and steering performance. This validated the intelligent modules' data transmission stability, environmental prediction safety, and system coordination adaptability, proving the equipment meets rapid deployment requirements in complex environments.

Simultaneous localization and mapping (SLAM) is essential for mobile robotics, enabling autonomous navigation in dynamic, unstructured outdoor environments without relying on external positioning systems. These environments pose significant challenges due to variable lighting, weather conditions, and complex terrain. Visual‐Inertial SLAM has emerged as a promising solution for robust localization under such conditions. This paper benchmarks several open‐source visual‐Inertial SLAM systems, including traditional methods (ORB‐SLAM3, VINS‐Fusion, OpenVINS, Kimera, and SVO Pro) and learning‐based approaches (HFNet‐SLAM, AirSLAM), to evaluate their performance in unstructured natural outdoor settings. We focus on the impact of loop closing on localization accuracy and computational demands, providing a comprehensive analysis of these systems' effectiveness in real‐world environments and especially their application to embedded systems in outdoor robotics. Our contributions further include an assessment of varying frame rates on localization accuracy and computational load. The findings highlight the importance of loop closing in improving localization accuracy while managing computational resources efficiently, offering valuable insights for optimizing Visual‐Inertial SLAM systems for practical outdoor applications in mobile robotics. The data set and the benchmark code are available under https://github.com/iis-esslingen/vi-slam_lc_benchmark .

This paper presents a fuzzy system approach that incorporates sensing, control and planning to enable micro wall climbing robots navigating in unstructured environments. Based on a fuzzy multi-sensor data fusion scheme, a novel strategy for integrating task scheduling, sensing, planning and real-time execution is proposed so that task scheduling, action planning and motion control can be treated in a unified framework, and the design of task synchronization becomes much easier. A hybrid method is employed to create robot's gaits by switching the robot between continuous motion modes with the help of a finite state machine driven by the signals from robot sensors. For a highly non-linear system as the micro wall climbing robot, a fuzzy motion controller is designed to improve the robot's performance at different situations. Experimental results prove the validity of the proposed method.

The ability of autonomous or semi-autonomous mobile robots to rapidly and accurately predict their mobility characteristics is an important requirement for their use in unstructured environments. Most methods for mobility prediction, however, assume precise knowledge of environmental (i.e. terrain) properties. In practical conditions, significant uncertainty is associated with terrain parameter estimation from robotic sensors, and this uncertainty must be considered in a mobility prediction algorithm. Here a method for efficient mobility prediction based on the stochastic response surface approach is presented that explicitly considers terrain parameter uncertainty. The method is compared to a Monte Carlo-based method and simulations show that the stochastic response surface approach can be used for efficient, accurate prediction of mobile robot mobility.

Polymer-based substrate with patterned microstructures has been widely utilized for the development of cell chip in tissue engineering and/or flexible sensors in robotics. The key challenge is to fabricate the polymer-based substrate with the localized patterned microstructures. In this study, we presented a method to fabricate localized microstructures by standing surface acoustic wave (SSAW) and user-defined waveguides. To investigate the working mechanism, we developed a 3D numerical model to analyze the induced acoustic pressure distribution and final generated microstructures. Results demonstrated that the utilized waveguide could localize the acoustic pressure field in a specified region within the prepared photosensitive fluid film based on Rayleigh’s radiation theory and capillary wave motion. By adjusting the SSAW driven modes and using different shaped waveguides, both numerical modeling and experimental tests showed that the localized microstructures can form on the liquid film and then successfully fabricated using ultraviolet (UV) solidification. Therefore, our developed method by using the SSAW and waveguide provides a promising alternative process to fabricate the polymer-based microstructure with localized patterns, and the fabricated polymer-based microstructures could be used for the development of flexible sensors, actuator, and/or soft robotics in future applications.

Flexible piezoresistive sensor based on surface modified dishcloth fibers for wearable electronics device

