Autonomous Mobile Robot Research Articles

Fuzzy Method for Preliminary Classification of the Micro-Electromechanical Systems Data

Data of micro-electromechanical systems (sensors) are widely used in modern multipurpose intellectual environments, autonomous mobile robots and automated systems possessing intellectual components. Since the amount of data delivered by the sensors is rather considerable and their significance varies we propose a fuzzy method of the data preliminary classification aimed at selection of the data important for the planning and control decisions while meaningless data or data that do not contain new information are discarded. As a result the amount of the resource intensive intellectual processing and the workload in the network infrastructure could be reduced significantly. The paper presents the analysis for the preliminary classification problem, the abstract model of a sensor and classification method. The method is based on the delay which is controlled by multiplicative decrease additive increase rule and expresses the expectation of the context uncertainty. The delay decreases if the variables measured by the sensor presumably change and increases if it keep its value. Numerical examples are presented as well. They consider the set of mount chest accelerometer uncalibrated measurements. We compare source data set and set of the selected values formed by an algorithm implementing fuzzy preliminary classification. Several metrics were used to compare the selected values with the source data and with CMA filter as well.

Bausteinkasten zur Einführung flexibler mobiler Robotik

Abstract Autonomous mobile robots are a key factor in maintaining the competitiveness of companies in Germany. However, their implementation and operation in brownfield environments pose various challenges for small and medium-sized enterprises. The FlexTools research project aims to help these companies with a modular toolbox to facilitate the customization and operation of autonomous mobile robots.

A Target Collision Algorithm of Autonomous Mobile Robot for Two-player Competition

Abstract A variety of robot competitions are held to train and attract students everywhere. This paper studies the algorithm of autonomous robot with two-player competition. The autonomous robots in a two-player competition takes the scoring form of collision and wins first. The impact object is the enemy target. The key algorithms in a two-player competition are target detection, target path decision, and collision target motion control. First, the vehicle target data set was established and enhanced, the lightweight detection algorithm model was screened and trained, the model pruning optimization experiment and the model deployment optimization experiment were carried out, and then the TensorRT framework was used for inter-layer fusion and data quantification processing. After hitting the target path decision algorithm after detecting the target target, it finally integrates the decision in simple and complex situations. The collision target motion control is the scheme of more intelligent and self-adjustable parameters of fuzzy Radial Basis Function (RBF) network Proportional Integral Derivative (PID) controller. Finally, the collision target implementation algorithm is tested in a real environment, and the results show that the task requirements can be met.

Real-time deep learning-based position control of a mobile robot

This study uses PID (Proportional-Integral-Derivative), fuzzy logic, and deep learning algorithm to experimentally achieve real-time position control of a four-wheel-drive symmetric autonomous mobile robot whose design and prototype are realized. At the same time, the convolutional neural network-based YOLO (You only look once) algorithm is used to detect and get around obstacles by classifying the items in front of it during the robot's movement, to get the robot to reach the position specified as a reference a fuzzy logic controller is created. As the robot can be used outside, It is necessary to recognize more than one type of obstacle. For this reason, YOLO training is conducted to classify eighty objects that the robot may encounter in the external environment, such as cats, dogs, people, and chairs. In this research, a single obstacle is studied and it is determined as a human class obstacle, which is one of the obstacles that the robot is most likely to encounter in the laboratory environment. While the target location points are sent to the robot with the designed Android application, all the controls needed by the robot are carried out by the microcontrollers on it, independent of any computer. As a result of the experimental studies, it is seen that people are detected in real time with YOLO within the range of 90 cm–130 cm specified in the algorithm, without any problems. In addition, the mobile robot reached the target points sent to it from the Android application without any errors by overcoming obstacles within the specified approach distance (0.05 m or 0.1 m). While the maximum mean absolute error in the speed controls made on the motors throughout all experimental studies is 0.351 rpm, the Robot moved with a maximum absolute linear speed error of 2.8 mm/s. This shows that the robot is successfully controlled with fuzzy and PID controllers in line with the information obtained with YOLO.

Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments

Autonomously making a map, localizing within it, and planning with it are fundamental problems in mobile robotics. Every autonomous mobile robot system must include a solution to all three problems. These three problems are interconnected, with simultaneous localization and mapping (SLAM) being a well-known issue. However, there is indeed a growing and developing realization in the research field that path planning how a robot goes about mapping and finding an environment (and then operating in the environment such as starting to the destination point) can avoid degenerate conditions and greatly reduce SLAM complexity. In this paper, the implementation of an autonomous mobile robot system for indoor environments using open-source ROS packages and a combination of cartography algorithm and adaptive Monte Carlo localization (AMCL) algorithms has been implemented. The system addresses the challenge of developing three components such as mapping, localization, and path planning systems for indoor autonomous mobile robots. The mapping module creates a global map using the cartography ROS package and SLAM algorithm. The localization module estimates the robot's pose using the AMCL approach. The planning module generates collision-free trajectories and control commands using the moving base ROS package. The experimental results demonstrate the effectiveness of this approach and its valuable contribution to the robotics field. The cartography algorithm mapping algorithm generates accurate and reliable maps, while the localization algorithm successfully determines the robot's position with good performance. Additionally, the path planning algorithm effectively avoids both static and dynamic obstacles, ensuring smooth navigation in the environment.

An Adaptive and Automatic Power Supply Distribution System with Active Landmarks for Autonomous Mobile Robots.

With the development of automation and intelligent technologies, the demand for autonomous mobile robots in the industry has surged to alleviate labor-intensive tasks and mitigate labor shortages. However, conventional industrial mobile robots' route-tracking algorithms typically rely on passive markers, leading to issues such as inflexibility in changing routes and high deployment costs. To address these challenges, this study proposes a novel approach utilizing active landmarks-battery-powered luminous landmarks that enable robots to recognize and adapt to flexible navigation requirements. However, the reliance on batteries necessitates frequent recharging, prompting the development of an automatic power supply system. This system integrates omnidirectional contact electrodes on mobile robots, allowing to recharge active landmarks without precise positional alignment. Despite these advancements, challenges such as the large size of electrodes and non-adaptive battery charging across landmarks persist, affecting system efficiency. To mitigate these issues, this research focuses on miniaturizing active landmarks and optimizing power distribution among landmarks. The experimental results of this study demonstrated the effectiveness of our automatic power supply method and the high accuracy of landmark detection. Our power distribution calculation method can adaptively manage energy distribution, improving the system's persistence by nearly three times. This study aims to enhance the practicality and efficiency of mobile robot remote control systems utilizing active landmarks by simplifying installation processes and extending operational durations with adaptive and automatic power supply distribution.

Towards smooth mobile robot deployments in dynamic human environments

AbstractRecently, there has been great interest in deploying autonomous mobile robots in airports, malls, and hospitals to complete a range of tasks such as delivery, cleaning, and patrolling. The rich context of these environments gives rise to highly unstructured motion that is challenging for robots to anticipate and adapt to. This results in uncomfortable and unsafe human–robot encounters, poor robot performance, and even catastrophic failures that hinder robot acceptance. Such observations have motivated my work on social robot navigation, the problem of enabling robots to navigate in human environments while accounting for human safety and comfort. In this article, I highlight prior work on expanding the classical autonomy stack with mathematical models and algorithms designed to contribute towards smoother mobile robot deployments in complex environments.

Minimal perception: enabling autonomy in resource-constrained robots.

The rapidly increasing capabilities of autonomous mobile robots promise to make them ubiquitous in the coming decade. These robots will continue to enhance efficiency and safety in novel applications such as disaster management, environmental monitoring, bridge inspection, and agricultural inspection. To operate autonomously without constant human intervention, even in remote or hazardous areas, robots must sense, process, and interpret environmental data using only onboard sensing and computation. This capability is made possible by advancements in perception algorithms, allowing these robots to rely primarily on their perception capabilities for navigation tasks. However, tiny robot autonomy is hindered mainly by sensors, memory, and computing due to size, area, weight, and power constraints. The bottleneck in these robots lies in the real-time perception in resource-constrained robots. To enable autonomy in robots of sizes that are less than 100 mm in body length, we draw inspiration from tiny organisms such as insects and hummingbirds, known for their sophisticated perception, navigation, and survival abilities despite their minimal sensor and neural system. This work aims to provide insights into designing a compact and efficient minimal perception framework for tiny autonomous robots from higher cognitive to lower sensor levels.

