Autonomous Motion Research Articles

Methamphetamine Removal from Aquatic Environments by Magnetic Microrobots with Cyclodextrin Chiral Recognition Elements.

The growing consumption of drugs of abuse together with the inefficiency of the current wastewater treatment plants toward their presence has resulted in an emergent class of pollutants. Thus, the development of alternative approaches to remediate this environmental threat is urgently needed. Microrobots, combining autonomous motion with great tunability for the development of specific tasks, have turned into promising candidates to take on the challenge. Here, hybrid urchin-like hematite (α-Fe2O3) microparticles carrying magnetite (Fe3O4) nanoparticles and surface functionalization with organic β-cyclodextrin (CD) molecules are prepared with the aim of on-the-fly encapsulation of illicit drugs into the linked CD cavities of moving microrobots. The resulting mag-CD microrobots are tested against methamphetamine (MA), proving their ability for the removal of this psychoactive substance. A dramatically enhanced capture of MA from water with active magnetically powered microrobots when compared with static passive CD-modified particles is demonstrated. This work shows the advantages of enhanced mass transfer provided by the externally controlled magnetic navigation in microrobots that together with the versatility of their design is an efficient strategy to clean polluted waters.

Engineering flower-shaped hierarchical micromotors on a sustainable biotemplate by teamed boronate affinity-based surface imprinting for effective separation of shikimic acid

Self-propelled nano/micromotors are the frontier of separation materials because they are capable of converting external energy in the surrounding environment into kinetic energy for their autonomous movement. In this work, a bioinspired flower-shaped hierarchical teamed boronate affinity (TBA)-imprinted Pt-free micromotor is introduced based on waste rape pollen as a biotemplate due to its merits of sustainability, rich source and low price. Such micromotor, composed of MnO2 nanosheets as a catalytic medium, Mg-Al layered double hydroxides (LDH) nanosheets as large surface area substrate and functional surface imprinted polymers as identification subject, exhibits autonomic motor behavior powdered by oxygen bubbles generated by the decomposition of H2O2 and selective recognition and separation for shikimic acid (SA). The hierarchical flower-shaped structure affords more accessible recognition sites for SA, which thus facilitates the separation process. Moreover, in combination with the TBA strategy, the micromotor can precisely recognize the target via boronate affinity between boronic acids and cis-diols from SA while showing strong binding capacity under neutral conditions. Benefiting from the pluripotent role of pioneering boronate affinity-covalent imprinted technique, hydrogen bonds interaction and nanoconfinement effect, the resulting affinity towards SA is evidently enhanced with maximal adsorption capacity of 129.51 mg g−1 at neutral pH in the presence of H2O2. After five capture/release cycles, the adsorption capacity remains above 85 %. This proposed flower-shaped hierarchical micromotor expands the scope of potential materials for the adsorption of SA and provides a new and promising direction for fabricating adsorbents applied in the separation and purification of natural products.

Makedonski separatizam na prostoru Južne Srbije u periodu 1941‒1945.

This article aims to shed light on Macedonian separatist aspirations in the area of the occupied part of the Kingdom of Yugoslavia, which in the pre-war period was known as South Serbia, during the World War II, but also immediately after its end, when a political entity called Macedonia was formed for the first time in history. Macedonian autonomists and federalists in this area promoted, tactically speaking, two version of the same policy in the Ottoman and interwar periods, which aimed at the national emancipation of the Macedonians from the rest of the Serbian nation. Thus, during the World War II, there will be several factions of Macedonian separatist politics. One will openly side with the Bulgarian fascist occupier and serve in his occupation apparatus, and for the first time towards the end of the war (1944) will try to form the Independent State of Macedonia, while the other will operate under the auspices of the Communist Party of Yugoslavia. During the war itself, and within the KPJ, several currents appeared, which, wanting to achieve the same goal, advocated different approaches to solving the Macedonian issue. One faction will advocate the fight for a Macedonian state in the Balkans through an autonomous national liberation movement with the possibility of joining the Balkan Federation, while the other, prevailing, will fight for the establishment of Macedonia within the Yugoslav framework, i.e. for the realization of national rights under the leadership of the KPJ as part of solving the national question. This policy will win after the Second World War, when the Macedonian federal unit is formed within the new Yugoslav state, which will be the embryo of the future independent state of North Macedonia.

