Granular grippers can manipulate a wide variety of objects, but need to be pressed on the object to conform to it. If the object is placed on unstable ground, e.g., on sand or water, this step might cause the object to sink or move away from the gripper, hindering proper operation. We introduce a granular gripper with an integrated suction cup, where suction and jamming are controlled independently. We demonstrate the system's robust and enhanced gripping capabilities by comparing its grasping performance with a typical granular gripper design. We show that the proposed device can grip objects that are challenging for typical granular grippers, including those placed on unstable ground, as the suction cup stabilizes the object, allowing the gripper to conform.

In modern naval warfare, it is crucial to achieve rapid and efficient attacks on enemy targets by unmanned surface vehicles (USVs). However, existing research on USV attack strategies is limited and often overlooks the requirement to allocate USVs effectively across regions of varying strategic value, which restricts performance in dynamic maritime combat environments. To this end, we proposed a novel task allocation and saturation attack strategy for USVs. First, we divided the areas according to the concentration of enemy USVs, and then reasonably allocated tasks according to the value of the enemy area and attack capability of our USVs. Then, our USVs sail towards the enemy area at a uniform angle and at the same time to form an encirclement and carry out precise saturation attacks. In task allocation, we introduce Logistic chaos mapping and differential evolution mechanism to improve the Grey Wolf Optimizer, thereby improving search efficiency and task allocation accuracy. In addition, we combine the optimal matching algorithm with the dynamic path control of Bezier curve to ensure the accuracy and flexibility of the coordinated saturation attack. The simulation experiment results show that the proposed approach exhibits high attack efficiency and practicality in different combat scenarios, and provides an effective solution for the attack missions of USVs in complex environments.

The coverage problem is relevant to numerous real-life applications such as agriculture, search and rescue, and demining. The primary objective of this problem is to cover as many positions as possible in an unknown environment. Utilizing multiple robots can significantly reduce the total time required for coverage while enhancing overall efficiency. In this paper, we introduce a novel Distributed Coverage Algorithm (DCA) utilizing multiple robots which is also scalable. This algorithm can be used in various real life situations like for agricultural field work, for search and rescue, etc. We formally prove termination, no overlap, correctness and time complexity of the DCA algorithm. We have simulated the DCA algorithm using the Webots multi-robot simulator and compared its performance with existing approaches. The simulation results reveal that the DCA algorithm significantly outperforms the existing approaches. DCA algorithm achieves a maximum coverage time reduction of 31.51% to 70.73% while also offering enhanced coverage efficiency in all environmental conditions.

With the development of industrial automation and intelligent logistics, the application of unmanned forklifts in warehousing and production environments has become increasingly widespread. However, the decline in positioning accuracy and the problem of drift seriously affect their stability and safety. Although existing LiDAR and inertial navigation systems have improved, they still face challenges related to cumulative errors during operation. This paper proposes a new algorithm called Aruco-LOAM, which significantly improves the positioning accuracy of unmanned forklifts by combining Aruco markers with LEGO-LOAM's LiDAR point cloud data and utilizing visual constraints during the graph optimization process. The research results indicate that this method suppresses cumulative errors and enhances the robustness of unmanned forklifts in complex environments, providing a more reliable navigation solution for warehousing systems.

Performing complex tasks in open environments remains challenging for robots, even when using large language models (LLMs) as the core planner. Many LLM-based planners are inefficient due to their large number of parameters and prone to inaccuracies because they operate in open-loop systems. We think the reason is that only applying LLMs as planners is insufficient. In this work, we propose DaDu-E, a robust closed-loop planning framework for embodied AI robots. Specifically, DaDu-E is equipped with a relatively lightweight LLM, a set of encapsulated robot skill instructions, a robust feedback system, and memory augmentation. Together, these components enable DaDu-E to (i) actively perceive and adapt to dynamic environments, (ii) optimize computational costs while maintaining high performance, and (iii) recover from execution failures using its memory and feedback mechanisms. Extensive experiments on real-world and simulated tasks show that DaDu-E achieves task success rates comparable to embodied AI robots with larger models as planners like COME-Robot, while reducing computational requirements by 6 .6×. Users are encouraged to explore our system at: https://rlc-lab.github.io/dadu-e/.

