Mobile Robot

Most indoor positioning systems require calibration before use. Fingerprinting requires the construction of a signal strength map, while ranging systems need the coordinates of the beacons. Calibration approaches exist for positioning systems that use Wi-Fi, radio frequency identification or ultrawideband. However, few examples are available for the calibration of visible light positioning systems. Most works focused on obtaining the channel model parameters or performed a calibration based on known receiver locations. In this paper, we describe an improved procedure that uses a mobile robot for data collection and is able to obtain a map of the environment with the beacon locations and their identities. Compared to previous work, the error is almost halved. Additionally, this approach does not require prior knowledge of the number of light sources or the receiver location. We demonstrate that the system performs well under a wide range of lighting conditions and investigate the inf...

This research aimed to develop an autonomous mobile robot that helps various kinds of people. The evasion of obstacles is absolutely imperative so that the robot can act in a human-life environment. Therefore, we developed a robot that moves through doors and avoids obstacles with the help of images taken by a camera set on the robot.

La robotica movil ofrece una gran posibilidad de desarrollar aplicaciones en diversas areas de la vida cotidiana. Sin embargo, para que sea util en la mayoria de las aplicaciones, se debe contar con un mapa del entorno sobre el cual planificar trayectorias libres de colision. Este mapa permite no solo moverse en el espacio circundante sino identificar la posicion del robot basado en las caracteristicas del entorno. En los ultimos anos se han creado gran variedad de algoritmos para la realizacion del SLAM con la intencion de dar la capacidad de posicionar un robot en un entorno desconocido a la vez que se navega en el, dando como resultado tecnicas con muy buenos resultados y con un bajo costo computacional que permiten ser implementados en sistemas embebidos como los computadores de placa reducida. En este trabajo se hizo uso de las tecnicas de creacion de mapas por metodos probabilisticos que resultan ser un gran aporte a la solucion del problema del SLAM y como fue la creacion de ...

Metric maps provide a reliable basis for mobile robot navigation. However, such maps are in general quite resource expensive and do not scale very well. Aiming for a highly scalable map, we adopt theories of insect navigation to develop an algorithm which builds a topological map for global navigation. Similar to insect conduct, positions in space are memorized as snapshots, which are unique configurations of landmarks. Unlike conventional snapshot approaches, we do not simply store the landmarks as a set, but we build a landmark tree which enables us to easily free memory in case of a continuously growing map while still preserving the dominant information. The resulting navigation is not sensor specific and solely relies on the directions of arbitrary landmarks. The generated map enables a mobile robot to navigate between defined locations and let it retrace a previously pursued path. Finally, we verify the reliability of the Landmark-Tree Map (LT-Map) concept and its robustness on memory limitations.

In this paper, we consider a trajectory design problem of an autonomous mobile robot working in industrial environments. In particular, we formulate an optimization problem that jointly determines the trajectory of the robot and the time step duration to minimize the energy consumption without obstacle collisions. We consider both static and moving obstacles scenarios. The optimization problems are nonconvex, and the main contribution of this work proposing successive convex approximation (SCA) algorithms to solve the nonconvex problems with the presence of both static and moving obstacles. In particular, we first consider the optimization problem in the scenario with static obstacles and then consider the optimization problem in the scenario with static and moving obstacles. Then, we propose two SCA algorithms to solve the nonconvex optimization problems in both the scenarios. Simulation results clearly show that the proposed algorithms outperform the A* algorithm, in terms of energy consumption. This shows the effectiveness of the proposed algorithms.

In this paper, we focus on the problem of exploring an unknown environment by a team of mobile robots. The main objective is to compare four different coordination strategies based on frontier concept (boundaries between unexplored and explored open areas) and analyze their performance in term of assignment quality, overall exploration time and computational complexity. In order to provide a suitable qualitative study we used three optimization criteria. Each strategy has been implemented and tested extensively in computerized simulation.

This paper describes the development of a multi-robot system for Area Reduction in Humanitarian Demining. In spite of the specific requirements imposed to the work being carried out by the Humanitarian Demining domain, the results being achieved can also be used in other domains such as surveillance and remote monitoring. A multi-agent based architecture is responsible for coordinating a progressive stochastic analysis of the terrain. The all-terrain navigation used by the mobile robots is attained by a novel embodied reactive obstacle avoidance method. A Control Mission software has been developed to plan, configure and supervise the operations. Highly compliant legged and wheeled platforms have been developed accomplishing low-cost all-terrain robots. Digital signal processing algorithms have been applied for landmine detection using the payload sensors.

