Journal of Field Robotics最新文献

Empowered by the combustion-enabled transient driving method (TDM), underwater soft robots exhibit instantaneous high-speed leaps from water, presenting valuable applications in robotic engineering. This study delves into the optimization of hydrodynamics during the high-speed jumping through the water-air interface, aiming to improve the overall performance of the TDM-enabled robots. We developed a Computational Fluid Dynamics (CFD) model to comprehensively investigate the fluid dynamics involved. This model analyzes the flow field induced by the high-speed leaping-out motions of TDM-driven underwater robots, including flow velocity distribution, pressure, turbulence structure, etc. Employing two-phase CFD model coupling with cavitation model and dynamic mesh technology, the CFD model is validated against experimental data, demonstrating satisfactory agreements and effectively improving calculating accuracy. Furthermore, we explore design modifications to improve locomotion performance. This shape optimization boosts locomotion velocity while simultaneously reducing drag resistance (the maximum drag coefficient has decreased 29%.) and turbulent energy dissipation rates (the maximum rate has decreased 26%.). The findings offer valuable insights for advancing the capabilities of underwater soft robots in high-speed cross-phase tasks.

To simplify the soft gripper structure while improving its gripping range, load capacity, and radial load-carrying capacity, Single-axis Deformation (SD) soft grippers were designed, inspired by the blooming behavior of petunias. Four SD finger structures were designed by analyzing the anatomical structure of petunia petals. The stress concentration resulting from the deformation of the fingers with different structures was analyzed using numerical methods under the same deformation load conditions. To verify the gripping ability of the SD soft gripper, we conducted experiments on opening and closing the SD soft gripper, as well as gripping tests on objects of various shapes and sizes, and also tested its radial load-carrying capacity. The experimental results showed that the opening and closing range of the SD soft gripper is 18–144 mm, the maximum gripping load is 39.4 N, and the radial bending angle is only 2.76° when the radial load is 24 N.

On-board strawberry-picking robots offer the potential to significantly reduce labor costs and enhance picking efficiency. How to achieve high precision and fast strawberry recognition on resource-constrained edge devices is the key to robotic strawberry harvesting. Before developing our model, two lightweighting methods that maintain model structure are explored to substantiate the thesis that only judicious compression strategies tailored to edge hardware specifications can transform heavyweight deep models into efficient and compact deployments with enhanced performance on embedded devices. Based on this, and in combination with RTSD, Superb Real-time Strawberry Detection (SP-RTSD), which is designed to achieve faster and more accurate strawberry recognition on edge devices. Firstly, the C2f-Faster module performs channel-wise feature screening to enhance feature extraction efficiency while reducing model parameters; secondly, a lightweight recognition head with a parameter sharing mechanism is proposed specially for the edge devices. The speed of SP-RTSD was significantly improved by 22% from 20.63 to 25.18 FPS, which is similar to the 25.2 FPS of RTSD. Without changing the model structure, the model size is reduced by 40.3% from 6.2 to 3.7 MB. In contrast to typical lightweight strategies, which often boost inference speed at the cost of accuracy, SP-RTSD achieves exceptional accuracy with a mean average precision (mAP) of 91.7%, slightly outperforming the original baseline model (90.7%). The improvements in accuracy, speed, and size demonstrate that SP-RTSD addresses the challenge of balancing accuracy with inference speed on edge devices. In comparison experiments with other advanced object detection and lightweight models, as well as tests on additional open-source strawberry data sets, SP-RTSD consistently delivered superior results, affirming its robustness. Furthermore, SP-RTSD demonstrated an impressive combined success rate of 92% in strawberry grasping simulation experiments with a robotic arm, thereby confirming its suitability for integration into practical picking machines.

The rise of Autonomous Electric Vehicles (AEVs) has presented formidable challenges in the automotive sector, demanding advanced sensor technology, intricate control systems, and sophisticated decision-making algorithms. Due to the inherently nonlinear dynamics and uncertainties associated with these vehicles, conventional control methods fall short of providing robust solutions. This study proposes a hybrid approach for coordinated longitudinal and lateral control in autonomous driving scenarios. Addressing lateral and longitudinal control, the research integrates road geometry and lateral dynamics considerations. Utilizing a Proportional Integral Derivative (PID) controller with Fire Hawk Optimizer (FHO) algorithm. This study optimizes controller gains for Nonlinear longitudinal dynamics, ensuring reliable speed tracking. Additionally, a Linear Parameter Varied-Models Predictive Controller (LPV-MPC) addresses the challenges related to time-varying longitudinal speeds and distance impact on vehicle lateral stability. Implementation in the matrix laboratory demonstrates the approach's superiority in terms of speed, precision, stability, trajectory tracking, and achieving a minimal lateral error of 0.0526 and mean error, mean absolute error and root mean squared error of 0.193, 0.087 and 0.108 respectively.