R²NMPC: A Real-Time Reduced Robustified Nonlinear Model Predictive Control with Ellipsoidal Uncertainty Sets for Autonomous Vehicle Motion Control

In this paper, we present a novel Reduced Robustified NMPC (R²NMPC) algorithm that has the same complexity as an equivalent nominal NMPC while enhancing it with robustified constraints based on the dynamics of ellipsoidal uncertainty sets. This promises both a closed-loop- and constraint satisfaction performance equivalent to common Robustified NMPC approaches while drastically reducing the computational complexity. The main idea lies in approximating the ellipsoidal uncertainty sets propagation over the prediction horizon with the system dynamics’ sensitivities inferred from the last optimal control problem (OCP) solution and similarly for the gradients to robustify the constraints. Thus, we do not require the decision variables related to the uncertainty propagation within the OCP, rendering it computationally tractable. Next, we illustrate the real-time control capabilities of our algorithm in handling a complex, high-dimensional, and highly nonlinear system, namely the trajectory following of an autonomous passenger vehicle modeled with a dynamic nonlinear single-track model. Our experimental findings, alongside a comparative assessment against other Robust NMPC approaches, affirm the robustness of our method in effectively tracking an optimal racetrack trajectory while satisfying the nonlinear constraints. This performance is achieved while fully utilizing the vehicle's interface limits, even at high speeds of up to 37.5m s−1, and successfully managing state estimation disturbances. Our approach maintains a mean solving frequency of 140Hz. The code used in this research is publicly accessible as open-source software:https://github.com/bzarr/TUM-CONTROL

Correction of the movement of the mobile robot using the modified algorithm

Currently, automation of processes in which a robot can replace a human is widespread. To solve this problem, robots must be able to move independently from one position to another. Currently, many different approaches have been developed to solve this problem, such as neural networks, the method of potential fields, fuzzy logic methods, genetic algorithms, and computer vision. One of the most effective methods of solving the navigation problem is the method of building a navigation system based on the use of algorithms for localization, mapping, and automatic obstacle avoidance. One of the most effective obstacle avoidance algorithms is the D-star algorithm, which, despite its effectiveness, has some drawbacks. The method of potential fields and neural networks also showed good results, but each of them separately has several significant drawbacks. Based on the method of building a navigation system, the method of potential fields and neural networks, a modified method has been developed that allows a mobile robot to perform autonomous movement in an environment with obstacles. This modification eliminates some of the problems that arise when implementing the navigation system and combines the advantages of the methods used.

Organic nanomotors: emerging versatile nanobots.

Artificial nanomotors are self-propelled nanometer-scaled machines that are capable of converting external energy into mechanical motion. A significant progress on artificial nanomotors over the last decades has unlocked the potential of carrying out manipulatable transport and cargo delivery missions with enhanced efficiencies owing to their stimulus-responsive autonomous movement in various complex environments, allowing for future advances in a large range of applications. Emergent kinetic systems with programmable energy-converting mechanisms that are capable of powering the nanomotors are attracting increasing attention. This review highlights the most-recent representative examples of synthetic organic nanomotors having self-propelled motion exclusively powered by organic molecule- or their aggregate-based kinetic systems. The stimulus-responsive propulsion mechanism, motion behaviors, and performance in antitumor therapy of organic nanomotors developed so far are illustrated. A future perspective on the development of organic nanomotors is also proposed. With continuous innovation, it is believed that the scope and possible achievements in practical applications of organic nanomotors with diversified organic kinetic systems will expand.