This paper describes the development of a multi-robot system for Area Reduction in Humanitarian Demining. In spite of the specific requirements imposed to the work being carried out by the Humanitarian Demining domain, the results being achieved can also be used in other domains such as surveillance and remote monitoring. A multi-agent based architecture is responsible for coordinating a progressive stochastic analysis of the terrain. The all-terrain navigation used by the mobile robots is attained by a novel embodied reactive obstacle avoidance method. A Control Mission software has been developed to plan, configure and supervise the operations. Highly compliant legged and wheeled platforms have been developed accomplishing low-cost all-terrain robots. Digital signal processing algorithms have been applied for landmine detection using the payload sensors.

Surveying has to do with the determination of the relative spatial location of points on or near the surface of surveyed area. It is the art of measuring horizontal and vertical distances, measuring angles between lines, of determining the direction of lines, and of establishing points by predetermined angular and linear measurements, along with the actual survey measurements. Survey data is portrayed graphically by the construction of maps, profiles and diagrams. This is about surveying process and it is same for regardless of coverage of large, medium or small area. 'Electronic Surveying' is the automated surveying process for relatively flat and large size indoor area. Word 'Electronic' implies to the fully automated process of measuring distances, storing the data, preparing and displaying a map and calculating further parameters. In either way Electronic surveyor is a typical embedded system, consist of microcontroller, memory, display and input device(s). Each element has its own special function(s) and all together accomplishes the process of surveying. An efficient algorithm drives whole surveying process.

The researches in the field of robotics technologies are set to improve the way intelligent processes accomplish their tasks and interact with the environmental world. An efficient mode for researching and testing the capabilities of the algorithms is putting the robots to play in different coordination games. The current project aims to set up an environment for Sony AIBO robots to play in a soccer game and a rescue game. In both games AIBO is assumed to detect and localize objects such as balls, shouting team players, landmarks and crying victims. The AIBO robot is able to sense the world with different modalities, i.e. sight, hearing and touch. The ultimate challenge of the paper is to fuse data from different communication channels. An additional approach is to program the robots for playing the Rescue game. The results relate to valuable information on common safety and emergency management issues.

Digital sentience is one of the most debated topics at the intersection of artificial intelligence, philosophy, ethics, and cultural thought. This paper examines the conceptual, ethical, and social dimensions of potential sentience in artificial systems. While current AI demonstrates advanced intelligence and the ability to simulate emotions, there is no empirical evidence of subjective awareness. Drawing on theoretical frameworks-including functionalism, Integrated Information Theory, Global Workspace Theory, and emergentist perspectives-this study explores the conditions under which digital sentience could be conceivable. Ethical implications, public perception, and religious interpretations are analyzed to highlight the challenges and responsibilities of human interaction with potentially sentient machines. Case studies of AI systems such as Sophia, LaMDA, and ChatGPT illustrate the gap between appearance and reality in digital consciousness. Ultimately, the paper emphasizes the need for interdisciplinary dialogue, precautionary ethical frameworks, and cultural sensitivity in preparing for possible future developments in AI sentience.

The Jet Propulsion Laboratory (JPL) has recently established an accelerated development initiative to enable high-resolution active optical ranging and terrain mapping capabilities for a series of upcoming Solar System exploration missions. Building on existing NASA airborne terrain mapping and spaceborne laser altimetry technologies, the JPL program seeks to develop a family of instruments which will serve the increasingly demanding navigational requirements of future robotic sciencecraft. Amongst the most challenging of these will be the Mars Sample Return (MSR) mission. The current MSR mission concept calls for the sampling of Martian terrain by two lander vehicles, followed by storage of the cached samples in Mars orbit and their eventual retrieval and return to Earth by a separate spacecraft. The complex rendezvous strategy that has been evolved to accomplish this goal places stringent demands on the autonomous navigation capabilities of the carrier vehicle and highresolution (-1 cm) acquisition and ranging lidar is a critical enabling component of the overall approach. In addition, risk mitigation provisions to be enacted under the re-architectured Mars Exploration Program will require increased assurance of safe landing conditions for future landed Mars missions. Consequently, we are also formulating designs for a terrain mapping lidar as part of an autonomous landing site selection system to be activated during the final stages of the entry/descent/landing operation on upcoming missions. These applications and others in solar system exploration will be described in this presentation.

