For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we upgrade traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for precise drone landings. To fully exploit thetemporal consistencyandspatial complementaritybetween these two modalities, we propose two innovative modules:(i)the Consistency-instructed Collaborative Tracking module, which further leverages the drone's physical knowledge of periodic micro-motions and structure for accurate measurements extraction, and(ii)the Graph-informed Adaptive Joint Optimization module, which integrates drone motion information for efficient sensor fusion and drone localization. Extensive experiments (30+ hours) demonstrate that mmE-Loc attains 0.083$m$localization accuracy and 5.12$ms$end-to-end latency, outperforming four state-of-the-art methods by over 48% and 62%, respectively.

This paper introduces EmbodiedAgent, a hierarchical framework for heterogeneous multi-robot control. EmbodiedAgent addresses critical limitations of hallucination in impractical tasks. Our approach integrates a next-action prediction paradigm with a structured memory system to decompose tasks into executable robot skills while dynamically validating actions against environmental constraints. We present Mul-tiPlan+, a dataset of more than 18,000 annotated planning instances spanning 100 scenarios, including a subset of impractical cases to mitigate hallucination. To evaluate performance, we propose the Robot Planning Assessment Schema (RPAS), combining automated metrics with LLM-aided expert grading. Experiments demonstrate EmbodiedAgent’s superiority over state-of-the-art models, achieving 71.85% RPAS score. Real-world validation in an office service task highlights its ability to coordinate heterogeneous robots for long-horizon objectives.

Ultrasound-guided therapeutic procedures rely heavily on operator skill, leading to variability and high training costs. The shortage of trained ultra-sonographers further exacerbates the issue, increasing workloads and associated health risks. Robotic technology has the potential to effectively tackle these issues, yet there has been limited research on fully autonomous robotic ultrasound-guided biopsy systems based on the entire workflow. To address this challenge, this paper presents an autonomous robotic operative framework for superficial organ biopsy. The system integrates real-time slice-to-volume registration and navigation, along with a needle insertion mechanism, following operational protocols to autonomously perform the entire biopsy procedure. The feasibility, robustness, and generalizability of the system are validated through experimental studies.

A solar tracker significantly increases the amount of energy harvested by a floating photovoltaic system by adjusting the pose of its photovoltaic (PV) panels to optimize their exposure to the solar rays. Conventional solar trackers perform such an adjustment using actuators, which is energy-consuming and involves complex structures. In this paper, a novel dual-axis wave-driven solar tracker is proposed where the photovoltaic (PV) panel is adjusted by the inertia force and gravity. Actuators are replaced by brakes to fix the pose of the PV panel. The kinematics and dynamics of the system are investigated, which are further used for the state feedback and the formulation of the control strategy. A sliding-mode observer is used to estimate the effect of winds on the system, which enables the robustness of the system to be enhanced. A motion planning strategy is also developed to track the solar position and minimize the movements of two joints. Indoor experiments are conducted to test the accuracy of the state feedback and dynamic model. The performance of the uncertainty observer is also tested by experiments. At last, field experiments on the real water surface are implemented to verify the feasibility of the proposed system. The results show that the solar tracker is able to adjust the PV panels at a velocity of 8.89 deg/s under the effect of limited base motions with amplitude less than 4°. Note to Practitioners—Traditional solar trackers can be driven passively by chemical energy or actively using actuators. The former cannot function stably in the field environment, and the latter is complex in structure and energy-consuming. For the FPV system under the effect of waves, the solar tracker can utilize the motion of the base to align its PV panel to the solar position. Thus, we present a wave-driven solar tracker without using actuators, and brakes are applied to lock the position of joints. The proposed solar tracker is energy-efficient, has a simpler structure and hardware than the active one, and is more robust than the passive one. We model the system and propose a control scheme to drive the PV panel using inertia and gravity. We also observe the effect of winds to enhance the system robustness. The motion planning strategy is investigated to minimize the movements of the joints. The impact of base motion and control period on the energy-harvesting efficiency is analyzed by simulations. Both indoor and field experiments are implemented to verify the feasibility of the system. The experimental results show that the system performs well even when the base shacking is less than 4°. Note that the control algorithm is a model-based algorithm, which means the model parameters (inertia, gravity, etc.) need to be reidentified for different PV panels and system properties. That is a challenge for large-scale deployment with different configurations. Future research will address the system identification problem and enable the model parameters to be updated automatically.