A mobile robot system usually has multiple sensors of various types. In a dynamic and unstructured environment, information processing and decision making using the data acquired by these sensors pose a signi.cant challenge. Kalman .lter- based methods have been developed for fusing data from various sensors for mobile robots. However, the Kalman .lter methods are computationally intensive. Markov and Monte Carlo methods are even less e.cient than Kalman .lter methods. In this paper, we present an alternative method based on principal component analysis (PCA) for processing the data acquired with multiple sensors.KeywordsPrincipal Component AnalysisMobile RobotData FusionMultiple SensorInfrared SensorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Divers have to perform technical underwater tasks in dangerous and unstructured environments. To reduce a diver's workload and improve overall safety, an underwater robotic assistant (UWRA) could be deployed with the diver. The UWRA could assist the diver in navigation, quickly ferry tools from the surface, and carry underwater samples. To develop and test the autonomy required for such a UWRA, autonomy developers could benefit from a simulated underwater environment. Unfortunately, a real-time simulation for one of the most important sensors in underwater robotics, a forward-looking 2D imaging sonar, does not exist in robotics simulators. We developed a simulation for 2D imaging sonars that executes in real-time in the Gazebo robotics simulator. The 2D imaging sonar simulator was assessed by comparing the sonar images that it generated with sonar images generated by real sonar systems in similar environments. For example, we tested both the simulated and real sonar systems in environments with underwater man-made structures, divers, and varying terrain. The sonar simulator was also assessed based on its computational complexity and compared to other similar simulators with respect to sonar image generation rates. We found that our 2D imaging sonar simulator generated qualitatively realistic images and that it was computationally efficient enough to execute in real-time with the Gazebo simulator.

A flexible tactile sensor has the ability to sense and characterize the firmness and mechanical stiffness of the object. In this work, we demonstrate a low-cost, highly sensitive, flexible capacitive tactile sensor using a nanoneedle-patterned polydimethylsiloxane (PDMS) as a dielectric layer to detect the ripeness of the fruits. The nanoneedle-patterned PDMS was formed using an anodic oxidized porous alumina film as a mold. The developed tactile sensor exhibited high sensitivity due to the high deformity and elasticity of the nanoneedles on the PDMS layer. The higher sensitivity of 1.042–0.67 kPa−1 for a low-pressure regime below 0.2 kPa and the lower sensitivity of 0.35 kPa−1 for a pressure range above 0.6 kPa were demonstrated. This corresponds to a ~12% change in capacitance at 0.1-kPa pressure. Loading unloading tests for over 500 cycles were performed to confirm the absence of hysteresis. The mechanical elastic modulus of different tomato samples was tested using the tactile sensor and a universal test machine. The device could easily discriminate tomatoes of different stiffness. The tomato ripeness detection experiments demonstrate that the sensor can be used in robotic sensors, to estimate the qualitative ripeness of the fruits, during harvesting and transportation. This concept can be utilized in assessing ripeness levels of fruits like mangoes, kiwis, pears, avocados, sapodilla, and so on, after adequate recalibration of the sensor, leading to different sensing ranges.

Transparent and flexible force sensor provides potential applications in a lot of area such as human wearable sensor, robotic sensor and underwater equipment. Here the work presents a zig‐zag patterned hydrogel‐based force sensor. Slow‐gelling alginate hydrogel, self‐designed microextrusion 3D printer and polydimethylsiloxane (PDMS) were adapted for the sensor fabrication. A slow‐gelling hydrogel printing ink was prepared and printed onto the treated PDMS layer with a computer‐aided designed zig‐zag pattern. The line width and resistance of the printed hydrogel were precisely controlled by the self‐designed microextrusion 3D printer. To stabilise the water content, a sandwich structure was adopted and two PDMS cover layers were fabricated to seal the hydrogel sensor. Theoretical analysis was performed and it is shown that the change rate of resistance was linear related to the force, and this analysis was proved by experiment. Experiments also show that the sensor was flexible and transparent. The zig‐zag patterned hydrogel also and the PDMS layers provided stable water content and recording.

Average local image entropy can be seen as an effective measure of a training environments potential toward producing highly fit robots. As seen in Fig.6 the measure is sensitive to different window values but in our application a wide range of values could have been used. Our selection of neighborhood size was a reasonable as only very low values (around 10) show poor discriminancy. Fig. 7 shows how the average fitness obtained during testing is clearly dependent on the diversity of the training room used. A training environment with too much environmental diversity is as unsuitable as one with not enough diversity. Higher motivation diversity (H ( M)) caused lower average score values. This is possibly due to the different and conflicting (e.g. orthogonal) requirements of the different motivations upon the robotic neural brain. These scores are also negatively impacted by the fitness function used (4) given that it includes a motivation satisfaction product (mn × f n ) in each rule that further penalizes lower behavior fitnesses ( f n ). Even though obtained fitness was generally lower with more diverse motivations, the obtained behaviors demonstrated very good capability (e.g. room exploration, battery usage, etc) and were in agreement with the specified motivations. Because of this, motivation diversity results are somewhat counter intuitive in that by diversifying motivation values one could naively expect higher overall fitness but this is clearly not the case due to system constraints.