The Jet Propulsion Laboratory (JPL) has recently established an accelerated development initiative to enable high-resolution active optical ranging and terrain mapping capabilities for a series of upcoming Solar System exploration missions. Building on existing NASA airborne terrain mapping and spaceborne laser altimetry technologies, the JPL program seeks to develop a family of instruments which will serve the increasingly demanding navigational requirements of future robotic sciencecraft. Amongst the most challenging of these will be the Mars Sample Return (MSR) mission. The current MSR mission concept calls for the sampling of Martian terrain by two lander vehicles, followed by storage of the cached samples in Mars orbit and their eventual retrieval and return to Earth by a separate spacecraft. The complex rendezvous strategy that has been evolved to accomplish this goal places stringent demands on the autonomous navigation capabilities of the carrier vehicle and highresolution (-1 cm) acquisition and ranging lidar is a critical enabling component of the overall approach. In addition, risk mitigation provisions to be enacted under the re-architectured Mars Exploration Program will require increased assurance of safe landing conditions for future landed Mars missions. Consequently, we are also formulating designs for a terrain mapping lidar as part of an autonomous landing site selection system to be activated during the final stages of the entry/descent/landing operation on upcoming missions. These applications and others in solar system exploration will be described in this presentation.

In this research the master-slave method implemented on an embedded system using 3 processor applied to the mobile robot, to know the speed of program execution of robot. As a comparison is also used a robot with an embedded system based on single processor. From the experimental results, by applying the slave master method obtained the execution time of 546,5 μs and the number of iteration 1079, while for single processor-based system obtained execution time average 67828 μs and the amount of iteration average 147 times. Where the number of iterations is obtained by running the robot for 10 s. From this experiment, it can be concluded that there is a performance increase of 7.3% when compared to embedded systems based on single processor. 

In the field of mobile robot navigation, challenges such as nonlinear conditions, uncertainties, and the development ‎of advanced methods have necessitated accurate position estimation. In this paper, fuzzy based adaptive ‎unscented Kalman filter (FAUKF) is evaluted to enhance the state estimation performance for mobile robot ‎localization. In proposed method, the FAUKF algorithm effectively removes the noise uncertainty by adaptively ‎adjusting the covariance of the measurement noise according to the adaptation law. The output of the Mamdani ‎Fuzzy Inference System (FIS) acts as an observer and improves the matching law. The results of this research show ‎the accuracy of FAUKF algorithm position estimation compared to traditional UKF methods. Also, this study ‎presents a pioneering navigation strategy for mobile robots, which integrates random tree routing algorithm with ‎rapid exploration (RRT*) for optimal path design in indoor environments. The goal of RRT* integration is to create ‎optimal routes taking into account safety and environmental constraints. By combining the Kalman filter ‎prediction and updating steps, this method reduces the accumulation of uncertainty during the localization process ‎and facilitates accurate localization and path planning towards the target.‎

This paper describes a simulation study of an Intersection Collision Warning System. For the study, an Inter-Vehicle Communication (IVC) Simulator has been developed. The IVC simulator is composed of vehicle traffic and wireless communication simulation modules . The simulator makes it possible to evaluate the vehicle collision issues that arise in the vicinity of an intersection and the impact of inter-vehicle communication on collisions. A collision warning system based on inter-vehicle communication is envisaged, and its performance is tested using the IVC simulator. The results show that such a warning system will be a viable way of decreasing intersection collisions.

This paper shows how to control a robot with omnidirectional wheels, using as example robots with four motors, and generalizing to n motors. More than three wheels provide redundancy: many combinations of motors speeds can provide the same Euclidean movement. Since the system is over-determined, we show how to compute a set of consistent and optimal motor forces and speeds using the pseudoinverse of coupling matrices. This approach allows us also to perform a consistency check to determine whether a wheel is slipping on the floor or not. We show that it is possible to avoid wheel slippage by driving the robot with a motor torque under a certain threshold or handle it and make high accelerations possible.

and key words This paper deals with the calibration of a cylindrical omnidirectional imaging system, based on a rotating 2048 pixels linear camera which provides high definition panoramas. The two-step algorithm relies on line segment projections as calibration features, which are sinusoidal curves. A cylindrical image line detection algorithm is proposed, based on the dual principle of the Hough transform. Moreover, the use of Plucker coordinates introduces some new characteristics in the calibration process. This kind of formalism allows a linearization of the cylindrical projection, which is non-linear in the usual way. Results obtained from this first step are used to evaluate one of the intrinsic parameters, the other one being determined by a linear criterion minimization in the dual space, ie the sine magnitude space. Non-Linear Calibration, Omnidirectional Imaging System, Plucker, Coordinates, Hough Transform.