Autonomous Emergency Landing for Fixed-Wing Aircraft with Energy-Constrained Closed-Loop Prediction

This paper presents a new approach for autonomous motion planning for aircraft suffering from a loss-of-thrust emergency. Specifically, we show how modifications to the Closed-Loop Rapidly exploring Random Trees (CL-RRT) framework combined with controlled energy dissipation can enable rapid and effective kinodynamic motion planning. This CL-RRT Glide algorithm uses closed-loop prediction not only for node connections but also to estimate the remaining energy and prune infeasible paths. This greatly speeds up the search process, which is essential for emergency situations. In addition, we improve the ability of the gliding aircraft to reach a goal position and energy state. We do so by creating a Dissipative Total Energy Control Scheme (TECS). Dissipative TECS enables the glider to lose excess altitude in order to reach a desired energy level. Simulation results illustrate how the proposed methods enable faster motion planning. We also integrate the system into a small unmanned aerial vehicle system and experimentally demonstrate autonomous glide planning and execution during a motor-failure event. This type of algorithm can primarily benefit unmanned aircraft but can also serve to assist pilots in stressful emergency situations.

Learning-based MPC for Autonomous Motion Planning at Freeway Off-ramp Diverging

Learning-based MPC for Autonomous Motion Planning at Freeway Off-ramp Diverging

Magnetic gelatin-hesperidin microrobots promote proliferation and migration of dermal fibroblasts.

Dermal fibroblasts play a crucial role in the formation of granulation tissue in skin wounds. Consequently, the differentiation, migration, and proliferation of dermal fibroblasts are considered key factors in the skin wound healing process. However, in patients with diabetic foot ulcers, the proliferation and migration of fibroblasts are impaired by reactive oxygen species and inflammatory factors impair. Therefore, a novel magnetic gelatin-hesperidin microrobots drug delivery system was developed using microfluidics. The morphology, motility characteristics, and drug release of the microrobot were assessed, along with its impact on the proliferation and migration of human dermal fibroblasts under high-glucose conditions. Subjected to a rotating magnetic field, the microrobots exhibit precise, controllable, and flexible autonomous motion, achieving a maximum speed of 9.237μm/s. In vitro drug release experiments revealed that approximately 78% of the drug was released within 30min. It was demonstrated through cellular experiments that the proliferation of human dermal fibroblasts was actively promoted by the nanorobot, the migration ability of fibroblasts in a high-glucose state was enhanced, and good biocompatibility was exhibited. Hence, our study may provide a novel drug delivery system with significant potential for promoting the healing of diabetic foot wounds.

Development of Image Processing Based Line Tracking Systems for Automated Guided Vehicles with ANFIS and Fuzzy Logic

Automated Guided Vehicles (AGVs) are robotic vehicles with the ability to move using mapping and navigation technologies to perform tasks assigned to them, guided by guides. Using sensor data such as laser scanners, cameras, magnetic stripes or colored stripes, they can sense their environment and move safely according to defined routes. The basic requirement of motion planning is to follow the path and route with minimum error even under different environmental factors. The key factor here is the most successful detection of the guiding structure of a system moving on its route. The proposed system is to equip a mechanical system that can produce very fast outputs and autonomous motion as a result of combining different algorithms with different hardware structures. In the line detection process, the wide perspective image from the camera is designed to be gradually reduced and converted into image information that is more concise but representative of the problem in a narrower perspective. In this way, the desired data can be extracted with faster processing over less information. In this study, the image information is divided into two parts and planned as two different sensors. The fact that the line information was taken from two different regions of the image at a certain distance enabled the detection of not only the presence of the line but also the flow direction. With the fuzzy system, the performance of the system was increased by generating PWM values on two different hardware structures, loading image capture, image processing processes and driving the motors. In order to determine the membership function parameters of the fuzzy system for each input, the ANFIS approach was used on the data set modeling the system. The outputs produced by the ANFIS model were combined into a single fuzzy system with two outputs from the system rules framework and the system was completed. The success of the algorithms was ensured by partitioning the task distribution in the hardware structure. With its structure and success in adapting different technologies together, a system that can be recommended for similar problems has been developed.