Limit Cycles of the Three-dimensional Quadratic Differential System via Hopf Bifurcation

تم في هذه الدراسة النظر في النظام التفاضلي التربيعي ثلاثي الأبعاد، حيث يصبح نقطة الأصل الإحداثيات نقطة توازن هوبف. تم دراسة وجود واستقرار الدورات النهائية التي تنبثق من نقطة هوبف. يتم حساب معاملات ليبانوف المرتبطة بنقطة هوبف باستخدام طريقة الإسقاط. أولاً، تم تحديد أربع عائلات من شروط المعلمات التي من خلالها يمكن للنظام التفاضلي التربيعي ثلاثي الأبعاد أن يظهر البعد الثالث لتشعب هوبف. تم إعطاء الدليل التحليلي لكل مجموعة من الحالات المعلمية عن طريق حساب معاملات ليبانوف، تصفير لقيمة معاملات ليبانوف الأولى و الثاني و غيرالصفري لمعاملات ليبانوف الثالثة. تم تقديم الشروط الواضحة لوجودية واستقرارية ثلاث دورات النهائية ناشئة عن كل عائلة من تشعبات هوبف. يُظهر ناتج الوجود نقطة هوبف مستقرة (غير مستقرة)، مصحوبة بظهور دورتين حديتين مستقرتين (غير مستقرة) جنبًا إلى جنب مع دورة حدية واحدة غير مستقرة (مستقرة) في جوار النقطة الأصل غير المستقر (المستقر) لنظام المعادلة التربيعية ثلاثية الأبعاد. بالإضافة إلى ذلك، يتم استخدام النتيجة لاستكشاف الدورات النهائية لنظام الجذب الفوضوي n-scroll، والذي له العديد من الاستخدامات العملية، بما في ذلك الاتصال الآمن، والتشفير، وتوليد الأرقام العشوائية، والروبوتات المتنقلة المستقلة. تم اشتقاق الشروط التي بموجبها تصبح نقطة الأصل لهذا النظام هي نقطة هوبف، ويمكن أن توجد ثلاث دورات نهائية حول نقطة هوبف. وأخيرًا، توضح العروض العددية أن النظام يخضع لتشعب هوبف فوق الحرج، مما يؤدي إلى دورتين حديتين مستقرتين وواحدة غير مستقرة. وعلاوة على ذلك، تم التحقق من كافة النتائج.

A hybrid sampling-based RRT* path planning algorithm for autonomous mobile robot navigation

The path-planning algorithms for autonomous mobile robot navigation are crucial, often relying on sampling-based methods. RRT* is a robust, sampling-based path planning algorithm. The sampling process in RRT* plays a pivotal role, where uniform sampling can lead to slow convergence, while non-uniform sampling offers faster convergence but may struggle in complex environments due to its limited exploration. Thus, achieving a balance between exploitation and exploration is essential when selecting the sampling method for the RRT* path-planning algorithm. To address this issue, this research paper introduces Hybrid-RRT*, a path planning method that utilizes hybrid sampling. This unique approach generates samples using both non-uniform and uniform samplers. Hybrid-RRT* is evaluated against three baseline path planning algorithms—RRT*-N, Informed RRT*, and RRT*—across three different 384x384 2D simulation environments. Compared to these baseline methods, Hybrid-RRT* achieves superior results across all five performance metrics: convergence rate, success rate, number of nodes visited, path length, and planning time. According to the numerical results, the proposed algorithm achieves a faster average convergence rate that is 76.14% higher than RRT*, 24% higher than Informed RRT*, and 3.33% higher than RRT*-N. Moreover, it reduces node exploration by an average of 48.53% compared to RRT* and 40.83% compared to Informed RRT*. The simulation results demonstrate that the proposed Hybrid-RRT* algorithm effectively addresses the issue of slow convergence with uniform sampling and the challenge of limited exploration with non-uniform sampling methods.

Advancements in Autonomous Mobile Robot: A Holistic Review of Obstacle Avoidance Methods

The emergence of AMRs has altered our perspective and relationship with automation. At the heart of this transition is navigation and obstacle avoidance, both of which are important needs for deploying AMRs in a variety of scenarios. This comprehensive review looks at the latest advances in navigation and collision avoidance for AMRs, including a wide range of modern techniques and methodologies, algorithms, and technologies that aim to improve functionality. The study provides a detailed analysis of known approaches, such as rule-based approaches, potential fields, reactive navigation systems as behavior systems, and path-following algorithms, that have been developed to address the difficulty in practice. In contrast, technological advancements in machine learning, computer vision sensor fusion, and SLAM techniques, as well as edge computing, are reviewed in light of their unprecedented impact on AMR navigation. Global and local techniques are tackled using universal worldwide optics as well as national adaptations that reveal the unique characteristics of individual countries. The Data Analysis and Processing section emphasizes the importance of technologies that define AMR performance. Due to the constraints imposed by previous studies, it is clear that additional research is required to focus on closing gaps in controlled environments and using standard benchmarks; sensor heterogeneity issues; and practical implementation of theoretical aspects. In a nutshell, this review provides a map of the complex world of AMR navigation and obstacle avoidance. Its primary purpose is to contribute to the continuing debate, promote innovation, and suggest new research avenues in a fast-changing world of autonomous mobile robotics that breaks down traditional deployment constraints.