Résumé et mots clés Cet article présente le calibrage d'un capteur cylindrique omnidirectionnel, qui fournit des panoramas Haute Résolution par la mise en rotation d'une caméra CCD linéaire. L'algorithme utilise les segments de droite pour primitives, dont les projections sont des composées de sinusoïdes, et est constitué de deux étapes. Un algorithme de détection et d'identification de l'image de droites est proposé, selon une méthode proche de la transformée de Hough. De plus, l'utilisation des coordonnées de Plücker permet la linéarisation de la projection cylindrique. Les résultats extraits de la première étape sont utilisés pour l'évaluation du premier paramètre intrinsèque. Le second est quant à lui déterminé par l'intermédiaire de la minimisation d'un critère linéaire dans l'espace d'accumulation.

We introduce and compare two algorithms related to ego-motion, applicable to a robot using a panoramic visual sensor in an unknown environment. The first method, computationally cheap, extends a family of bio-inspired navigation systems by providing an orientation estimation that previously had to be obtained from external references. The second method solves the general structurefrom-motion problem and is here adapted to the panoramic sensor case. Both methods produce good results in a test scene, though the calibrated method has a greater precision. A step-by-step comparison of the methods is included, with mobile robotics applications in mind.

This paper introduces an advanced predictive control algorithm tailored for spherical robots operating within obstacle-dense environments. The proposed strategy employs a sophisticated system model to forecast the robot's behavior over a defined future time horizon, capitalizing on the strengths of predictive control, which include accelerated convergence time, particularly beneficial in dynamic scenarios. We conduct a comprehensive comparative analysis between the predictive control approach and feedback linearization control, focusing on the robot's navigation through obstacles. Our findings indicate that the predictive control framework considerably improves the robot's overall motion performance, leading to shorter response times, enhanced convergence capabilities, and greater resilience when navigating obstacle-dense environments. Furthermore, this approach effectively minimizes control errors, achieving rapid convergence to zero. This research highlights the effectiveness of predictive control in optimizing the agility and accuracy of spherical robots in challenging operational settings. The proposed method represents the first algorithm capable of effectively addressing a comprehensive range of motion tasks for spherical robots. This includes trajectory tracking and both static and dynamic obstacle avoidance with global stability and optimal performance, which are essential for their development.

This paper introduces TORNADO, a cloudintegrated robotics platform designed to tackle the challenges of autonomous manipulation in dynamic indoor environments, particularly those involving small, soft, or deformable objects. TORNADO integrates large-scale foundation models for perception, language comprehension, and high-level reasoning, to achieve strong zero-shot generalization across a wide range of tasks. At its core, the platform features an adaptive cognitive pipeline capable of dynamically reconfiguring its modules-including semantic 3D SLAM, people-aware navigation, dexterous manipulation, and human-in-the-loop learning-to manage uncertainty and adapt to changing conditions. Additionally, TORNADO incorporates a multi-modal Learning-from-Demonstration interface and an Explainable AI engine, enhancing transparency and easing the burden of teaching new tasks. The system is validated through three industry-relevant scenarios: (1) flexible gear and ply-sheet handling in a mechanical parts factory, (2) patient support in a hospital palliative ward, and (3) product sampling and waste management in a distribution center. TORNADO aims to significantly enhance the agility, safety, and overall task performance of mobile manipulators operating in dynamic, human-centric environments.

Robots are increasingly becoming an essential part of human life. Their use in industries such as automobile manufacturing and food processing has significantly reduced labor costs. This study examines various types of quadruped robots, focusing on the design, simulation, and practical implementation of a walking quadruped robot. A key feature of this robot is its ability to walk smoothly across flat surfaces without interruption. Unlike wheeled robots, our design uses legs, which offer better performance in certain scenarios. The leg movements are controlled by servo motors, powered by lithium-ion batteries. During the design process, our main goals were high performance and low construction cost, making the robot more accessible for researchers and developers. In today's advanced society, we are progressing rapidly with the integration of artificial intelligence and automation technologies. These innovations are helping us lead smarter and more convenient lives. Among the many areas of AI and automation research, mobile robotics-particularly legged robots-has long been a topic of great interest.

