Journal of Field Robotics最新文献

Similar to other underwater robots, bionic robotic fish face entrapment risks when stranded due to wave action or water level drop. In this paper, we propose a locomotion strategy for a whale shark-inspired robotic fish, enabling it to autonomously return to aquatic environments after being stranded on land. This strategy is informed by the terrestrial locomotion capabilities of mudskippers and is particularly significant given the considerable mass of such robotic fish, which compounds the difficulty of land-based movement. First, we introduce a lightweight YOLOv5 model-based algorithm for deep-water area recognition, which identifies the direction for the bionic robot fish to re-enter the water. Subsequently, pectoral fin-based crawling gaits are designed by the innate two degrees of freedom within the existing pectoral fin structure of the robot. These gaits empower the robotic fish to move on a multitude of terrestrial terrains. Extended field experiments have validated the effectiveness of our water recognition algorithm and locomotion strategy, confirming the ability of the whale shark-inspired robotic fish to perform successful water entry maneuvers from the shore. Additionally, the capability to traverse various landforms are also verified. This work provides valuable insights into self-rescue mechanisms for stranding underwater robots and promotes practical applications of bionic robotics.

This paper investigates the cooperative trajectory tracking (CTT) control problem of multiple autonomous underwater vehicles (AUVs). The multi-AUV system is characterized by uncertain dynamics, being subjected to the impact about input saturation constraints and unmeasurable disturbances. First, a neural network-based data-driven control algorithm is proposed for the multi-AUV system with unmeasurable disturbances and model parameters uncertain. The radial basis function neural network is employed to estimate the primary pseudo parameters of an equivalent data model, established through dynamic linearization methods. Subsequently, an iterative learning control approach based on adaptive gain is designed to act as a feedforward scheme along the iteration axis to enhance the tracking accuracy within a time constraint. Third, to prove that the resulting CTT control system fulfills the bounded stability under the proposed control approach, a formal stability analysis is provided. Finally, a simulation case study is conducted to illustrate the effectiveness of the proposed CTT control approach.

On-orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on-orbit assembly, repair, and astronaut assistance. Of these scenarios, on-orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on-orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free-flying “assembler” robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on-orbit assembly and other scenarios require collision-avoiding motion planning, particularly when operating in a “construction site” scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long-horizon planning, accomplished using offline and online sampling-based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information-aware planning framework for on-orbit reconfiguration model learning; and (3) connection with robust control tools to provide low-level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on-orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free-flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free-flyers will need to consider.

This paper focuses on the Light Detection and Ranging (LiDAR)–Inertial Measurement Unit (IMU) simultaneous localization and mapping (SLAM) problem: How to fuse the sensor measurement from the LiDAR and IMU to online estimate robot's poses and build a consistent map of the environment. This paper presents LTA-OM: an efficient, robust, and accurate LiDAR SLAM system. Employing fast direct LiDAR-inertial odometry (FAST-LIO2) and Stable Triangle Descriptor as LiDAR–IMU odometry and the loop detection method, respectively, LTA-OM is implemented to be functionally complete, including loop detection and correction, false-positive loop closure rejection, long-term association (LTA) mapping, and multisession localization and mapping. One novelty of this paper is the real-time LTA mapping, which exploits the direct scan-to-map registration of FAST-LIO2 and employs the corrected history map to provide direct global constraints to the LIO mapping process. LTA mapping also has the notable advantage of achieving drift-free odometry at revisit places. Besides, a multisession mode is designed to allow the user to store the current session's results, including the corrected map points, optimized odometry, and descriptor database for future sessions. The benefits of this mode are additional accuracy improvement and consistent map stitching, which is helpful for life-long mapping. Furthermore, LTA-OM has valuable features for robot control and path planning, including high-frequency and real-time odometry, driftless odometry at revisit places, and fast loop closing convergence. LTA-OM is versatile as it is applicable to both multiline spinning and solid-state LiDARs, mobile robots and handheld platforms. In experiments, we exhaustively benchmark LTA-OM and other state-of-the-art LiDAR systems with 18 data sequences. The results show that LTA-OM steadily outperforms other systems regarding trajectory accuracy, map consistency, and time consumption. The robustness of LTA-OM is validated in a challenging scene—a multilevel building having similar structures at different levels. To demonstrate our system, we created a video which can be found on https://youtu.be/DVwppEKlKps.