Real-Time Indoor Visible Light Positioning (VLP) Using Long Short Term Memory Neural Network (LSTM-NN) with Principal Component Analysis (PCA).

New applications such as augmented reality/virtual reality (AR/VR), Internet-of-Things (IOT), autonomous mobile robot (AMR) services, etc., require high reliability and high accuracy real-time positioning and tracking of persons and devices in indoor areas. Among the different visible-light-positioning (VLP) schemes, such as proximity, time-of-arrival (TOA), time-difference-of-arrival (TDOA), angle-of-arrival (AOA), and received-signal-strength (RSS), the RSS scheme is relatively easy to implement. Among these VLP methods, the RSS method is simple and efficient. As the received optical power has an inverse relationship with the distance between the LED transmitter (Tx) and the photodiode (PD) receiver (Rx), position information can be estimated by studying the received optical power from different Txs. In this work, we propose and experimentally demonstrate a real-time VLP system utilizing long short-term memory neural network (LSTM-NN) with principal component analysis (PCA) to mitigate high positioning error, particularly at the positioning unit cell boundaries. Experimental results show that in a positioning unit cell of 100 × 100 × 250 cm3, the average positioning error is 5.912 cm when using LSTM-NN only. By utilizing the PCA, we can observe that the positioning accuracy can be significantly enhanced to 1.806 cm, particularly at the unit cell boundaries and cell corners, showing a positioning error reduction of 69.45%. In the cumulative distribution function (CDF) measurements, when using only the LSTM-NN model, the positioning error of 95% of the experimental data is >15 cm; while using the LSTM-NN with PCA model, the error is reduced to <5 cm. In addition, we also experimentally demonstrate that the proposed real-time VLP system can also be used to predict the direction and the trajectory of the moving Rx.

Towards Semantic Interoperability: An Information Model for Autonomous Mobile Robots

The collaboration among autonomous mobile robots (AMRs), including unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and/or unmanned surface vehicles (USVs), significantly enhances their capabilities by enabling them to tackle more complex tasks exceeding those of individual robots. However, to fully exploit this collaboration, a common understanding of exchanged information—referred to as semantic interoperability—is crucial. Achieving semantic interoperability between these robots requires a deep understanding of relevant information and its underlying structure. To address this challenge, this paper presents a platform- and technology-independent information model developed specifically for AMRs. This model aims to facilitate collaboration by structuring information in a way that ensures semantic interoperability. The paper outlines the model's development process, beginning with a structured consolidation of information from pertinent scientific literature, resulting in a foundational framework for representing knowledge and semantics within the domain of AMRs. The practical application of the information model is demonstrated through a use case involving multiple AMRs. Additionally, the paper provides insights into the employed methodology, emphasizing the significance of systematic literature reviews and collaboration with practitioners to refine and validate the model. It also discusses theoretical and practical implications, addressing potential limitations encountered during the research.

Dynamic and probabilistic safety zones for autonomous mobile robots operating near humans

The inefficiency of maintaining static and long-lasting safety zones in environments where actual risks are limited is likely to increase in the coming decades, as autonomous systems become more common and human workers fewer in numbers. Nevertheless, an uncompromising approach to safety remains paramount, requiring the introduction of novel methods that are simultaneously more flexible and capable of delivering the same level of protection against potentially hazardous situations. We present such a method to create dynamic safety zones, the boundaries of which can be redrawn in real-time, taking into account explicit positioning data when available and using conservative extrapolation from last known location when information is missing or unreliable. Simulation and statistical methods were used to investigate performance gains compared to static safety zones. The use of a more advanced probabilistic framework to further improve flexibility is also discussed, although its implementation would not offer the same level of protection and is currently not recommended.

Navigation benchmarking for autonomous mobile robots in hospital environment

The widespread adoption of robotic technologies in healthcare has opened up new perspectives for enhancing accuracy, effectiveness and quality of medical procedures and patients’ care. Special attention has been given to the reliability of robots when operating in environments shared with humans and to the users’ safety, especially in case of mobile platforms able to navigate autonomously. From the analysis of the literature, it emerges that navigation tests carried out in a hospital environment are preliminary and not standardized. This paper aims to overcome the limitations in the assessment of autonomous mobile robots navigating in hospital environments by proposing: (i) a structured benchmarking protocol composed of a set of standardized tests, taking into account conditions with increasing complexity, (ii) a set of quantitative performance metrics. The proposed approach has been used in a realistic setting to assess the performance of two robotic platforms, namely HOSBOT and TIAGo, with different technical features and developed for different applications in a clinical scenario.

