Autonomous Motion Research Articles

Self-sufficient self-oscillating microsystem driven by low power at low Reynolds numbers.

Oscillations at several hertz are a key feature of dynamic behavior of various biological entities, such as the pulsating heart, firing neurons, or the sperm-beating flagellum. Inspired by nature’s fundamental self-oscillations, we use electroactive polymer microactuators and three-dimensional microswitches to create a synthetic electromechanical parametric relaxation oscillator (EMPRO) that relies on the shape change of micropatterned polypyrrole and generates a rhythmic motion at biologically relevant stroke frequencies of up to ~95 Hz. We incorporate an Ag-Mg electrochemical battery into the EMPRO for autonomous operation in a nontoxic environment. Such a self-sufficient self-oscillating microsystem offers new opportunities for artificial life at low Reynolds numbers by, for instance, mimicking and replacing nature’s propulsion and pumping units.

Research on Nonline-of-Sight Positioning Method of Intelligent Mobile Terminal Based on Intelligent Monitoring Architecture of Multimedia Sensor Network

With the continuous development of social economy, information resources have become more and more valued resources. Based on the intelligent monitoring architecture of the multimedia sensor network, this article proposes a nonline-of-sight positioning method that can fit the characteristics of autonomous movement for the object of intelligent terminal, that is, first draw the corresponding position trajectory according to the speed attribute of the node. On this basis, according to the relative position trajectory and radio frequency signal positioning, the two-by-two positioning of position and direction is comprehensively realized, and the positioning result is obtained; the positioning accuracy is evaluated according to the positioning of the radio frequency signal, and the false positioning result of the distorted radio frequency signal is stripped out to reduce the error influences. Practical results show that the method is effective and can meet the needs of positioning accuracy.

Toward Chemotactic Supramolecular Nanoparticles: From Autonomous Surface Motion Following Specific Chemical Gradients to Multivalency-Controlled Disassembly.

Nature designs chemotactic supramolecular structures that can selectively bind specific groups present on surfaces, autonomously scan them moving along density gradients, and react once a critical concentration is encountered. Since such properties are key in many biological functions, these also offer inspirations for designing artificial systems capable of similar bioinspired autonomous behaviors. One approach is to use soft molecular units that self-assemble in an aqueous solution generating nanoparticles (NPs) that display specific chemical groups on their surface, enabling multivalent interactions with complementarily functionalized surfaces. However, a first challenge is to explore the behavior of these assemblies at sufficiently high-resolution to gain insights on the molecular factors controlling their behaviors. Here, by coupling coarse-grained molecular models and advanced simulation approaches, we show that it is possible to study the (autonomous or driven) motion of self-assembled NPs on a receptor-grafted surface at submolecular resolution. As an example, we focus on self-assembled NPs composed of facially amphiphilic oligomers. We observe how tuning the multivalent interactions between the NP and the surface allows to control of the NP binding, its diffusion along chemical surface gradients, and ultimately, the NP reactivity at determined surface group densities. In silico experiments provide physical–chemical insights on key molecular features in the self-assembling units which determine the dynamic behavior and fate of the NPs on the surface: from adhesion, to diffusion, and disassembly. This offers a privileged point of view into the chemotactic properties of supramolecular assemblies, improving our knowledge on how to design new types of materials with bioinspired autonomous behaviors.

Modular Robotic Limbs for Astronaut Activities Assistance.

In order to meet the assist requirements of extravehicular activity (EVA) for astronauts, such as moving outside the international space station (ISS) or performing on-orbit tasks by a single astronaut, this paper proposes an astronaut robotic limbs system (AstroLimbs) for extravehicular activities assistance. This system has two robotic limbs that can be fixed on the backpack of the astronaut. Each limb is composed of several basic module units with identical structure and function, which makes it modularized and reconfigurable. The robotic limbs can work as extra arms of the astronaut to assist them outside the space station cabin. In this paper, the robotic limbs are designed and developed. The reinforcement learning method is introduced to achieve autonomous motion planning capacity for the robot, which makes the robot intelligent enough to assist the astronaut in unstructured environment. In the meantime, the movement of the robot is also planned to make it move smoothly. The structure scene of the ISS for extravehicular activities is modeled in a simulation environment, which verified the effectiveness of the proposed method.