Omni-directional mobile robot (OMR) is a robot that can move in all directions with an additional wheel around the core wheel. This research presents a PID controller method at the OMR plant with the specific purpose of getting to the specified point. OMR uses three wheels with an angle difference of 120 degrees. The application of this method using MATLAB assistance from knowing the kinematics to the performance results from the application of the control method. The stages of this research are robot design, component selection, electrical design of the robot, determination of forward kinematic and reverse kinematic and determination of PID controller for positioning. Testing is done by determining the position and determining the point to be determined. Simulation time testing is when the state is at 3,6,9,12 and 15 seconds. The results of the simulation robot can follow the specified coordinates.

For mobile robots, the essential units of actuation, computation, and sensing must be designed to fit within the body of the robot. Additional capabilities will largely depend upon a given activity, and should be easily reconfigurable to maximize the diversity of applications and experiments. To address this issue, we introduce a modular architecture originally developed and tested in the design and implementation of the X-RHex hexapod that allows the robot to operate as a mobile laboratory on legs. In the present paper we will introduce the specification, design and very earliest operational data of Canid, an actively driven compliant-spined quadruped whose completely different morphology and intended dynamical operating point are nevertheless built around exactly the same "Lab on Legs" actuation, computation, and sensing infrastructure. We will review as well, more briefly a second RHex variation, the XRL platform, built using the same components.

Nieautoryzowane rozpowszechnianie całości lub fragmentu niniejszej monografii w jakiejkolwiek formie jest zabronione. Wykonywanie kopii metodą kserograficzną, fotograficzną, a także kopiowanie książki na nośniku filmowym, magnetycznym lub innym powoduje naruszenie praw autorskich niniejszej publikacji. Monografia dostępna jest w formie papierowej i elektronicznej. Korzystanie z materiału ilustracyjnego zawartego w monografii wymaga każdorazowej zgody wydawcy -nie dotyczy materiału uzyskanego w ramach wolnego dostępu.  grantu dziekańskiego WIMiR AGH w Krakowie -15.11.130.969,  działalności statutowej KAP AGH w Krakowie -11.11.130.958,  działalności badawczej Politechniki Rzeszowskiej,  działalności badawczej Politechniki Świętokrzyskiej. Wszystkie występujące w tekście znaki towarowe i firmowe są zastrzeżonymi znakami firmowymi bądź towarowymi ich właścicieli i wymieniono je jedynie w celach informacyjnych.

Localization is a crucial skill in mobile robotics because the robot needs to make reasonable navigation decisions to complete its mission. Many approaches exist to implement localization, but artificial intelligence can be an interesting alternative to traditional localization techniques based on model calculations. This work proposes a machine learning approach to solve the localization problem in the RobotAtFactory 4.0 competition. The idea is to obtain the relative pose of an onboard camera with respect to fiducial markers (ArUcos) and then estimate the robot pose with machine learning. The approaches were validated in a simulation. Several algorithms were tested, and the best results were obtained by using Random Forest Regressor, with an error on the millimeter scale. The proposed solution presents results as high as the analytical approach for solving the localization problem in the RobotAtFactory 4.0 scenario, with the advantage of not requiring explicit knowledge of the exa...

We introduce a new family of geometric loci-S M Nazmuz Sakib Iso-Entropy Loci (SIELs)defined as the level sets of the Shannon entropy of normalized inverse-distance weights to a fixed set of anchors. Let F = {a 1 ,. .. , a k } ⊂ R n , a norm ∥ • ∥, and s > 0. For x / ∈ F , define A j (x) = ∥x-a j ∥-s , w j (x) = A j (x)/ i A i (x), and H s (x) =j w j (x) log w j (x). We study {x : H s (x) = h}, showing (i) translation/scale and norm-isometry invariances, (ii) a precise k = 2 characterization as two Apollonius loci (circles in R 2 with ℓ 2), and (iii) large-s concentration near Voronoi edges with a first-order thickness heuristic. New named results include the S M Nazmuz Sakib Two-Locus Theorem for k = 2, the Sakib Equalization Principle (entropy maximizers near equalized log-distances), and a normalized Sakib Constant C(F, s) = max x H s (x)/ log k ∈ (0, 1]. We provide schematic illustrations and contextualize SIELs against IDW interpolation [2], Apollonius/weighted Voronoi diagrams [11], differentiable Voronoi methods [6, 7], and Voronoi entropy (as a global statistic) [8].