Navit(oo)n: Open Source Mobile Robot Project for Nakanoshima Robot Challenge

Recently, the legislation regarding autonomous mobile robots for outdoor pedestrian areas have been advancing, leading to increased expectations for task automation such as transportation and cleaning. Outdoor environments like parks, where vehicles cannot enter, present many three-dimensional terrains such as stairs and inclined surfaces, causing difficulty in achieving accurate environment recognition and autonomous movement. Furthermore, robots that navigate pedestrian walkways must be smaller and lighter than cars and also have a robust system capable of traversing steps and uneven surfaces and withstanding rainy weather. Currently, robots designed for paved roads are commercially available; however, robots capable of navigating park walkways are still in the research and development stage. Therefore, to accelerate the research and development of outdoor autonomous mobile robots, this study proposes the Navit(oo)n platform, designed for use in outdoor environments. This robot can be manufactured using easily obtainable parts, and all CAD data, circuit design data, and autonomous movement software are provided as open source. This paper introduces an overview of Navit(oo)n that successfully completed the course and achieved all tasks in the recent Nakanoshima Robot Challenge.

Research on the progress of neurorehabilitation

With the intensification of global aging problems, the incidence of central nervous system diseases such as stroke is getting higher and higher. At the same time, the number of disabled people has also increased. Amputation and neurological diseases are important causes of disability, and neurorehabilitation is an important method for restoring the nervous function and autonomous movement ability of the disabled. At present, neurorehabilitation methods commonly used in clinical treatment mainly include: wearing prosthetic limbs, functional electrical stimulation (FES) therapy, rehabilitation robots, and so on. The prosthetic limb refers to an artificial device used to replace the function of a lost limb. Many advanced technologies are being used to improve the motion control of prosthetics to achieve natural movement. FES is a technique that can improve physiological function or treat diseases by stimulating nerves or tissues through current, and has become one of the important means to promote nerve regeneration and restore motor function. Rehabilitation robots are the equipment that uses mechanical devices and advanced control technology to assist rehabilitation treatment and functional recovery. This article mainly discusses the current research progresses of prosthetic limbs, EFS and rehabilitation robots, so as to promote the development of neurorehabilitation.

Trial of Utilization of an Environmental Map Generated by a High-Precision 3D Scanner for a Mobile Robot

In recent years, high-precision 3D environmental maps have attracted the attention of researchers in various fields and have been put to practical use. For the autonomous movement of mobile robots, it is common to create an environmental map in advance and use it for localization. In this study, to investigate the usefulness of 3D environmental maps, we scanned physical environments using two different simultaneous localization and mapping (SLAM) approaches, specifically a wearable 3D scanner and a 3D LiDAR mounted on a robot. We used the scan data to create 3D environmental maps consisting of 3D point clouds. Wearable 3D scanners can be used to generate high-density and high-precision 3D point-cloud maps. The application of high-precision maps to the field of autonomous navigation is expected to improve the accuracy of self-localization. Navigation experiments were conducted using a robot, which was equipped with the maps obtained from the two approaches described. Autonomous navigation was achieved in this manner, and the performance of the robot using each type of map was assessed by requiring it to halt at specific landmarks set along the route. The high-density colored environmental map generated from the wearable 3D scanner’s data enabled the robot to perform autonomous navigation easily with a high degree of accuracy, showing potential for usage in digital twin applications.

Increasing the Level of Autonomy of Control of the Electric Arc Furnace by Weakening Interphase Interactions

Steelmaking is one of the most energy-intensive industries, so improving control efficiency helps to reduce the energy used to produce a tonne of steel. Mutual influences between the phases of an electric arc furnace in available electrode movement control systems cause unproductive electrode movements as a reaction to the redistribution of currents among the phases of a three-phase power supply system due to changes in arc length in one of the phases. The nonlinearity of the characteristics of an electric arc furnace significantly complicates the ability to provide autonomous electrode movement control. The approach proposed in this paper, based on the formation of a matrix of mutual influences with variable coefficients, significantly improves the per-phase autonomy of the electrode movement control system. Nonlinear dependences of the mutual influence coefficients as a function of the current increment in the phase in which the disturbance occurred are obtained. Thus, it is possible to practically eliminate unproductive electrode movements in existing control systems by avoiding the traditional use of a dead zone, which reduces the control quality in the zone of small disturbances. The complex of experiments performed using the mathematical model demonstrate that the mutual influence improves the dynamic properties of the electrode movement system in certain operating modes.

