Synthetic micromotor has gained substantial attention in biomedicine and environmental ***-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fuel-free propul...
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Synthetic micromotor has gained substantial attention in biomedicine and environmental ***-based degradable micromotor composed of magnesium(Mg),zinc(Zn),and iron(Fe)have promise due to their nontoxic fuel-free propulsion,favorable biocompatibility,and safe excretion of degradation products Recent advances in degradable metallic micromotor have shown their fast movement in complex biological media,efficient cargo delivery and favorable biocompatibility.A noteworthy number of degradable metal-based micromotors employ bubble propulsion,utilizing water as fuel to generate hydrogen *** novel feature has projected degradable metallic micromotors for active in vivo drug delivery *** addition,understanding the degradation mechanism of these micromotors is also a key parameter for their design and *** propulsion efficiency and life span govern the overall performance of a degradable metallic *** we review the design and recent advancements of metallic degradable ***,we describe the controlled degradation,efficient in vivo drug delivery,and built-in acid neutralization capabilities of degradable micromotors with versatile biomedical ***,we discuss micromotors’efficacy in detecting and destroying environmental ***,we address the limitations and future research directions of degradable metallic micromotors.
This paper examines the possibility of using low-cost commercial off-the-shelf audio recording equipment in combination with machine learning techniques to discover the presence of hostile UAVs. A convolutional neural...
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With the development of autonomous driving, more autonomous vehicles are equipped with multiple sensors to perform better. For multi-sensor systems, accurate extrinsic calibration is a prerequisite for perception and ...
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Sampling-based path planning is widely used in robotics,particularly in high-dimensional state *** the path planning process,collision detection is the most time-consuming ***,we propose a learning-based path planning...
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Sampling-based path planning is widely used in robotics,particularly in high-dimensional state *** the path planning process,collision detection is the most time-consuming ***,we propose a learning-based path planning method that reduces the number of collision *** develop an efficient neural network model based on graph neural *** model outputs weights for each neighbor based on the obstacle,searched path,and random geometric graph,which are used to guide the planner in avoiding *** evaluate the efficiency of the proposed path planning method through simulated random worlds and real-world *** results demonstrate that the proposed method significantly reduces the number of collision checks and improves the path planning speed in high-dimensional environments.
Autonomous robots often rely on monocular cameras for odometry estimation and navigation. However, the scale ambiguity problem presents a critical barrier to effective monocular visual odometry. In this paper, we pres...
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Autonomous robots often rely on monocular cameras for odometry estimation and navigation. However, the scale ambiguity problem presents a critical barrier to effective monocular visual odometry. In this paper, we present CodedVO, a novel monocular visual odometry method that overcomes the scale ambiguity problem by employing custom optics to physically encode metric depth information into imagery. By incorporating this information into our odometry pipeline, we achieve state-of-the-art performance in monocular visual odometry with a known scale. We evaluate our method in diverse indoor environments and demonstrate its robustness and adaptability. We achieve a 0.08m average trajectory error in odometry evaluation on the ICL-NUIM indoor odometry dataset. IEEE
With the growing demand for efficient logistics and warehouse management, unmanned aerial vehicles (UAVs) are emerging as a valuable complement to automated guided vehicles (AGVs). UAVs enhance efficiency by navigatin...
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In this article,a robot skills learning framework is developed,which considers both motion modeling and *** order to enable the robot to learn skills from demonstrations,a learning method called dynamic movement primi...
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In this article,a robot skills learning framework is developed,which considers both motion modeling and *** order to enable the robot to learn skills from demonstrations,a learning method called dynamic movement primitives(DMPs)is introduced to model motion.A staged teaching strategy is integrated into DMPs frameworks to enhance the generality such that the complicated tasks can be also performed for multi-joint *** DMP connection method is used to make an accurate and smooth transition in position and velocity space to connect complex motion *** addition,motions are categorized into different goals and *** is worth mentioning that an adaptive neural networks(NNs)control method is proposed to achieve highly accurate trajectory tracking and to ensure the performance of action execution,which is beneficial to the improvement of reliability of the skills learning *** experiment test on the Baxter robot verifies the effectiveness of the proposed method.
SkyvoltRobot represents an innovative solution for electric vehicle (EV) charging services. This system achieves higher efficiency and broader coverage by transporting charging units with robots suspended on overhead ...
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This paper examines the possibility of using low-cost commercial off-the-shelf audio recording equipment in combination with machine learning techniques to discover the presence of hostile UAVs. A convolutional neural...
This paper examines the possibility of using low-cost commercial off-the-shelf audio recording equipment in combination with machine learning techniques to discover the presence of hostile UAVs. A convolutional neural network (CNN) was trained to detect and localize two types of quadrotor drones using ground truth position data collected with motion capture equipment. System performance was evaluated on pre-recorded validation data sets and in realtime operation. In both cases, drones can be successfully detected and localized within the constrained working volumes studied, achieving angular accuracies in the 8–13° range. However, further work remains to be done before system feasibility in outdoor conditions can be established.
The tumor microenvironment-sensitive prodrug-based nanoparticles(NPs)have emerged as a promising drug delivery system(DDS).The shape of these particles plays a crucial role in their in vivo ***,non-spherical organic N...
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The tumor microenvironment-sensitive prodrug-based nanoparticles(NPs)have emerged as a promising drug delivery system(DDS).The shape of these particles plays a crucial role in their in vivo ***,non-spherical organic NPs are rarely reported due to the inherent flexibility and variability of organic ***,we fabricate reduction-sensitive prodrug NPs and explore the impact of their morphology properties on their in vivo *** are self-assembled into spherical NPs with distearoyl phosphoethanolamine-PEG2000(DSPE-PEG2k),or into rod-shaped NPs with D-a-tocopherol polyethylene glycol 2000 succinate(TPGS2k)due to the stronger binding *** comparison with spherical NPs,the endocytosis of rod-shaped NPs predominantly relies on caveolae-mediated pathways rather than clathrin-mediated ones,potentially avoiding degradation by ***,the rod-shaped NPs exhibit prolonged circulation time,increased tumor accumulation,and enhanced antitumor *** current findings reveal the significant effect of particle shape on the behavior of prodrug NPs and introduce a novel paradigm for high-efficacy cancer therapy of prodrug NPs.
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