Transparent objects are difficult to perceive due to their unique optical properties, and the dynamic interaction between the hand and object further complicates pose estimation. To address this problem, we propose HTPE-Net, a monocular instance-level 6D pose estimation method for hand-held transparent objects, addressing the significant challenges posed by the texture-less, non-Lambertian surfaces, and hand-object occlusions. HTPE-Net integrates hand and object features through a dual-stream feature extraction backbone and a hand-to-object feature enhancement module, generating geometric features and hand attention maps to improve robustness to background changes and occlusions. The network is trained on a modified version of the TransHand-14K dataset and demonstrates superior performance compared to state-of-the-art methods. Additionally, a sim-to-real experiment validates the practical applicability of HTPE-Net in real-world robot perception tasks. The proposed approach significantly advances the accuracy and robustness of 6D pose estimation for hand-held transparent objects, with potential applications in robotics, human-machine interaction, and augmented reality.

Transparent liquid volume estimation is crucial for robot manipulation tasks, such as pouring. However, estimating the volume of transparent liquids is a challenging problem. Most existing methods primarily focus on data collection in the real world, and the sensors are fixed to the robot body for liquid volume estimation. These approaches limit both the timeliness of the research process and the flexibility of perception. In this paper, we present SimLiquid20k, a high-fidelity synthetic dataset for liquid volume estimation, and propose a YOLO-based multi-modal network trained on fully synthetic data for estimating the volume of transparent liquids. Extensive experiments demonstrate that our method can effectively transfer from simulation to the real world. In scenarios involving changes in background, viewpoint, and container variations, our approach achieves an average error of 5% in real-world volume estimation. In addition, our work conducts two application experiments integrate with ChatGPT, showcasing the potential of our method in service robotics. The accompanying video and supplementary materials are available at https://simliquid.github.io/.

Quadrupedal robots are highly regarded for their superior locomotion capabilities and terrain adaptability, making them competent in a wide range of applications. In autonomous navigation tasks, they are required to track upper-level trajectories to reach designated locations with flexible obstacle avoidance. This is typically achieved by a planner which generates a reference velocity and a controller which accurately tracks the velocity commands. In this article, we propose a learning-based controller for quadrupedal robots that achieves accurate and robust velocity tracking. To bridge the gap between simulation and reality, an analytical actuator model is proposed to simulate physical actuator dynamics. We then train a control policy in simulation using Constrained Reinforcement Learning, where symmetry and smoothness constraints are incorporated into reinforcement learning. The symmetry constraint promotes coordinated locomotion and consistent velocity tracking performance, while the smoothness constraint reduces jerky actions and generates stable velocity performance. The control policy is zero-shot deployed on the Unitree AlienGo. It demonstrates a tracking error of less than 0.084 m/s over the entire velocity range and robust locomotion on natural terrain. We also test our controller by integrating it to a pedestrian tracking framework and prove its capability of trajectory following and long-term reliablitiy.

This study introduces a compact, autonomous mobile weed management robot designed to promote sustainable agricultural practices and enhance crop protection through effective early-stage weed management. Equipped with a laser-based system, the robot enables precise weed removal tailored to specific agricultural contexts. It employs an AI-driven image classification approach for weed detection, achieving a mean average precision (mAP) of 0.32 and a detection rate of 118 ms on a Raspberry Pi 5 platform. The robot features a two-degree-of-freedom arm for accurate laser positioning, with exposure duration dynamically adjusted based on identified weed species to minimize energy consumption and protect neighboring crops and soil. Field trials in Vancouver, Canada, and Arusha, Tanzania, demonstrated the robot’s effectiveness, achieving weed removal success rates of 97% and 96%, respectively, in a maximum of 60 seconds targeting pigweed, purslane, and nutsedge. Designed to be cost-efficient and scalable, this innovative system offers an environmentally sustainable solution for effective weed management, significantly reducing herbicide use and enhancing weed targeting precision. This research underscores the dual benefits of integrating autonomous technology into agriculture, improving productivity and sustainability while protecting crop health and ecosystems.