We propose a fixed-lag smoother-based sensor fusion architecture to leverage the complementary benefits of rangebased sensors and visual-inertial odometry (VIO) for localization. We use two fixed-lag smoothers (FLS) to decouple accurate state estimation and high-rate pose generation for closed-loop control. The first FLS combines ultrawideband (UWB)-based range measurements and VIO to estimate the robot trajectory and any systematic biases that affect the range measurements in cluttered environments. The second FLS estimates smooth corrections to VIO to generate pose estimates at a high rate for online control. The proposed method is lightweight and can run on a computationally constrained micro-aerial vehicle (MAV). We validate our approach through closed-loop flight tests involving dynamic trajectories in multiple real-world cluttered indoor environments. Our method achieves decimeter-to-subdecimeter-level positioning accuracy using off-the-shelf sensors and decimeter-level tracking accuracy with minimally-tuned open-source controllers.

Autonomous mobile robots have transformed industries by enabling operations without human intervention. Fuzzy logic controllers (FLCs) are widely used in robots for path planning due to their capability to handle uncertainties. However, large and complex fuzzy rule sets often result in computational inefficiencies, leading to increased navigational time. This study proposes a similarity-based rule reduction method to optimize fuzzy inference systems. The approach leverages a similarity threshold algorithm to identify and merge redundant rules using a similarity index. The hypothesis suggests that simplifying the rule set will either sustain or improve the robot's task completion time. The study is validated through simulations of an FLC-based path planning nonholonomic wheeled mobile robot navigating in a static, unknown environment. The robot detects obstacles, localizes itself, and maps its surroundings in real-time to achieve effective navigation. The primary inputs are obstacle distances, and the output determines wheel velocity. MATLAB and CoppeliaSim are used to optimize and evaluate the robot's performance, with traversal time as the primary metric. The results show that cutting down the number of fuzzy logic rules shortened the robot's traversal time and boosted its processing speed and overall performance. By simplifying the rule base by nearly half (48.15%), the robot was able to complete its navigation tasks more quickly and efficiently. This demonstrates a practical way to tackle the long-standing issue of fuzzy rule complexity, making decisionmaking systems more adaptive and responsive. These results are particularly significant for real-time applications where speed and dependability are crucial, like robotics, driverless cars, and predictive mainte nance.

This paper involves the design and implementation of cell phone detection mobile robot. This is applicable in examination halls, private conferences and meeting rooms in which the use of cell phones is highly restricted. The robot is able to detect the presence of unauthorized communications by active phones and then distort them. The detection process is achieved by implementing an electrical circuit for sensing undesirable signals and using NodeMCU for interfacing the robot with a main computer unit. The main computer unit is used as a controlling platform in terms of mobile robot navigation as well as detection and jamming activating. This is accomplished by creating a graphical control panel programmed using a special HTML script. In this work, the cell phone detection mobile robot has been applied in an examination hall to simulate real educational environment. The robot has detected active calls by cell phones with a circle diameter of 1.2 meter and then jammed them directly. The whole area has been covered for detection and jamming by roaming the robot wirelessly via the remote main computer. Different voltage measurements for different detection distances have been recorded.

This paper involves the design and implementation of cell phone detection mobile robot. This is applicable in examination halls, private conferences and meeting rooms in which the using of cell phones is highly restricted. The robot is able to detect the presence of unauthorized communications by active phones and then distort them. The detection process is achieved by implementing an electrical circuit for sensing undesirable signals and using NodeMCU for interfacing the robot with a main computer unit. The main computer unit is used as a controlling platform in terms of mobile robot navigation as well as detection and jamming activating. This is accomplished by creating a graphical control panel programmed using a special HTML script. In this work, the cell phone detection mobile robot has been applied in an examination hall to simulate real educational environment. The robot has detected active calls by cell phones with a circle diameter of 1.2 meter and then jammed them directly...

The efficient implementation of on-line adaptation in real time is an important research problem in fuzzy control. The goal is to develop autonomous selforganizing controllers [5] employing system-independent control metaknowledge which enables them to adjust their control policies depending on the systems they control and the environments in which they operate. An autonomous fuzzy controller would continuously observe system behavior while implementing its control actions and would use the outcomes of these actions to refine its control policy. It could be designed to lie dormant when its control

In human-to-human communication, a handdrawn route map is often sketched to show a desired navigation path. In this paper, we describe a PDA sketching interface that can be used to direct a mobile robot along a specified path. Because sketched route maps are not drawn precisely or necessarily to scale, we do not attempt to analyze precise path information, but rather qualitative route information is extracted. The paper focuses on the front-end sketch understanding and includes a description of the interactive features, such as deleting, moving, and labeling landmarks. Results of a user evaluation are also presented, in which participants report that the sketching interface was as easy as using pencil and paper by a 2:1 margin.

