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Motion control of mobile robots in a cluttered environment with obstacles is an important problem. It is unsatisfactory to control a robot’s motion using traditional control algorithms in a complex environment in real time. Gaze tracking technology has brought an important perspective to this issue. Gaze guided driving a vehicle based on eye moveme...
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Context 1
... the proposed method is powerful to determine robot speed, orientation, and obstacle avoidance. In Figure 13, the simulation of the robot's initial and goal points is illustrated and robot motion data on this behavior is also shown in Table 3. Figure 13. Robot trajectory from eye gaze based go-to-goal behavior. ...
Context 2
... the proposed method is powerful to determine robot speed, orientation, and obstacle avoidance. In Figure 13, the simulation of the robot's initial and goal points is illustrated and robot motion data on this behavior is also shown in Table 3. Figure 13. Robot trajectory from eye gaze based go-to-goal behavior. ...
Citations
... In experimental psychology, eye movements serve as a powerful tool for investigating various psychological processes, including language processing, image processing, auditory processing, memory, social cognition, and decisionmaking, in an unobtrusive and accurate manner [9]. In the field of human-machine interaction, gaze tracking technology proves highly useful for predicting people's intentions [10], or for remotely controlling pointers or vehicles [11], thereby facilitating seamless collaboration between machines and humans. ...
Face detection and recognition play pivotal roles across various domains, spanning from personal authentication to forensic investigations, surveillance, entertainment, and social media. In our interconnected world, pinpointing an individual’s identity amidst millions remains a formidable challenge. While contemporary face recognition techniques now rival or even surpass human accuracy in critical scenarios like border identity control, they do so at the expense of poor explainability, leaving the underlying causes of errors largely unresolved. Moreover, they demand substantial computational resources and a plethora of labeled samples for training. Drawing inspiration from the remarkably efficient human visual system, particularly in localizing and recognizing faces, holds promise for developing more efficient and interpretable systems, with high gains in scenarios where misidentification can yield grave consequences. In this context, we introduce the Uniss-FGD dataset, which captures gaze data from observers presented with facial images depicting diverse expressions. In view of the potential uses of Uniss-FGD, we propose two baseline experiments on a subset of the dataset in which we perform a comparative analysis juxtaposing the attention mechanisms of ViTs, multi-scale handcrafted features, and human observers when viewing facial images. These preliminary comparisons pave the way to future investigation into the integration of human attention dynamics into advanced and diverse image analysis frameworks. Beyond the realms of Computer Science, numerous research disciplines stand to benefit from the rich gaze data encapsulated in this dataset.
... Various researchers have published their works on numerous methods or techniques that are used in navigating AMRs working on different environments, as explained in [16][17][18][19][20]. Kovacs et al. [21] introduced the novel Arti cial Potential Field (APF) algorithm, where information about the velocity and orientation of the domestic animals has been studied for constructing different paths in a household environment. ...
... This algorithm has certain shortcomings, such as, sudden variations, moving obstacles, oscillations, and many other factors. In order to overcome these di culties, Dirik et al. [17] designed a type-2 fuzzy rule based navigational techniques in outdoor environments. More recently, P.B. Kumar et al. [20] focused on designing the techniques for the path planning as well as navigation of mobile robots by applying regression analysis. ...
... From Table 11, it can be deducted that A* algorithm travels 810.22 cm for Type-1, 878.77 cm for Type-2, and 926.75 cm for Type-3 dense environments since this algorithm does not incessantly produces the optimal path as there are certain disturbances are occurred in front of 3D obstacle. In the FLC algorithm [17], fuzzy rules are designed in such a way that the optimal decisions such as left and right turn are taken for avoiding the obstacle and searching the best optimal path that needs expertise of the making of rules. The average path length for all Type-1 and Type-2 dense environment is around 730 cm whereas in Type-3 environment records 841 cm since the obstacles are arranged in scattered form. ...
In this paper, a unique Machine Learning (ML) model namely, Adaptive Block Coordinate Descent Logistic Regression (ABCDLR), is proposed for segregating the movement of an Autonomous Mobile Robot (AMR) by framing it as three class problem, i.e., no, left, and right turn. The velocities of the left and right wheels, as well as the distance of the obstacle from AMR, are collected in real time by two Infrared (IR) and one Ultrasonic (US) sensors, respectively. The performance of the proposed algorithm is compared with three other state-of-the-art ML algorithms, such as, K-Nearest Neighbour (KNN), Naïve Baiyes, and Gradient Boosting, for obstacle avoidance by AMR; considering the accuracy, sensitivity, specificity, precision values for three different speed conditions, i.e., low, medium, and high. Various Logistic Regression (LR) model parameters, such as, pseudo R-squared (R2), Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), LL-null, and Log-Likelihood Ratio (LLR) are considered to investigate the performance of the proposed ABCDLR model. Furthermore, the proposed model has been applied for path planning in three different types of dense environments, and its performance is compared with four other competitive path planning approaches, such as, A*, Fuzzy Logic Controller(FLC), Vector Field Histogram(VFH) and ASGDLR.
... In terms of path tracking, i.e., where a reference trajectory is known beforehand, there are approaches based on predictive control [20], based on fuzzy theory [21], and based on classic control [22]. There is also a significant contribution related with the obstacles avoidance and the reaching goal time reduction; the broad range includes the A* algorithm [23,24], neural networks [25], genetic algorithms [26,27], fuzzy logic [28], particle swarm optimization [29], and ant colony optimization [30], among others [31,32]. ...