Autonomous overtaking decision and motion planning of intelligent vehicles based on graph convolutional network and conditional imitation learning

To ensure safe overtaking of intelligent vehicles in dynamic interactive environments, this paper proposes an end-to-end learning method for autonomous overtaking based on Graph Convolutional Network (GCN) and Conditional Imitation Learning (CIL). This method completes the autonomous overtaking behavior by directly mapping the environmental perception data to the underlying vehicle control actions (e.g. throttle and steer angle). This method fully considers the influence of other vehicles’ driving behavior on the overtaking behavior of the ego vehicle. Firstly, the dynamic interactive environments information around the ego vehicle is aggregated in the form of graph-structured data, and the aggregated global features are used as the input of the GCN to output the optimal action instructions that the ego vehicle should take. Secondly, combined with CIL, the action instructions output by the GCN are used as high-level commands to guide CIL. Finally, combined with other perception data, the underlying control actions of the vehicle will be output by CIL to complete safe overtaking in dynamic interactive environments. The method proposed in this paper can effectively extract the global information of the driving scene and complete the collision-free autonomous overtaking behavior, which greatly improves the intelligence of the driving system. The feasibility of the method has been verified by experiments on the CARLA simulation platform. The experimental results prove that the performance of this method is better than that of the conventional end-to-end learning framework, and it has better success rate and generalization performance.

The Poona Pact, Indian National Congress and the descriptive and substantive representation of Dalits in colonial India

AbstractWithin the prevailing historiographical tradition of modern India, critics see the Poona Pact as having “disenfranchised” Dalits, which they attribute to the fact that, due to the numerical superiority of caste Hindus, the implementation of joint electorates resulted in the consolidation of power within the Indian National Congress: the party that, critics allege, protected the interests of the caste Hindu community. Critics further argue that Dalit candidates who successfully ran for office under the Congress party’s banner, garnering support mostly from caste Hindu voters, failed to speak for the interests of the Dalit community effectively. This article examines the returns of the provincial assembly elections held in 1936–1937 and 1945–1946, as well as the functioning of the Congress ministries in the provinces of British India between 1937 and 1939 and 1946 and 1947 to challenge the criticisms mentioned above and to argue that the inclusion of reserved seats, primary elections, and cumulative voting mechanisms had a significant role in enhancing the potential of the Poona Pact to ensure genuine descriptive representation of Dalits. The article also finds that the affiliation of Dalit legislators with the Congress party had a beneficial impact on their substantive representation in the provincial legislatures where the Congress formed ministries because Dalit interests and the ideological and programmatic dynamics of the Congress party were congruent. In this context, Gandhi, a member of the caste Hindu community, played the role of a “critical actor” who encouraged the Congress party to undertake measures to advance the interests of the Dalit community. Moreover, a powerful and autonomous anti-untouchability movement led by the Harijan Sevak Sangh played a crucial role in enhancing the institutional capabilities of the Congress governments, enabling them to effectively address the concerns and challenges faced by the Dalit community, which further bolstered the substantive representation of Dalits.

A Bilateral Teleoperation System With Learning-Based Cognitive Guiding Force

This article investigates the problem involving a fuzzy logic system-based bilateral teleoperation system with a unified impedance/admittance structure and learning-based assisted cognitive guiding force, enhancing a smooth transition between augmentation and autonomous motion on the master side. The impedance structure regulates the impedance and admittance via the parameters determined by the fuzzy logic system and, besides the variable impedance during interaction with the environment, the fuzzy Q-learning algorithm generates an assisted cognitive guiding force to guide the trainee operator in free space for reaching the interaction surface smoothly and steadily. The degeneration of tracking performance in the bilateral teleoperation system caused by time delay is analyzed and reduced via a mixed-type error. Experimental results verify the stability analyses, and under the proposed control scheme, the human-robot-environment interaction is smooth and stable.