Magnetic Levitation (Maglev) is a technique that involves suspending an object using a magnetic field. This study presents a novel approach for robotic arm joints and end effectors by utilizing the functioning prototype of the Maglev system due to their similar functionality. The proposed approach utilizes a fractional-order enhanced model reference adaptive controller (FOEMRAC) in conjunction with the Coyote optimization algorithm (COA) to control the stability of levitating magnetic objects. The FOEMRAC system employs a modified MIT rule as its adaptation mechanism. The simulation is performed using the Quanser Maglev system, and a comparison is done with other state-of-the-art techniques such as linear quadratic regulator (LQR), particle swarm optimization-LQR (PSO-LQR), LQR + proportional integral (LQR + PI), LQR + proportional integral derivative (LQR + PID), proportional integral voltage + PI (PIV + PI), enhanced model reference adaptive controller (EMRAC), FOEMRAC, and PSO-FOEMRAC, respectively. The robustness of the controllers is assessed using various integral error criteria, such as integral absolute error (IAE), integral square error (ISE), and integral time absolute error (ITAE), respectively. Additionally, rise time, settling time, overshoot, and undershoot have been employed for comparison purposes with load disturbance and parametric uncertainties. The results are also validated on real-time hardware, demonstrating the superior performance of COA-FOEMRAC as compared to various controllers. Thus, it can be effectively employed to improve the functionality of the magnetic joints and magnetic end effectors in real-time applications. A video demonstrating the functioning of the Maglev system is available at this link: https://drive.google.com/file/d/1FrD1YKqRXSTTe44S2-ap126KljiVmOEU/view?usp=drivesdk.

Robust multisensor fusion of multi-modal measurements such as inertial measurement units (IMUs), wheel encoders, cameras, LiDARs, and GPS holds great potential due to its innate ability to improve resilience to sensor failures and measurement outliers, thereby enabling robust autonomy. To the best of our knowledge, this study is among the first to develop a consistent tightly-coupled Multisensor-aided Inertial Navigation System (MINS) that is capable of fusing the most common navigation sensors in an efficient filtering framework, by addressing the particular challenges of computational complexity, sensor asynchronicity, and intra-sensor calibration. In particular, we propose a consistent high-order on-manifold interpolation scheme to enable an efficient asynchronous sensor fusion and state management strategy (i.e., dynamic cloning). The proposed dynamic cloning leverages motion-induced information to adaptively select interpolation orders to control computational complexity while minimizing trajectory representation errors. We perform online intrinsic and extrinsic (spatiotemporal) calibration of all onboard sensors to compensate for poor prior calibration and/or degraded calibration varying over time. Additionally, we develop an initialization method with only proprioceptive measurements of IMU and wheel encoders, instead of exteroceptive sensors, which is shown to be less affected by the environment and more robust in highly dynamic scenarios. We extensively validate the proposed MINS in simulations and large-scale challenging real-world datasets, outperforming the existing state-of-the-art methods, in terms of localization accuracy, consistency, and computation efficiency. We have also open-sourced our algorithm, simulator, and evaluation toolbox for the benefit of the community: https://github.com/rpng/mins.

The multi-joint manipulator with vision sensors has been widely used in real applications. However, the fault detection and diagnosis accuracy are lowered and the time expense is increased for the increased number of sensors, as there are many factors that are relative with this problem. This paper is focused on the fault detection and diagnosis problem of multi-joint manipulator, and the problem was divided into two sub-problems. The first is that the position estimation strategy based on data fusion of visual sensor and the position sensor was designed to carry out the fault detection, and the whether the faults had happened or not were determined by the position estimation errors. The second was focused on the fault diagnosis problem, where the deep convolutional neural network (DCNN) fault diagnosis model based on time-frequency mixed signal was constructed. The proposed DCNN uses the time and frequency domain information as its inputs and executes the classification tasks. The specific fault was determined through the output of DCNN model. The DCNN model was activated only when the first fault detection unit indicated that there was a fault, so the time expense was reduced from 5.3 to 2.6 s. The experiment based on the AUBO-i5 manipulator was carried out to evaluate the proposed fault detection and diagnosis model, where 10 categories of data sets that represent different working conditions of manipulator were adopted. The experimental results showed that the proposed multi-joint manipulator fault detection could improve the position estimation accuracy by 41.2%, and the fault diagnosis accuracy was improved by 20%.

Miniature personal aerial vehicles (PAVs) with vertical take-off and landing (VTOL) capabilities offer significant advantages over conventional vehicles in rescue missions, particularly in terms of compactness, manned flight capability, and load-carrying capacity. However, detailed research work on such systems has been reported infrequently. This paper introduces a miniature VTOL PAV, weighing 55 kg and measuring 45 * 87 * 154 cm. The PAV is equipped with five vertically arranged micro-turbojet engines that enable VTOL capabilities and support a load capacity exceeding 100 kg. A two-degree-of-freedom vector nozzle mechanism attached to the engines allows precise thrust direction adjustments. Based on this propulsion system and the PAV's physical model, a cascade proportional-integral-derivative (PID) controller is developed to regulate PAV's position and attitude. Additionally, a feed-forward-based proportional-derivative (PD) controller is implemented to enhance the engine's thrust response. The PAV prototype underwent rigorous testing in various outdoor conditions, ranging from temperatures of −7°C to 42°C and wind speeds of 0 to 7.2 m/s. Experimental results show that the flight speed reached 14.65 m/s, with a flight duration exceeding 5 min. These results confirm the feasibility of the proposed PAV's design principles, demonstrating its adaptability to varying environmental conditions. While the primary focus of this paper is on the miniature PAV system, its findings contribute to the broader field of advanced air mobility research.