Development of the algorithm for selecting a rational regular urban route for electric bus operation

Introduction. The article presents an analysis of researches on the operation of public transport vehicles with an electric drive. Electric buses have been tested in megacities, and despite the advantages, problems were identi- fied both in the selection of the charging type, technological features and with the availability and operation of the charging infrastructure. The operation process is associated with a number of technical restrictions, which in prac- tice make it difficult to select routes for the electric bus operation. The aim of the study is to develop an algorithm for selecting a rational regular urban route for the electric bus operation and to test it on the example of the route network of Tyumen.Materials and methods. A structural diagram of the factors influencing the selection of the route for the electric bus operation has been formed. As a result of processing expert assessments, the most significant factors were identified. The diagram of the interconnections of these factors is identified and limitations for the algorithm are for- mulated based on the theory of systems analysis. The algorithm for selecting the rational regular urban route for the electric bus operation has been developed. It enables creating the simulation model for selecting a rational regular urban route for the operation of an electric bus.Results. The diagram of factors influencing the selection of a rational regular urban route for the operation of an electric bus, taking into account their mutual relations, has been formed. The algorithm and simulation model for se- lecting a rational regular urban route for the operation of an electric bus has been developed. Several urban routes in Tyumen were assessed for the rationality of the introduction of electric buses.Discussion and conclusions. Evaluation of five regular urban routes in Tyumen using the developed algorithm and the simulation model revealed three rational routes, and also showed the inefficiency of the other two. It due to the discrepancy between the duration of the charging session and breaks in operation, as well as the insufficient charge level of the traction battery to ensure the required autonomous movement of the vehicle. In the course of the continuation of these studies, the developed algorithm will be supplemented with the stage of economic assess- ment of routes for the operation of electric buses.

Directed Information Flow Analysis Reveals Muscle Fatigue-Related Changes in Muscle Networks and Corticomuscular Coupling.

As a common neurophysiological phenomenon, voluntary muscle fatigue is accompanied by changes in both the central nervous system and peripheral muscles. Considering the effectiveness of the muscle network and the functional corticomuscular coupling (FCMC) in analyzing motor function, muscle fatigue can be analyzed by quantitating the intermuscular coupling and corticomuscular coupling. However, existing coherence-based research on muscle fatigue are limited by the inability of the coherence algorithm to identify the coupling direction, which cannot further reveal the underlying neural mechanism of muscle fatigue. To address this problem, we applied the time-delayed maximal information coefficient (TDMIC) method to quantitate the directional informational interaction in the muscle network and FCMC during a right-hand stabilized grip task. Eight healthy subjects were recruited to the present study. For the muscle networks, the beta-band information flow increased significantly due to muscle fatigue, and the information flow between the synergist muscles were stronger than that between the synergist and antagonist muscles. The information flow in the muscle network mainly flows to flexor digitorum superficialis (FDS), flexor carpi ulnar (FCU), and brachioradialis (BR). For the FCMC, muscle fatigue caused a significant decrease in the beta- and gamma-band bidirectional information flow. Further analysis revealed that the beta-band information flow was significantly stronger in the descending direction [electroencephalogram (EEG) to surface electromyography (sEMG)] than that in the ascending direction (sEMG to EEG) during pre-fatigue tasks. After muscle fatigue, the beta-band information flow in the ascending direction was significantly stronger than that in the descending direction. The present study demonstrates the influence of muscle fatigue on information flow in muscle networks and FCMC. We proposes that beta-band intermuscular and corticomuscular informational interaction plays an adjusting role in autonomous movement completion under muscle fatigue. Directed information flow analysis can be used as an effective method to explore the neural mechanism of muscle fatigue on the macroscopic scale.

Mapping performance comparison of 2D SLAM algorithms based on different sensor combinations

Currently, some existing SLAM algorithms were widely applied in the field of mobile robots. However, most SLAM algorithms were tested either by simulation or using offline bags storing messages, which ignored other contributing factors influencing mapping including the changes of position and speed of the robot during it autonomous movement. The aim of this study is to investigate the solution of constructing more accurate map with high quality sensors based on freely available SLAM algorithms. Comparisons and analyses of three 2D SLAM algorithms (i.e. Gmapping, Hector SLAM and Cartographer) available in ROS were conducted to map different environments in our work using different sensor combinations of a LIDAR, a Stereo camera and an IMU, respectively. The research showed that the Cartographer algorithm has advantage over two other algorithms on constructing well-defined and informative environmental maps with minimal errors. Also, the LIDAR scan is more suitable than the ZED scan as the input of mapping algorithms, and the Cartographer combining the LIDAR scan, ZED odometry and IMU is the best solution to map in all combinations.

