Journal of Field Robotics最新文献

This paper fuses ideas from reinforcement learning (RL), Learning from Demonstration (LfD), and Ensemble Learning into a single paradigm. Knowledge from a mixture of control algorithms (experts) are used to constrain the action space of the agent, enabling faster RL refining of a control policy, by avoiding unnecessary explorative actions. Domain-specific knowledge of each expert is exploited. However, the resulting policy is robust against errors of individual experts, since it is refined by a RL reward function without copying any particular demonstration. Our method has the potential to supplement existing RLfD methods when multiple algorithmic approaches are available to function as experts, specifically in tasks involving continuous action spaces. We illustrate our method in the context of a visual servoing (VS) task, in which a 7-DoF robot arm is controlled to maintain a desired pose relative to a target object. We explore four methods for bounding the actions of the RL agent during training. These methods include using a hypercube and convex hull with modified loss functions, ignoring actions outside the convex hull, and projecting actions onto the convex hull. We compare the training progress of each method using expert demonstrators, employing one expert demonstrator with the DAgger algorithm, and without using any demonstrators. Our experiments show that using the convex hull with a modified loss function not only accelerates learning but also provides the most optimal solution compared with other approaches. Furthermore, we demonstrate faster VS error convergence while maintaining higher manipulability of the arm, compared with classical image-based VS, position-based VS, and hybrid-decoupled VS.

Learning-based visual odometry (VO) becomes popular as it achieves a remarkable performance without manually crafted image processing and burdensome calibration. Meanwhile, the inertial navigation can provide a localization solution to assist VO when the VO produces poor state estimation under challenging visual conditions. Therefore, the combination of learning-based technique and classical state estimation method can further improve the performance of pose estimation. In this paper, we propose a learning-based visual-inertial odometry (VIO) algorithm, which consists of an end-to-end VO network and an $��\; ��S�\; �E_2�\; ��\left(�\; �3�\; �\right)���$-Extended Kalman Filter (EKF). The VO network mainly combines a convolutional neural network with a recurrent neural network, taking advantage of two consecutive monocular images to produce relative pose estimation with associated uncertainties. The $��\; ��S�\; �E_2�\; ��\left(�\; �3�\; �\right)���$-EKF, which is proved to overcome the inconsistency issues of VIO, propagates inertial measurement unit kinematics-based states, and fuses relative measurements and uncertainties from the VO network in its update step. The extensive experimental results on the KITTI data set and the EuRoC data set demonstrate the superior performance of the proposed method compared to other related methods.

This paper presents the design and development of a funnel-shaped Sparus Docking Station intended for the non-holonomic torpedo-shaped Sparus II Autonomous Underwater Vehicle. The Sparus Docking Station is equipped with sensors and batteries, allowing for a stand-alone long-term deployment of the vehicle. An inverted Ultra Short Base-Line system is used to locate the Docking Station as well as to provide long-term drift-less vehicle navigation. The Sparus Docking Station is able to observe the ocean currents using a Doppler Velocity Log, being motorized to allow its self-alignment with the current. Moreover, a docking algorithm accounting for the current is used to guide the robot during the docking maneuver. The paper reports consecutive successful experimental results of the docking maneuver in sea trials in two different countries.

Plastering is dominated manually, exhibiting low levels of automation and inconsistent finished quality. A comprehensive review of literature indicates that extant plastering robots demonstrate a subpar performance when tasked with rectifying defects in the transition area. The limitations encompass a lack of capacity to independently evaluate the quality of work or perform remedial plastering procedures. To address this issue, this research describes the system design of the Puttybot and a paradigm of plastering to solve the stated problems. The Puttybot consists of a mobile chassis, a lift platform, and a macro/micromanipulator. The force-controlled scraper parameters have been calibrated to dynamically modify their rigidity in response to the applied putty. This strategy utilizes convolutional neural networks to identify plastering defects and executes the plastering operation with force feedback. This paradigm's effectiveness was validated during an autonomous plastering trial wherein a large-scale wall was processed without human involvement.

Heavy-duty hexapod robots have impressive stability, high load-bearing capacity, and exceptional adaptability to rugged terrains. They are capable of working in challenging outdoor environments such as planetary exploration, disaster relief and mountain transportation. Their ability to traverse terrain requires effective motion planning and accurate force distribution, neither of which is currently at the level required for widespread practical applications. In this paper, the mechanical legs are divided into support and swing legs, and the adaptability of the hexapod robot to unknown rugged terrain is enhanced by introducing the Decomposition Quadratic Programming-based Contact Force Distribution (DQP-based CFD) method. Moreover, an efficient replanning strategy can handle accidental collisions between swinging legs and unmodelled obstacles. Extensive field experiments demonstrate the effectiveness of our proposed motion planning and contact force distribution methods.