With the aging population and labor shortage, the proportion of labor costs in the production costs of tomato harvesting is increasing, making the research and development of tomato harvesting robots urgent. This paper develops a cherry tomato harvesting robot, consisting of a mobile chassis, lifting platform, robotic arm, end effector, depth camera, and control system, to achieve automated walking navigation, harvesting, and basket loading. This paper outlines the system design and harvesting scenario of the tomato harvesting robot, details the design and implementation of the robot’s chassis and walking route planning, end effector design, tomato recognition and positioning, robotic arm motion planning, and control system. Furthermore, the architecture and control flow of the software system are elaborated. Finally, field tests validate the robot’s performance and harvesting success rate. Tests show that the overall accuracy and recall rates of the visual recognition model for tomato bunches are 85.04% and 88.71%, respectively, and for the stalks are 82.72% and 81.51%, respectively, with a harvesting success rate of 70.77%. These efforts provide beneficial exploration for the future commercial application of cherry tomato harvesting robots.

： The secure gripping of soft grippers remains a difficult challenge to address. To solve this challenge, the Single-axis deformation (SD) soft grippers were designed that were 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. In order to prove the SD soft grippers have a improving ability of secure gripping, The dexterity, gripping capacity, and radial load-bearing capacity of the SD soft grippers were tested through experiments. The experimental results shown that the opening and closing range of the SD soft gripper is 18mm-144mm, the maximum gripping load is 39.4N, and the radial bending angle is only 2.76 when the radial load is 24N.

Recently, there has been a surge of international interest in extraterrestrial exploration targeting the Moon, Mars, the moons of Mars, and various asteroids. This contribution discusses how current state-of-the-art Earth-based testing for designing rovers and landers for these missions currently leads to overly optimistic conclusions about the behavior of these devices upon deployment on the targeted celestial bodies. The key misconception is that gravitational offset is necessary during the terramechanics testing of rover and lander prototypes on Earth. The body of evidence supporting our argument is tied to a small number of studies conducted during parabolic flights and insights derived from newly revised scaling laws. We argue that what has prevented the community from fully diagnosing the problem at hand is the absence of effective physics-based models capable of simulating terramechanics under low gravity conditions. We developed such a physics-based simulator and utilized it to gauge the mobility of early prototypes of the Volatiles Investigating Polar Exploration Rover (VIPER). This contribution discusses the results generated by this simulator, how they correlate with physical test results from the NASA-Glenn SLOPE lab, and the fallacy of the gravitational offset in rover and lander testing. The simulator, which is open-sourced and publicly available, supports trafficability analysis and facilitates principled studies into in-situ resource utilization activities like digging, bulldozing, and berming in low gravity environments.

In this paper, we discuss the development and deployment of a robust autonomous system capable of performing various tasks in the maritime domain under unknown dynamic conditions. We investigate a data-driven approach based on modular design for ease of transfer of autonomy across different maritime surface vessel platforms. The data-driven approach alleviates issues related to a priori identification of system models that may become deficient under evolving system behaviors and/or shifting, unanticipated, environmental influences. Our proposed learning-based platform comprises a deep Koopman system model and a change point detector that provides guidance on domain shifts prompting relearning under severe exogenous and endogenous perturbations. Motion control of the autonomous system is achieved via an optimal controller design. The Koopman linearized model naturally lends itself to a linear–quadratic regulator (LQR) control design. We propose the C3D control architecture “Cascade Control with Change Point Detection and Deep Koopman Learning”. The framework is verified in station keeping tasks on an ASV in both simulation and real experiments. The approach demonstrated a consistent improvement in mean distance error across all test cases compared to the methods that do not consider system changes.