Autonomous land vehicle path planning algorithm based on improved heuristic function of A-Star

The path planning of autonomous land vehicle has become a research hotspot in recent years. In this article, we present a novel path planning algorithm for an autonomous land vehicle. According to the characteristics of autonomous movement towards the autonomous land vehicle, an improved A-Star path planning algorithm is designed. The disadvantages of using the A-Star algorithm for path planning are that the path planned by the A-Star algorithm contains many unnecessary turning points and is not smooth enough. Autonomous land vehicle needs to adjust its posture at each turning point, which will greatly waste time and also will not be conducive to the motion control of autonomous land vehicle. In view of these shortcomings, this article proposes a new heuristic function combined with the artificial potential field method, which contains both distance information and obstacle information. Our proposed algorithm shows excellent performance in improving the execution efficiency and reducing the number of turning points. The simulation results show that the proposed algorithm, compared with the traditional A-Star algorithm, makes the path smoother and makes the autonomous land vehicle easier to control.

Research on optimization of complex path of inspection robot

It is a common method to use intelligent robots for automatic inspection in highly automated ships. Path planning and optimization research are an important part of robotics. Intelligent tracking is the key to autonomous movement of mobile robots. In this paper, a grid map is used to establish a simulation model of the robot tracing environment. In a grid map, the more complex the map environment, the slower the algorithm will converge. The -fill-merge method processes the concave obstacles in the environment into regular shapes, reduces the number of unnecessary calculations, thereby greatly optimizing the tracing environment and speeding up the convergence speed. Finally, the MATLAB simulation experiment shows that the method based on environment optimization can effectively accelerate the convergence speed, avoid the local optimum, and quickly find the global optimum when dealing with the problem of autonomous tracking of intelligent robots in a map environment with a large number of concave obstacles. solution.

Lane-Level Map-Aiding Approach Based on Non-Lane-Level Digital Map Data in Road Transport Security

To prevent terror attacks in which trucks are used as weapons as happened in Nice or Berlin in 2016, the European Project Autonomous Emergency Maneuvering and Movement Monitoring for Road Transport Security (TransSec) was launched in 2018. One crucial point of this project is the development of a map-aiding approach for the localization of vehicles on digital maps, so that the information in digital map data can be used to detect prohibited driving maneuvers, such as off-road or wrong-way drivers. For example, a lane-level map-aiding approach is required for wrong-way driver detection. Navigation Data Standard (NDS) is one of the worldwide map standards developed by several automobile manufacturers. So far, there is no lane-level NDS map covers a large area, therefore, it was decided to use the latest available NDS map without lane level accuracy. In this paper, a lane-level map-aiding approach based on a non-lane-level NDS map is presented. Due to the inaccuracy of vehicle position and digital map the map-aiding does not always provide the correct results, so probabilities of off-road and wrong-way diver detection are estimated to support risk estimation. The performance of the developed map-aiding approach is comprehensively evaluated with both real and simulated trajectories.

Application of Model-based Control in Autonomous Vehicle Chassis Control and Motion Planning

Application of Model-based Control in Autonomous Vehicle Chassis Control and Motion Planning

A Magnetocatalytic Propelled Cobalt–Platinum@Graphene Navigator for Enhanced Tumor Penetration and Theranostics

A Magnetocatalytic Propelled Cobalt–Platinum@Graphene Navigator for Enhanced Tumor Penetration and Theranostics

Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?

Most surgeons are skeptical as to the feasibility of autonomous actions in surgery. Interestingly, many examples of autonomous actions already exist and have been around for years. Since the beginning of this millennium, the field of artificial intelligence (AI) has grown exponentially with the development of machine learning (ML), deep learning (DL), computer vision (CV) and natural language processing (NLP). All of these facets of AI will be fundamental to the development of more autonomous actions in surgery, unfortunately, only a limited number of surgeons have or seek expertise in this rapidly evolving field. As opposed to AI in medicine, AI surgery (AIS) involves autonomous movements. Fortuitously, as the field of robotics in surgery has improved, more surgeons are becoming interested in technology and the potential of autonomous actions in procedures such as interventional radiology, endoscopy and surgery. The lack of haptics, or the sensation of touch, has hindered the wider adoption of robotics by many surgeons; however, now that the true potential of robotics can be comprehended, the embracing of AI by the surgical community is more important than ever before. Although current complete surgical systems are mainly only examples of tele-manipulation, for surgeons to get to more autonomously functioning robots, haptics is perhaps not the most important aspect. If the goal is for robots to ultimately become more and more independent, perhaps research should not focus on the concept of haptics as it is perceived by humans, and the focus should be on haptics as it is perceived by robots/computers. This article will discuss aspects of ML, DL, CV and NLP as they pertain to the modern practice of surgery, with a focus on current AI issues and advances that will enable us to get to more autonomous actions in surgery. Ultimately, there may be a paradigm shift that needs to occur in the surgical community as more surgeons with expertise in AI may be needed to fully unlock the potential of AIS in a safe, efficacious and timely manner.

