Frontiers in Robotics and AI最新文献

In recent years, several studies that analyze and interpret fish behavioral patterns in aquaculture settings have been published. Understanding how the fish react and respond to various scenarios and treatments can help provide insight and knowledge on how to achieve sustainable and efficient aquaculture production. Many of these research efforts have been conducted in land based tanks as this allows for closer and more continuous monitoring of the fish than what is possible at commercial facilities, essentially improving data quality and hence the possible insights to gain from these. However, most experimental tanks are closed-loop environments that are not particularly similar to commercial production units, as a consequence the results obtained in these systems are not directly transferable to industrial setups. Moreover, tank monitoring in such trials is often done using a single or a limited selection of different observation modes, which may not be sufficient to capture the full dynamics of fish responses. The present study seeks to address these challenges by developing the Cyber-Enhanced tank environment for aquaculture research. This concept features a tank environment setup to simulate the prevailing conditions in aquaculture units, mimicking natural light conditions, hiding sensors and other systems to reduce impacts on the fish and potential collisions, and using a tank color known to stimulate positive welfare in farmed fish. The tank was equipped with a novel sensor suite for high-fidelity detection and monitoring of fish behaviors based on a combination of an event camera, a scanning imaging sonar and conventional cameras. This innovative concept represents a step towards conducting experimental setups that are both more realistic in that conditions resemble those in commercial facilities and that uses a multi-modal sensor approach to capture details in fish responses in behaviors. The setup will be used as a basis for future fish responses experiments monitoring experiments in intensive aquaculture tanks.

Introduction: Socially assistive robots (SARs) have shown promise in pediatric healthcare by helping children manage the stress and anxiety associated with medical procedures. However, limited research exists on the specific robot behaviors that are most effective in reducing negative emotions in children during stressful interventions. This study aimed to compare the effectiveness of two emotional support strategies provided by a SAR during a vaccination event: internal emotion regulation through a guided breathing exercise and external emotion regulation via motivational speech and physical comfort (hugging). Additionally, we compared the effects of active and passive participation in the two SAR interventions.

Methods: A field study was conducted during annual group vaccination days, involving 225 children aged 8-12 years. Emotional and behavioral outcomes, including anxiety, fear, trust, and willingness to engage with the robot, were measured using self-report questionnaires.

Results: Results indicated that while girls reported higher levels of fear and anxiety than boys, active participation in the SAR intervention led to greater reductions in fear and anxiety, particularly among girls. Additionally, active hugging enhanced both engagement and trust, with girls showing a stronger response to such a physical comfort intervention.

Discussion: These findings indicate that, within the constraints of this study, SAR interventions were associated with reduced negative emotions in children during vaccinations, with active participation and physical comfort being particularly impactful for emotional support. This study offers valuable insights into optimizing SAR interventions in pediatric healthcare.

Embodied intelligent systems build upon the foundations of behavioral robotics and classical cognitive architectures. They integrate multimodal perception, world modeling, and adaptive control to support closed-loop interaction in dynamic and uncertain environments. Recent breakthroughs in Multimodal Large Models (MLMs) and World Models (WMs) are profoundly transforming this field, providing the tools to achieve its long-envisioned capabilities of semantic understanding and robust generalization. Targeting the central challenge of how modern MLMs and WMs jointly advance embodied intelligence, this review provides a comprehensive overview across key dimensions, including multimodal perception, cross-modal alignment, adaptive decision-making, and Sim-to-Real transfer. Furthermore, we systematize these components into a three-stage theoretical framework termed "Dynamic Perception-Task Adaptation (DP-TA)". This framework integrates multimodal perception modeling, causally driven world state prediction, and semantically guided strategy optimization, establishing a comprehensive "perception-modeling-decision" loop. To support this, we introduce a "Feature-Conditioned Modal Alignment (F-CMA)" mechanism to enhance cross-modal fusion under task constraints.

In macro world, the robots are programmed machines, engineered to perform repetitive and often specialized tasks. Scaling down the size of a robot by a billionth of a meter gives a nano robot. The primary driving force behind the advent of micro and nano robots have always been the domain of medical technology and these robots are popularly known as nano bio-robots. The current review shines a light on these bio-robots in all their facets, encompassing both top-down and bottom-up fabrication approaches to their associated challenges followed by ethical approvals. The study describes in detail the synthesis techniques of nano bio-robot along with required actuation mechanism of the bio-robots. Further, in this paper, how a nano biorobotic drug-delivery system (NDDS) can deliver the drugs in a controlled way to the targeted site of the host in contrast to conventional drug administration is discussed. The paper also reviews and summarizes the administration pathways of these bio-robots in the human body and their efficacy in reducing various disorders. Overall, it can be said that the integration of nano robots with bio-concept presents distinct advantages and possesses significant promises for many applications.

As part of the robotics technologies required for In-situ resource utilization (ISRU), the development of cargo rovers for transporting resources is needed. However, these cargo rovers have unique technical challenges that differ from conventional exploration rovers, including the need to traverse rough terrains with their varying mass due to transporting payloads. Moreover, research addressing these challenges has been limited, and the relevant technologies have not been fully established. To address these challenges, this paper proposes a parametric model for estimating wheel slippage. The model is formulated as a function of four input parameters: slope angle, rover heading angle, payload mass, and wheel angular velocity, and is applicable to resource-transporting rovers with varying mass. Additionally, the use of a parametric model reduces computational load, which offers advantages for onboard implementation. The proposed estimation model was quantitatively evaluated by comparing datasets obtained from multi-body dynamics analysis. This paper also introduces a new traversability assessment model which incorporates the proposed slip estimation model. We demonstrated the proposed model by integrating it into a sampling based motion planning. The simulation result of the motion planning show that the planner with our model can generate safer motions and enables the rover to reach the target regardless of the cargo payload.