The nonlinear control problem of quadrotor UAVs which perform cooperative transportation of payloads is treated with the use of nonlinear optimal and multi-loop flatness-based control methods. The load is suspended with a link from a cart which is turn in connected through cables with two quadrotors. The aim is to compute the flight path and the control inputs of the quadrotors that will allow to lift the load and move it to any desirable final position. First, the dynamic model of the cable-suspended load is obtained through Euler-Lagrange analysis. Despite underactuation the associated nonlinear optimal control problem is solved, thus allowing to compute the lift forces of the cables that enable the load to move on the vertical plane until it reaches the targeted position. These forces are also applied with opposite sign to the quadrotors’ side through joints at the other end of the cables. Thus, the dynamic model of the quadrotors is updated by including in it additional drag forces which are due to the tension of the cables. The flight paths for the two quadrotors that enables to bring the suspended load to its final position are also computed. Next, for each quadrotor the nonlinear control and path following problem is solved, taking into account the cable-induced drag forces effects. To this end, a flatness-based control approach which is implemented in successive loops is applied to each quadrotor. The state-space model of each quadrotor UAV is separated into subsystems, which are connected between them in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input-output linearized flat systems. The state variables of the second subsystem become virtual control inputs for the first subsystem. In turn, exogenous control inputs are applied to the second subsystem. The whole control method is implemented in two successive loops and its global stability properties are also proven through Lyapunov stability analysis. The whole procedure is repeated at each sampling instance, that is (i) solution of the nonlinear optimal control problem for the transportation of the payload (ii) computation of the drag forces which are exerted on the UAVs due to lifting the load, (iii) solution of the multi-loop flatness-based control problem for the individual UAVs. This control method allows each quadrotor to follow precisely the defined flight path and finally achieves to bring the load to the targeted position.

In this paper, we present a method for the manipulation of objects in maritime environments, in particular for the transportation of objects between two docked ships using a 6-DoF robotic arm. The presented method uses RGBD camera mounted on the robot arm end effector to localize the object with respect to the robot arm base. The design of a gripper, and a control method based on force and torque measurements provide a robust method for picking up object, that can compensate the influence of relative ship motion due to waves and localization error. The components of the proposed method, as well as the entire manipulation procedure have been tested both in a laboratory environment and in a real-world maritime environments scenario and have shown that the solution works reliably.

There is great interest in alternative pollination strategies for crop production in the face of climate change and perennial threats to the traditional pollination mechanisms. This review explores the potential for robotic pollination in response to these challenges to crop fertilization and global food production. Herein we describe the viability of novel robotic systems equipped with artificial intelligence and machine vision, as alternatives to traditional insect pollination. We examine the technological progress and challenges for both aerial and ground-based robotic artificial pollination systems and emphasize the need for continued research and development in this area to ensure sustainable agricultural productivity. This paper highlights the importance of robotic pollination as a practical and environmentally sustainable approach in modern agriculture, amidst burgeoning ecological threats and a dwindling agricultural workforce.

Miniature personal aerial vehicles (PAVs) with vertical take-off and landing (VTOL) capabilities offer numerous advantages over existing vehicles, particularly in terms of high maneuverability, manned flight capability, and load-carrying capacity required during rescue missions. However, the detailed research work was reported infrequently. In this paper, a miniature PAV capable of piloted operation in both standing and sitting postures is introduced. This PAV, weighing 45kg and measuring 45cm*87cm*154cm, is designed for easy transport and minimal spatial footprint. It incorporates five vertically arranged micro turbojet engines that enable VTOL capabilities and can carry loads exceeding 100kg while maintaining high maneuverability. Notably, a two-degree-of-freedom nozzle mechanism attached to the engines allows for precise thrust direction adjustments. Building upon this propulsion system and the physical model of the PAV, a cascade proportional-integral-derivative (PID) controller is specifically designed for the PAV to regulate its 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℃ to 42℃ and wind speeds of 0 to 7.2m/s. The test results substantiate the feasibility of the proposed PAV’s working principles, demonstrating its adaptability to environmental fluctuations. While the focus of this paper lies on the miniature PAV system, its implications extend to broader applications within advanced air mobility research.

2D LiDAR simultaneous localization and mapping(SLAM) often encounters issues such as registration failures, cumulative errors, and low scan repeatability, which prevent the construction of consistent and accurate maps. To address these challenges and reduce the need for remapping, this study proposes an interactive solution for 2D LiDAR SLAM, commonly used in AGVs, by manually adding or modifying constraints. However, for large-scale environments, manually adding and correcting constraints can be highly labor-intensive and difficult. To overcome this, an automatic loop closure detection and edge enhancement strategy was developed. A multi-scale loop closure detection algorithm enables fast loop closure detection, while the edge enhancement strategy revisits the pose graph to reduce overall error. Additionally, a GUI application was developed to visualize the pose graph, edges, and node information, allowing for interactive batch optimization. Finally, several experiments were conducted to demonstrate the performance improvements achieved by the proposed method.