In this paper, a new capture system for UAV precision landing in a disturbed environment is proposed. Compared with the traditional visual guided landing methods, perching mechanism based methods, and tethered landing methods, the proposed system takes into account the stability during landing process and retains the high accessibility of the UAV. The proposed system consists of a winch subsystem and a magnetic catcher device. They establish an automatic tethered-UAV system for landing before the UAV touchdown. We analyzed the design principle as well as the feasibility of the magnetic catcher. An optimization problem is formulated to obtain a better layout of magnets on the catcher. The problem is relaxed based on interpolation simulation of attraction force. Experiments are conducted both in indoor and outdoor environments based on different UAV platforms respectively. The results validate that the catcher design and the capture system can achieve a successful landing in both cases.

This paper introduces an intelligent system which composes music following the users' instructions. Current auto-matic music generation models are lack of stability. Meanwhile, they cannot satisfy the preference of different people. To overcome these challenges, we train a Transformer-based neural network to generate short music segments using a dataset. A user can compose music pieces by interacting with a well-trained generator. Our system collects the user's feedback during the interactions, and fine-tunes the neural network to optimize the generator. After a large number of interactions, our system can learn the musical taste of the user and customize a personal automatic music composer for him or her. Our work enhances the application value of generative models significantly, which enables people to compose music with the assistance of artificial intelligence.

Obstacle avoidance is crucial for autonomous surface vehicles (ASVs) in the sea because rescue is extremely difficult there. OceanVoy, a sailboat toward long range energy-saving voyage, has to overcome the dual challenges, i.e. from the environmental interference and its low mobility preventing from precise obstacle avoidance. We propose a control scheme based on real-time collision risk assessment and a hybrid propulsion system to enhance safety of OceanVoy. A novel sailboat safety zone (SSZ) has been designed to warn the potential collision during its sailing. Both intrinsic characteristics of OceanVoy and environmental factors have been considered in SSZ. We use lateral and axial thrusters to provide emergency propulsion. A collision avoidance algorithm is executed to coordinate motors in rudder, sail and thrusters based on SSZ. Both simulation and experiments have been conducted and the results have validated our system and collision avoidance scheme.

Self-assembly has attracted growing interests in modular robotics during past decades. Recent work accelerates the assembly process by parallelizing the docking actions among robots. However, these methods can only apply to ideal environments without obstacles. Otherwise, robots will get trapped during the assembly process, due to the complex scenes with obstacles. This paper presents an efficient parallel assembly planning algorithm for modular robots by taking the surrounding obstacles into consideration. By this algorithm, the docking actions are able to avoid immovable obstacles, and therefore parallel self-assembly of robots can adapt to complex environments. To validate the efficacy and generality, the authors have implemented this algorithm in a grid-world simulation environment with 25 distinct maps. The simulation results show a much higher success rate (more than 80%) of our proposed algorithm compared with the existing parallel self-assembly planning algorithms. Finally, the feasibility of the algorithm is affirmed by a self-assembly experiment on the automated guided vehicles (AGVs).

In the process of landing unmanned aerial vehicles (UAVs) on an unmanned surface vehicle (USV), a manipulator can be applied to help the UAV land safely and accurately. However, it is a challenge to control the manipulator on a disturbed USV due to joint velocity constraints and bandwidth limitations. To solve this problem, a predictive control framework is proposed in this paper. We leverage a first-order delay system to describe the kinematics of each joint, and control joint velocities by the model predictive controller (MPC). To generate references for MPC, the motion of the floating base needs to be predicted. We apply the recent approach for motion prediction based on the wavelet network (WN) and modify the network to get smooth trajectories. The accuracy of the modified wavelet network (MWN) for motion prediction is tested on four-hour motion data from the real ocean environment and the smoothness of the generated trajectories is also evaluated. Simulations and experiments are implemented to verify the proposed method, the results show that the average control accuracies are improved by more than 30% and 50% in position and rotation compared with the traditional inverse kinematics (IK) controller for 1 Hz base fluctuation.