Driving in an off-road environment is challenging for autonomous vehicles due to the complex and varied terrain. To ensure stable and efficient travel, the vehicle requires consideration and balancing of environmental factors, such as undulations, roughness, and obstacles, to generate optimal trajectories that can adapt to changing scenarios. However, traditional motion planners often utilize a fixed cost function for trajectory optimization, making it difficult to adapt to different driving strategies in challenging irregular terrains and uncommon scenarios. To address these issues, we propose an adaptive motion planner based on human-like cognition and cost evaluation for off-road driving. First, we construct a multilayer map describing different features of off-road terrains, including terrain elevation, roughness, obstacle, and artificial potential field map. Subsequently, we employ a convolutional neural network-long short-term memory network to learn the trajectories planned by human drivers in various off-road scenarios. Then, based on human-like generated trajectories in different environments, we design a primitive-based trajectory planner that aims to mimic human trajectories and cost weight selection, generating trajectories that are consistent with the dynamics of off-road vehicles. Finally, we compute optimal cost weights and select and extend behavioral primitives to generate highly adaptive, stable, and efficient trajectories. We validate the effectiveness of the proposed method through experiments in a desert off-road environment with complex terrain and varying road conditions. The experimental results show that the proposed human-like motion planner has excellent adaptability to different off-road conditions. It shows real-time operation, greater stability, and more human-like planning ability in diverse and challenging scenarios.

High-speed underwater wireless optical communication holds immense promise in ocean monitoring and surveys, providing crucial support for the real-time sharing of observational data collected by autonomous underwater vehicles (AUVs). However, due to inaccurate target information and external interference in unknown environments, link alignment is challenging and needs to be addressed. In response to these challenges, we propose a reinforcement learning-based alignment method to control the AUV to establish an optical link and maintain alignment. Our alignment control system utilizes a combination of sensors, including a depth sensor, Doppler velocity log (DVL), gyroscope, ultra-short baseline device, and acoustic modem. These sensors are used in conjunction with a particle filter to observe the environment and estimate the AUV's state accurately. The soft actor-critic algorithm is used to train a reinforcement learning-based controller in a simulated environment to reduce pointing errors and energy consumption in alignment. After experimental validation in simulation, we deployed the controller on an actual AUV called Tri-TON. In experiments at sea, Tri-TON maintained the link and angular pointing errors within 1 m and $��\; ��1�\; �0^{\circ }��$, respectively. Experimental results demonstrate that the proposed alignment control method can establish underwater optical communication between AUV fleets, thus improving the efficiency of marine surveys.

This paper investigates four different mobile robots with respect to their driving characteristics and soil preservation properties in an agricultural environment. Thereby, robots of classical design from agriculture as well as systems from space robotics with advanced locomotion concepts are considered to determine the individual advantages of each rover concept with respect to the application domain. Locomotion experiments were conducted to analyze the general driving behavior, tensile force, and obstacle-surmounting capability and ground interaction of each robot. Various soil conditions typical for the area of application are taken into account, which are varied in terms of moisture and density. The presented work covers the specification of the conducted experiments, documentation of the implementation as well as analysis and evaluation of the collected data. In the evaluation, particular attention is paid to the change in driving characteristics under different soil conditions, as well as to the soil stress caused by driving, since soil quality is of critical importance for agricultural applications. The analysis shows that the advanced locomotion concepts, as used in space robotics, also have positive implications for certain requirements in agricultural applications, such as maneuverability in wet conditions and soil conservation. The results show potential for design innovations in agricultural robotics that can be used, to open up new fields of application for instance in the context of precision farming.

Image dehazing and object detection are two different research areas that play a vital role in machine learning. When merged together and implemented in real-time, it is a boon in the field of artificial intelligence, specifically robotics. Object detection and tracking are two of the major implementations in almost the entire robot's training and learning. The learning of the robot depends on the images; these images can be camera-captured images or a pretrained data set. Real-time outdoor images clicked in bad weather conditions, such as mist, haze, smog, and fog, often suffer from poor visibility, and the consequences are incorrect results and hence an unexpected robot's behavior. To overcome these consequences, we have presented a novel approach to object detection and identification during adverse weather conditions. This method is proposed to be implemented in a real-time environment to monitor animal behavior near railway tracks during fog, haze, and smog. This is not limited to specific application areas but can be used to identify endangered species and take active steps to save them from mishap. The deployment is done in a real-time indoor environment using Tortoisebot mobile robot with a robot operating system framework.