FPGA Implementation of Pillar-Based Object Classification for Autonomous Mobile Robot

FPGA Implementation of Pillar-Based Object Classification for Autonomous Mobile Robot

Fiducial Markers and Particle Filter Based Localization and Navigation Framework for an Autonomous Mobile Robot

Fiducial Markers and Particle Filter Based Localization and Navigation Framework for an Autonomous Mobile Robot

From muscular to dexterous: A systematic review to understand the robotic taxonomy in construction and effectiveness

AbstractThis work presents an investigation of robotic technologies' effectiveness in construction activities. Sixty‐four highly relevant publications were identified from the database. By systematically reviewing the publications, the secondary data that are of interest to the review theme were retrieved and further evaluated. It is found that robotic technologies for automated construction is a growing field, where the taxonomy of robot was reflected in a diversified manner in the existing studies, ranging from the muscular guy—robotic manipulator—to the dexterous ones—unmanned aerial vehicle, autonomous mobile robot, automated guided vehicle, autonomous construction machinery and quadruped robot. In addition, the existing studies have provided substantial evidence to reveal the robotic technologies' effectiveness against traditional human methods in construction scenarios, and the measures for effectiveness consisted of productivity, precision, and success rate. With the evidence, it seems that the construction sector could benefit from robotic technologies to achieve intelligent workflows. Furthermore, based on the existing knowledge foundation in the current literature, a theoretical framework for future research direction is proposed. The framework envisages the integration of large models with construction robots to address operational inefficiencies, reduce costs, and simplify management.

Customizable 3D-printed decoupled structural lithium-ion batteries with stable cyclability and mechanical robustness

Structural batteries are considered one of the promising strategies for improving the endurance of electric vehicles. However, the trade-off between load-bearing capability and electrochemical performance remains a significant challenge. This paper proposes an efficient 3D printing-assisted fabrication strategy for high-performance structural batteries with customizable geometric configurations. The composite structural battery sample shows a bending modulus of 24.5 GPa, which could withstand maximum tensile stress and three-point bending stress of 155 MPa (specific tensile strength of 88340 N m kg−1) and 123 MPa (specific bending strength of 61553 N m kg−1). Meanwhile, it can achieve a high energy density of 120 Wh kg−1 and 210 Wh L−1 (3.5 mA cm−2) and superior capacity retention of up to 92 % after 500 cycles (10.5 mA cm−2). More importantly, the in-situ mechanical-electrochemical test demonstrates exceptional performance, retaining an ultra-high capacity of 98.7 % under a tensile stress of 80 MPa, and maintaining a capacity retention rate of 97 % with an average capacity decay per cycle of only 0.18 % under a bending stress of 96.3 MPa. In addition, finite element analysis is also used to verify the failure mechanism of the battery under bending load. Meanwhile, the fabricated structural battery can be applied to autonomous mobile robots, showing the multifunction energy storage and load-bearing. As a result, this work showcases the great potential of incorporating high-performance structural batteries into engineering applications such as small-scale warehousing, logistics equipment, and intelligent robotics.

Part feeding scheduling for mixed-model assembly lines with autonomous mobile robots: benefits of using real-time data

ABSTRACT Mixed-model assembly is increasingly widespread to meet customer requirements for customisation and short delivery times. Flexible part feeding systems are required to timely replenish assembly stations with materials, avoid station idle times, and limit inventory levels on the shop floor. Part feeding scheduling is a complex and dynamic problem, affected by processing time fluctuations, equipment failures, and variations of product mix. Although real-time data of factory processes and resources is widely available and can be exploited using a digital twin of the part feeding system, there is a lack of scientific evidence on the benefits of using real-time data in part feeding scheduling. This research addresses this gap by developing an agent-based simulation model of a part feeding system with a fleet of autonomous mobile robots (AMRs) and comparing a real-time dynamic part feeding scheduling approach with static benchmark approaches. Numerical results indicate that using real-time data improves the performance of the part feeding system and the assembly system significantly, and allows improving the trade-off between the AMR fleet size and the total storage capacity on the shop floor, resulting in lower investment costs for AMRs given a certain storage capacity or lower required storage capacity given an AMR fleet.