In activity recognition, sensor based errors or uncertainties induce a wrong statement in the environment representation. To overcome these issues we introduce our ontology-based approach where a system, based on a mobile robot and an ambient system, is able to generate collaborative tasks when necessary to recognize activities. Using two ontologies to represent the environment, inconsistencies are detected and high level data are exchanged between both systems to correct and clarify ambiguous description of the environment. This process allows to generate adequate clarifying tasks by explicitly including knowledge and capabilities of each system. We expose our approach in two experiments where interaction between both systems are mandatory to respond a human "find an object" request in a first scenario and recognize multi user activities in a second scenario.

In activity recognition, sensor based errors or uncertainties induce a wrong statement in the environment representation. To overcome these issues we introduce our ontology-based approach where a system, based on a mobile robot and an ambient system, is able to generate collaborative tasks when necessary to recognize activities. Using two ontologies to represent the environment, inconsistencies are detected and high level data are exchanged between both systems to correct and clarify ambiguous description of the environment. This process allows to generate adequate clarifying tasks by explicitly including knowledge and capabilities of each system. We expose our approach in two experiments where interaction between both systems are mandatory to respond a human ”find an object” request in a first scenario and recognize multi user activities in a second scenario.

Nowadays electric vehicles (EV) utilization is increasing. Because of charging issues, EVs are troubling people at the time of the journey because of the lack of charging stations. Therefore, to overcome these issues, robotic arm position for automatic electric vehicle is introduced in this analysis. This vehicle is operated through solar, so charging issues are overcome. The robotic arm position for automatic electric vehicle is fully automated by 4 infrared radiation (IR) sensors, which are placed in variations, back and other sides with particular speed limit variations, so that accidents can be avoided. The Flux in hand gloves can operate without manual operation while driver is sleeping. This analysis uses Raspberry Pi, python software with machine learning (ML) algorithm (support vector machine). Hence, this robotic arm position for automatic electric vehicle shows better results in terms of charging issues, accident ratio and driver presence.

This work in progress paper presents an automated approach for network coverage prediction in real-world environments by combining mobile mapping, 3D mesh generation, and a ray launching based network simulator. We identify the challenges and demonstrate the functionality of such a pipeline. We preview an empirical evaluation in a realistic real-world environment.

Pada penelitian ini bertujuan mendesain navigasi gerak pada robot beroda menggunakan metode Fuzzy Logic Controller (FLC). Robot beroda tersebut memiliki masukan berupa 3 buah sensor ultrasonik dan keluaran berupa 2 buah motor DC. Masukan dan keluaran tersebut digunakan sebagai variabel-variabel FLC. Variabel masukannya adalah sensor F (depan), sensor R (kanan) dan sensor L (kiri) yang masing-masing memiliki 3 himpunan Membership Function (MF) yaitu sangat dekat, dekat dan jauh. Variabel keluarannya adalah motor R (kanan) dan motor L (kiri) yang masing-masing memiliki 4 himpunan MF yaitu berhenti, pelan, sedang dan cepat. Jumlah if then rule dalam Fuzzy Inference System (FIS) yang digunakan adalah 27 buah rule. Pengujian dilakukan sebanyak 4 studi kasus pergerakan robot beroda yaitu bergerak lurus, belok kanan, belok kiri dan berhenti. Pengujian tersebut berhasil membuktikan bahwa FLC dapat mengendalikan gerak robot beroda.

Path planning is general problem of mobile robots, which has special characteristics when applied to marine applications. In addition to avoid colliding with obstacles, in marine scenarios, environment conditions such as water currents or wind need to be taken into account in the path planning process. In this paper, several solutions based on the Fast Marching Method are proposed. The basic method focus on collision avoidance and optimal planning and, later on, using the same underlying method, the influence of marine currents in the optimal path planning is detailed. Finally, the application of these methods to consider marine robot formations is presented.