While marine animal behavior is often studied in constrained lab setups, a more reliable exploration should be done in their natural environment and without human interference. This task becomes excessively more challenging when quantitative data are needed in large and unconstrained aquatic environments. Toward that end, researchers widely use acoustic positioning telemetry to remotely track their subjects, though this often requires an extensive network of receivers placed in the environment ahead of time. This study proposes a new tracking method that continuously tracks and reports the trajectory of a target in unconstrained marine environments using a single-moving acoustic receiver. Instead of deploying an extensive array of static receivers, we use a single receiver mounted on an autonomous surface vehicle to obtain highly accurate results with much cheaper and simpler means. The receiver position and earlier target location estimations are used to calculate an optimal trajectory for the receiver, which in turn provides subsequent readings and target localizations based on a new variant of the Time Difference of Arrival approach. We demonstrate the performance of the proposed methods using both simulations and field experiments.

The nuclear energy sector can benefit from mobile robots for remote inspection and handling, reducing human exposure to radiation. Advances in cyber–physical systems have improved robotic platforms in this sector through digital twin (DT) technology. DTs enhance situational awareness for robot operators, crucial for safety in the nuclear energy sector, and their value is anticipated to increase with the growing complexity of cyber–physical systems. The primary motivation of this work is to rapidly develop and evaluate a robot fleet interface that accounts for these benefits in the context of nuclear environments. Here, we introduce a multimodal immersive DT platform for cyber–physical robot fleets based on the ROS-Unity 3D framework. The system design enables fleet monitoring and management by integrating building information models, mission parameters, robot sensor data, and multimodal user interaction through traditional and virtual reality interfaces. A modified heuristic evaluation approach, which accounts for the positive and negative aspects of the interface, was introduced to accelerate the iterative design process of our DT platform. Robot operators from leading nuclear research institutions (Sellafield Ltd. and the Japan Atomic Energy Agency) performed a simulated robot inspection mission while providing valuable insights into the design elements of the cyber–physical system. The three usability themes that emerged and inspired our design recommendations for future developers include increasing the interface's flexibility, considering each robot's individuality, and adapting the platform to expand sensor visualization capabilities.

This paper presents the development and implementation of a multiple unmanned aerial vehicle system focused on coverage path planning on multiple separated areas capable of re-planning the collective mission in case of unexpected events. For this purpose, we present a distributed-centralized architecture that uses heuristic and computationally efficient methods to perform the planning/re-planning and decision-making tasks during the control of the mission execution. We performed a computational evaluation of the algorithms, comparing them with other proposals, together with experiments in simulated and real flights. The results show that the system can distribute tasks equitably among the aircraft in an efficient way, even in the middle of the flight, when facing unexpected events; and show a higher computational efficiency when compared to multiple proposals in the state of the art.

It is important to obtain real-time leaf density of fruit-tree canopies for the precision spray control of plant-protection robots. However, conventional detection techniques for the characteristics of fruit-tree canopies cannot acquire the canopy internal information, which may provide an unsatisfactory accuracy of detection of leaf densities. This paper proposes a method for estimating canopy leaf density of fruit trees based on wind-excited audio. A wind-exciting implement was used to force fruit-tree canopy leaves vibrating to produce audio. Then, some correlation analysis methods were used to extract key characteristic parameters of wind-excited audio that were significantly correlated with leaf density. Finally, based on the data set of wind-excited audio, a few machine-learning methods were used to develop leaf-density estimation models. Test results showed that: (1) there were five key feature parameters of wind-excited audio that were significantly correlated with leaf density: the short-time energy, spectral centroid, the frequency average energy, the peak frequency, and the standard deviation of frequency. (2) the estimation model of leaf density developed based on backpropagation neural network for fruit-tree canopy showed the optimal estimation results, which can achieve the estimation of leaf density of fruit-tree canopies accurately. The overall correlation coefficient (R) of the estimation model was more than 0.84, the root-mean-square error was less than 0.73 m² m⁻³, and the mean absolute error was less than 0.53 m² m⁻³. This study is expected to provide a technical solution for the leaf-density detection of fruit-tree canopies of plant-protection robots.