Robotic surfaces consisting of many actuators can change shape to perform tasks, such as object transportation and sorting. Increasing the number of actuators can enhance the robot's capacity, but controlling a large number of actuators is a challenging problem that includes issues such as the increased system-wide refresh time. We propose a novel control method that has constant refresh times, no matter how many actuators are in the robot. Having a distributed nature, the method first approximates target shapes, then broadcasts the approximation coefficients to the actuators and relies on itself to compute the inputs. To confirm the system size-independent scaling, we build a robot surface and measure the refresh time as a function of the number of actuators. We also perform experiments to approximate target shapes, and a good agreement between the experiments and theoretical predictions is achieved. Our method is more efficient because it requires fewer control messages to coordinate robot surfaces with the same accuracy. We also present a modeling strategy for the complex robot-object interaction force based on our control method and derive a feedback controller for object transportation tasks. This feedback controller is further tested by object transportation experiments, and the results demonstrate the validity of the model and the controller.

Current industrial robots deployed in small and medium-sized businesses (SMEs) are too complex, expensive, or dependent on external computing resources. In order to bridge this gap, we introduce an autonomous logistics robot that combines adaptive control and visual perception on a small edge computing platform. The NVIDIA Jetson Nano was equipped with a modified ResNet-18 model that allowed it to concurrently execute three tasks: object-handling zone recognition, obstacle detection, and path tracking. A lightweight rack-and-pinion mechanism enables payload lifting of up to 2 kg without external assistance. Experimental evaluation in semi-structured warehouse settings demonstrated a path tracking accuracy of 92%, obstacle avoidance success of 88%, and object handling success of 90%, with a maximum perception-to-action latency of 150 m. The system maintains stable operation for up to 3 hours on a single charge. Unlike other approaches that focus on single functions or require cloud support, our design integrates navigation, perception, and mechanical handling into a low-power, standalone solution. This highlights its potential as a practical and cost-effective automation platform for SMEs.

As service robots become increasingly integrated into public spaces, effective communication between robots and humans is essential. Elevators, being common shared spaces, present unique challenges and opportunities for such interactions. In this study, we developed a Human-Facility Interaction (HFI) system to facilitate communication between service robots and passengers in elevator environments. The system provided both verbal (voice announcements) and non-verbal (light signals) information to passengers waiting for an elevator alongside a service robot. We installed the system in a hotel and conducted two experiments involving 31 participants to evaluate its impact on passengers' impressions of the elevator and the robot. Our findings revealed that voice-based information significantly improved passengers' impressions and reduced perceived waiting time. However, light-based information had minimal impact on impressions and unexpectedly increased perceived waiting time. These results offer valuable insights for designing future HFI systems to support the integration of service robots in buildings.

Autonomous driving has the potential to enhance driving comfort and accessibility, reduce accidents, and improve road safety, with vision sensors playing a key role in enabling vehicle autonomy. Among existing sensors, event-based cameras offer advantages such as a high dynamic range, low power consumption, and enhanced motion detection capabilities compared to traditional frame-based cameras. However, their sparse and asynchronous data present unique processing challenges that require specialized algorithms and hardware. While some models originally developed for frame-based inputs have been adapted to handle event data, they often fail to fully exploit the distinct properties of this novel data format, primarily due to its fundamental structural differences. As a result, new algorithms, including neuromorphic, have been developed specifically for event data. Many of these models are still in the early stages and often lack the maturity and accuracy of traditional approaches. This survey paper focuses on end-to-end event-based object detection for autonomous driving, covering key aspects such as sensing and processing hardware designs, datasets, and algorithms, including dense, spiking, and graph-based neural networks, along with relevant encoding and pre-processing techniques. In addition, this work highlights the shortcomings in the evaluation practices to ensure fair and meaningful comparisons across different event data processing approaches and hardware platforms. Within the scope of this survey, system-level throughput was evaluated from raw event data to model output on an RTX 4090 24GB GPU for several state-of-the-art models using the GEN1 and 1MP datasets. The study also includes a discussion and outlines potential directions for future research.

Cleaning PV (photovoltaic) panels is essential for a PV station, as dirt or dust reduces the effective irradiation of solar energy and weakens the efficiency of converting solar energy into free electrons. The inconsistent (cleaning efficacy) and unsafe (summarized voltage and current) manual method is a challenge for a PV station. Therefore, this paper develops a cleaning robot with PV detection, path planning, and action control. Firstly, a lightweight Mobile-VIT (Mobile Vision Transformer) model with a Self-Attention mechanism was used to improve YOLOv8 (You Only Look Once v8), resulting in an accuracy of 91.08% and a processing speed of 215 fps (frames per second). Secondly, an A* and a DWA (Dynamic Window Approach) path planning algorithm were improved. The simulation result shows that the time consumption decreased from 1.19 to 0.66 s and the Turn Number decreased from 23 to 10 p (places). Finally, the robot was evaluated and calibrated in both indoor and outdoor environments. The results showed that the algorithm can successfully clean PV arrays without manual control, with the rate increasing by 23% after its implementation. This study supports the maintenance of PV stations and serves as a reference for technical applications of deep learning, computer vision, and robot navigation.