The Fast Marching based algorithm proposed here solves the problem of finding Feedback Control Laws for mobile robots, including nonholonomic vehicles. It integrates in a single Real Time Controller the global motion planning tasks and the collision avoidance capabilities required to efficiently move a mobile robot in a dynamic environment. The solution proposed is fast enough to be used in real-time and to operate with a laser scanner system at the sensor rate frequency. The method combines map-based and sensor-based planning operations to provide a smooth and reliable motion plan. The method works in two steps: In the first, it uses a Fast Marching technique to propagate a wave from the walls and obstacles to determine a potential of slowness for the robot. In the second step, this slowness map is used as refractive index, to calculate the potential of the propagation of a wave from the robot pose to the goal with time as the last axis. The generated trajectory corresponds to the path of the light ray through a medium with non-homogeneous refraction index. The robot trajectory is calculated on the vector field associated to the potential surface. The computational efficiency of the method allows the planner to operate at high rate sensor frequencies. For small and medium scale environments, the proposed method avoids the need for a collision avoidance algorithms plus a global motion planner. Since the method works over a smooth vector field, it allows the simple introduction of nonholonomic constraints. This way, the method can be used directly to develop a control scheme for nonholonomic vehicles, for example for car like robots. This enables simplification of the mobile robot architectures, while maintaining good time response, smooth and safe planned trajectories with continuous curvature. The trajectory generated by the planner is the fastest possible to reach the goal position, considering the best path according to the maximum acceptable velocity at each point in the trajectory (path plus velocity).

This paper describes a robust algorithm for mobile robot formations based on the Voronoi Fast Marching path planning method. This is based on the propagation of a wave throughout the model of the environment, the wave expanding faster as the wave's distance from obstacles increases. This method provides smooth and safe trajectories and its computational efficiency allows us to maintain a good response time. The proposed method is based on a local-minima-free planner; it is complete and has an O(n) complexity order where n is the number of cells of the map. Simulation results show that the proposed algorithm generates good trajectories.

In this paper, a new methodology to build compact local maps in real time for outdoor robot navigation is presented. The environment information is obtained from a 3-D scanner laser. The navigation model, which is called traversable region model, is based on a Voronoi diagram technique, but adapted to large outdoor environments. The model obtained with this methodology allows a definition of safe trajectories that depend on the robot's capabilities and the terrain properties, and it will represent, in a topogeometric way, the environment as local and global maps. The application presented is validated in real outdoor environments with the robot called GOLIAT.

It is necessary to determine the robot's pose to localize a mobile robot in a known environment. If there is no information about the initial location, we are talking about global localization. In our previous work we solved this problem in a two-dimensional environment with an algorithm known as Evolutive Localization Filter (ELF). Based on evolutionary computation concepts, the proposed algorithm searches stochastically along the state space for the robot's best pose estimate. In this paper we have improved our method designing an algorithm with new features, called ELF-3D, that uses three-dimensional sensorial information. The resulting global localization module has been tested in a simulated indoor environment. The most important improvement obtained is the accuracy of the method, allowing to apply it in manipulation tasks.

Despite advances in autonomous robotics, the existing pathfinding algorithms used for search and rescue (SAR) still struggle to adapt in dynamic environments, resulting in delayed response and inefficient navigation. This study focuses on the development and implementation of a novel adaptive pathfinding algorithm in a simulated environment with improved real-time decision-making, route optimization, and adaptability to obstacles and dynamic terrain, that aims to enhance the performance of robotic agents in search and rescue scenarios. The newly developed pathfinding algorithm was tested in independent and cooperative role-based agent systems across varying robot counts, victim counts, and grid sizes. The results were then analyzed using a two-way ANOVA, which revealed that robot count and grid size significantly affected rescue completion time, and cooperative role-based agents outperformed independent ones, particularly in complex environments. The findings of this study demonstrate a significant improvement in navigation speed, obstacle avoidance, and agent coordination that contributes to the development of more reliable autonomous rescue operations and offers a scalable approach for implementing adaptive pathfinding in future SAR robotics, particularly in real-world disaster scenarios.

This paper explores the problem of influencing the environment by a group of autonomous robots through the creation and use of road infrastructure. The model object is ant roads (trails). We identify the main aspects of the behavior of different ant species in the process of collective foraging, and actions that together lead to the appearance of a phenomenon that the observer perceives as an ant road. We develop and describe an animat behavior model in the process of arranging a route. We define a list of mechanisms, a set of sensory capabilities, and manipulators that are necessary for the robot to implement options for arranging the route. The results of simulation modeling for solving the foraging problem with route clearing are consistent with theoretical models. The simulation results confirm our assumption that the route arrangement can be carried out by individual efforts of animats (robots) and without the need to organize joint actions.