Developing inverse motion planning technique for autonomous vehicles using integral nonlinear constraints

The study considers issues of elaborating and validating a technique of autonomous vehicle motion planning based on sequential trajectory and speed optimization. This method includes components such as representing sought-for functions by finite elements (FE), vehicle kinematic model, sequential quadratic programming for nonlinear constrained optimization, and Gaussian N-point quadrature integration. The primary novelty consists of using the inverse approach for obtaining vehicle trajectory and speed. The curvature and speed are represented by integrated polynomials to reduce the number of unknowns. For this, piecewise functions with two and three degrees of freedom (DOF) are implemented through FE nodal parameters. The technique ensures higher differentiability compared to the needed in the geometric and kinematic equations. Thus, the generated reference curves are characterized by simple and unambiguous forms. The latter fits best the control accuracy and efficiency during the motion tracking phase. Another advantage is replacing the nodal linear equality constraints with integral nonlinear ones. This ensures the non-violation of boundary limits within each FE and not only in nodes. The optimization technique implies that the spatial and time variables must be found separately and staged. The trajectory search is accomplished in the restricted allowable zone composed by superposing an area inside the external and internal boundaries, based on keeping safe distances, excluding areas for moving obstacles. Thus, this study compares two models that use two and three nodal DOF on optimization quality, stability, and rapidity in real-time applications. The simulation example shows numerous graph results of geometric and kinematic parameters with smoothed curves up to the highest derivatives. Finally, the conclusions are made on the efficiency and quality of prognosis, outlining the similarities and differences between the two applied models.

Multifunctional glucose-powered nanomotors with robust dual enzyme mimic activities

Catalytic micro-/nanomotors (MNMs) that use H2O2 as fuel to generate O2 bubbles for propulsion hold great promise for exciting applications in the biological and environmental fields. However, it is still a challenge to alleviate or avoid the negative impact of H2O2 on the ecological environment of water body. Herein, a novel glucose-driven nanomotor with robust dual enzyme-like activities was prepared by precisely controlling the phase composition and enzyme immobilization to construct a 3D hierarchical structure integrating Fe-MOF, MnO2 and glucose oxidase (GOx) on halloysite nanotube (HNTs) support. Embedding GOx into the framework of Fe-MOF by a simple one-step coprecipitation process allowed the in-situ generation of H2O2 in the presence of glucose. Such H2O2 could be further decomposed to O2 bubbles to drive nanomotors. Meanwhile, O2 bubbles and residual H2O2 could convert to active species superoxide radical (O2•−) and hydroxyl radical (•OH) due to the high peroxidase/oxidase-like activities of Fe-MOF/MnO2 in nanomotors. As a proof of concept, the as-synthesized nanomotors could realize sensitively colorimetric detection of glutathione (GSH) with a detection limit of 9.3 × 10−8 M. In particular, the nanomotors could also rapidly degrade tetracycline hydrochloride (TCH) under neutral conditions without additional H2O2. It is envisioned that the new strategy that combines strong enzyme-like activity with enhanced autonomous motion can promote the nanomotors efficiency for biomolecular sensing and environmental remediation.

Multifunctional CuS-based micro-flower loaded with carbon dots/laccase for effectively detection and removal of catechol

The miniaturization and autonomous mobility of the artificial micro-composite have gained widespread interest and attention in the field of catechol treatment. Unfortunately, it is a bottleneck for micro-composite in realizing the integration of multiple functions such as colorimetric detection and removal of catechol. Herein, we construct a Janus micro-composite with dual functions of detection and degradation for the efficient detection and removal of catechol. The Janus micro-composite for laccase immobilization was prepared by growing manganese dioxide nanoparticles (MnO2 NPs) on the hemisphere of copper sulfide (CuS) loaded with carbon (C) dots (Lac-MnO2@C@CuS). The half-coated MnO2 NPs is used as a power source to generate oxygen (O2), which drives the Janus micro-composite for autonomous directional motion at a speed of 178.83 ± 2.07 μm/s in the presence of 5 wt% H2O2. The Janus micro-composite with excellent peroxidase-like properties could display sensitive detection of catechol with the detection limits of 0.49 μM in range of 0–100 μM. Furthermore, the Janus micro-composite could decompose H2O2 to produce abundant reactive oxygen species (ROS) through Fenton reaction, which could promote the removal rate of catechol up to 96.6%. This is a promising micro-composite platform for integrated autonomous movement and simultaneous detection/removal of catechol, which can provide a new perspective for water environment treatment.