Crescent-shaped micromotor sorbents with sulfonic acid functionalized convex surface: The synthesis by A Janus emulsion strategy and adsorption for Li+

Self-propelled micromotor (SPM) plays a vital role in recycling of lithium (Li+) from wastewater in battery industry. In this work, a crescent-shaped micromotor sorbent (CSMSs) with sulfonic group on convex surface was prepared by Janus emulsion to extract Li+. Using sodium p-styrene sulfonate as a functional monomer, well-designed CSMSs was prepared by UV-induced monomer interfacial polymerization, and their pit size can be controlled by adjusting the ratio of two incompatible oils (ethoxylated trimethylolpropane triacrylate and liquid paraffin). In addition, MnO2 nanoparticles, which embedded into concave interface, generated O2 bubbles in the presence of H2O2, and constant circular or line motion of CSMSs was observed. Zeta potential of CSMSs was −51.66 eV at pH = 10, and strong electrostatic attraction between sulfonate groups and Li+ endowed the maximum monolayer adsorption capacity of 31 mg g−1 at 25 °C. Self-propelled effect further enhanced kinetic performance for Li+, and equilibrium time can be reduced from original 10–6.0 h, suggesting autonomous movement achieves rapid mixing and mass transportation. After three adsorption/desorption cycles, the adsorption capacity of the material remains above 90%. This simple and large-scale preparation strategy provided a synthetic method for functional and Janus SPM, as well as sorbents for Li+ enrichment.

The YTU dataset and recurrent neural network based visual-inertial odometry

Visual Simultaneous Localization and Mapping (VSLAM) and Visual Odometry (VO) are fundamental problems to be properly tackled for enabling autonomous and effective movements of vehicles/robots supported by vision-based positioning systems. This study presents a publicly shared dataset for SLAM investigations: a dataset collected at the Yildiz Technical University (YTU) in an outdoor area by an acquisition system mounted on a terrestrial vehicle. The acquisition system includes two cameras, an inertial measurement unit, and two GPS receivers. All sensors have been calibrated and synchronized. To prove the effectiveness of the introduced dataset, this study also applies Visual Inertial Odometry (VIO) on the KITTI dataset. Also, this study proposes a new recurrent neural network-based VIO rather than just introducing a new dataset. In addition, the effectiveness of this proposed method is proven by comparing it with the state-of-the-arts ORB-SLAM2 and OKVIS methods. The experimental results show that the YTU dataset is robust enough to be used for benchmarking studies and the proposed deep learning-based VIO is more successful than the other two traditional methods.

Bridge-Ship Collision Avoidance Control Based on AFSMC with a FRNN Estimator

For collision avoidance and maneuvering control in bridge areas, an adaptive fractional sliding mode control with fractional recurrent neural network (FRNN-AFSMC) is proposed. The uncertainties are estimated by FRNN, and the fractional gradient is adopted to improve the recurrent neural network (RNN). Its convergence has been proven. The influence of fractional order on algorithm performance is analyzed, and the simulation platform of ship collision avoidance control is built. Dynamic collision avoidance of multiple ships is simulated and verified. The results show the feasibility and effectiveness of dynamic autonomous collision avoidance motion control in a dynamic ocean environment.

Autonomous underwater vehicle challenge: design and construction of a medium-sized, AI-enabled low-cost prototype

The design of an autonomous underwater vehicle (AUV) with physical dimensions of 1100 mm × 700 mm × 330 mm, and weight of 55 kg, is introduced herein. This paper describes the design, materials, hydrodynamics, and system architecture of an AUV prototype named Synoris, developed as a low-cost and medium-scale testbed platform. Synoris moves via six brushless motors, can reach up to 200 m depth, has an autonomy estimated around 6 hours and a modular design for multiple payload options. Stability control, autonomous movement, obstacle avoidance temperature/pressure sensing, and video/image capturing are simultaneously performed by exploiting a set of onboard computers that are described briefly in Section 4. The whole platform is built on top of the open source software called ROS (robotic operating system) that provides a flexible framework for writing robot software by providing services such as low-level device control, message parsing, data fusion, and system integration. Synoris is ideal for underwater applications and missions, involving machine learning and computer vision features. AUV development in general meets high-cost solutions due to the complexity and harshness of the operational environment. Even the most cost-effective solutions demand plentiful resources. This paper describes the entire process of development and how a relatively low-cost approach can provide a reliable AUV for many underwater applications, involving AI and machine-learning capabilities.

Stimuli-Responsive Autonomous-Motion Molecular Machine for Sensitive Simultaneous Fluorescence Imaging of Intracellular MicroRNAs.

DNAzymes with enzymatic activity identified from random DNA pools by in vitro selection have recently attracted considerable attention. In this work, a DNAzyme-based autonomous-motion (AM) molecular machine is demonstrated for sensitive simultaneous imaging of different intracellular microRNAs (miRNAs). The AM molecular machine consists of two basic elements, one of which is a target-analogue-embedded double-stem hairpin substrate (TDHS) and the other is a locking-strand-silenced DNAzyme (LSDz). LSDz can be activated by target miRNA and catalytically cleave TDHS, generating Clv-TDHS and releasing free target analogue capable of triggering the next round of cleavage reaction. As such, the molecular machine can exert sustainable autonomous operation, producing an enhanced signal. Because the active target analogue comes from the machine itself and offers cyclical stimulation in a feedback manner, this target-induced autonomous cleavage circuit is termed a self-feedback circuit (SFC). The SFC-based molecular machine can be used to quantify miRNA-21 down to 10 pM without interference from nontarget miRNAs, indicating a substantial improvement in assay performance compared with its counterpart system without an SFC effect. Moreover, due to the enzyme-free process, the AM molecular machine is suitable for miRNA imaging in living cells, and the quantitative results are consistent with the gold standard PCR assay. More interestingly, the AM molecular machine can be used for the simultaneous fluorescence imaging of several intracellular miRNAs, enabling the accurate discrimination of cancerous cells (e.g., HeLa and MCF-7) from healthy cells. The SFC-based autonomous-motion machine is expected to be a promising tool for the research of molecular biology and early diagnosis of human diseases.

Agir sur le corps pour se mouvoir dans l’espace

This paper deals with causative verbs of motion extracted from a corpus of instructions in contemporary dance. The content of the corpus, the annotation procedure and the main models or patterns uncovered in the collected utterances are first set out. One of the models integrates causative verbs of motion that this analysis focuses on. In a second step, in order to classify these lexical items, the paper provides a broader discussion of the relationship between the expression of autonomous motion and that of caused motion and, in particular, of the way the semantic criteria singled out for analysing autonomous motion can be reused to study the expression of caused motion. After contextualizing the selected criteria within the corpus of dance instructions, the classification of the causative motion verbs is finally presented and commented.

Dual-stimuli-responsive CuS-based micromotors for efficient photo-Fenton degradation of antibiotics

Antibiotics as emerging pollutants in water pose great risks to human health. Due to their persistence in the environment, advanced oxidation processes (AOPs) have been proposed for the degradation of antibiotics. Therefore, developing efficient catalysts for AOPs becomes critical for the removal of antibiotics. Herein, we develop self-propelled CuS-based micromotors (CuS@Fe3O4/Pt) as active heterogenous catalysts for efficient photo-Fenton degradation of antibiotics. Combining the merits of conventional heterogenous and homogenous catalysts, the prepared micromotors are easy to recycle and free of secondary pollution risks, while demonstrating high degradation efficiency due to self-induced intensification of mass transfer via autonomous motion and microbubble generation. The H2O2 in the Fenton reagents can serve as the fuel for the micromotors to drive their self-propulsion by bubbles generated from catalytic decomposition of H2O2 by the platinum layer. The dual-stimuli-responsiveness of the micromotors to magnetic field and light irradiation allows multi-modes of propulsion and guidance in different systems. The efficient photothermal effect of CuS enables the micromotors to achieve collective phototactic motion toward light, whereas magnetic responsiveness facilitates the recovery and collection of the micromotors. The synergistic effect of CuS and Fe3O4 NPs in H2O2 under visible light irradiation generates a large amount of OH· and ·O2– to effectively degrade tetracycline within several minutes. With these advantages, the dual-stimuli-responsive CuS-based micromotors provide a new strategy for enhanced degradation of antibiotics in water purification applications.