This paper presents a type-2 fuzzy inference tree designed for a differential wheeled mobile
robot that navigates in indoor environments. The proposal consists of a controller designed for
obstacle avoidance, a controller for path recovery and goal reaching, and a third controller for the
real-time selection of behaviors. The system takes as inputs the information provided for a 2D laser
range scanner, i.e., the distance of nearby objects to the robot, as well as the robot position in space,
calculated from mechanical odometry. The real performance is evaluated through metrics such as
clearance, path smoothness, path length, travel time and success rate. The experimental results allow
us to demonstrate an appropriate performance of our proposal for the navigation task, with a higher
efficiency than the reference methods taken from the state of the art.
... In particular, despite the remarkable successes in different tasks, research on these approaches is a field of increasing interest [9], with regard to theoretical aspects, which are being deepened [10][11][12], as well as aspects regarding procedures for learning fuzzy systems optimizing accuracy and/or interpretability, or for solving mathematical tasks using fuzzy numbers and soft computing [13][14][15][16][17][18]. Moreover, these approaches are prone to easily and proficiently be employed in different new fields of application [19][20][21]. ...
... The author's approach is based on a fuzzy inference system and a Wiener inverse filter, providing autonomy, reliability, flexibility, and real-time execution, in restoring highly degraded signals without requiring exact knowledge of EB probe size, and a demonstration is given by comparing ground truth signals with restorations. Finally, in [21], the motion control of mobile robots in a cluttered environment with obstacles is considered. In particular, to control the motion of a mobile robot using an eye gaze coordinate as inputs to the system, the paper presents an intelligent vision-based gaze guided robot control, utilizing an overhead camera, an eye-tracking device, a differential drive mobile robot, vision, and an interval-type-2 fuzzy inference tool. ...
Research on fuzzy logic [1] and soft computing for decision making has a long history. In many fields of application, rule-based fuzzy systems have been employed [2–4] for their unique properties in solving modelling problems[...]
... A review of SLAM algorithms for Robot Navigation was addressed in [20]. -Robot path planning Dirik et al. [9] proposed a global path planning method based on ANNs and GAs to provide an effective path planning and obstacle avoidance solution. An ANN can be used to represent the environmental information in the robot workspace in order to find an obstacle avoidance path that leads the robot to the target neuron corresponding to the target location. ...
Robotics is one of the most emerging technologies today, and are used in a variety of applications, ranging from complex rocket technology to monitoring of crops in agriculture. Robots can be exceptionally useful in a smart hospital environment provided that they are equipped with improved vision capabilities for detection and avoidance of obstacles present in their path, thus allowing robots to perform their tasks without any disturbance. In the particular case of Autonomous Nursing Robots, major essential issues are effective robot path planning for the delivery of medicines to patients, measuring the patient body parameters through sensors, interacting with and informing the patient, by means of voice-based modules, about the doctors visiting schedule, his/her body parameter details, etc. This paper presents an approach of a complete Autonomous Nursing Robot which supports all the aforementioned tasks. In this paper, we present a new Autonomous Nursing Robot system capable of operating in a smart hospital environment area. The objective of the system is to identify the patient room, perform robot path planning for the delivery of medicines to a patient, and measure the patient body parameters, through a wireless BLE (Bluetooth Low Energy) beacon receiver and the BLE beacon transmitter at the respective patient rooms. Assuming that a wireless beacon is kept at the patient room, the robot follows the beacon’s signal, identifies the respective room and delivers the needed medicine to the patient. A new fuzzy controller system which consists of three ultrasonic sensors and one camera is developed to detect the optimal robot path and to avoid the robot collision with stable and moving obstacles. The fuzzy controller effectively detects obstacles in the robot’s vicinity and makes proper decisions for avoiding them. The navigation of the robot is implemented on a BLE tag module by using the AOA (Angle of Arrival) method. The robot uses sensors to measure the patient body parameters and updates these data to the hospital patient database system in a private cloud mode. It also makes uses of a Google assistant to interact with the patients. The robotic system was implemented on the Raspberry Pi using Matlab 2018b. The system performance was evaluated on a PC with an Intel Core i5 processor, while the solar power was used to power the system. Several sensors, namely HC-SR04 ultrasonic sensor, Logitech HD 720p image sensor, a temperature sensor and a heart rate sensor are used together with a camera to generate datasets for testing the proposed system. In particular, the system was tested on operations taking place in the context of a private hospital in Tirunelveli, Tamilnadu, India. A detailed comparison is performed, through some performance metrics, such as Correlation, Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), against the related works of Deepu et al., Huh and Seo, Chinmayi et al., Alli et al., Xu, Ran et al., and Lee et al. The experimental system validation showed that the fuzzy controller achieves very high accuracy in obstacle detection and avoidance, with a very low computational time for taking directional decisions. Moreover, the experimental results demonstrated that the robotic system achieves superior accuracy in detecting/avoiding obstacles compared to other systems of similar purposes presented in the related works.
... us, the Type-2 Fuzzy drives interesting works in medical, and several other fields, such as in [23][24][25][26][27][28][29][30][31][32][33]. ...
The paper aims to propose a distributed method for machine learning models and its application for medical data analysis. The great challenge in the medicine field is to provide a scalable image processing model, which integrates the computing processing requirements and computing-aided medical decision making. The proposed Fuzzy logic method is based on a distributed approach of type-2 Fuzzy logic algorithm and merges the HPC (High Performance Computing) and cognitive aspect on one model. Accordingly, the method is assigned to be implemented on big data analysis and data science prediction models for healthcare applications. The paper focuses on the proposed distributed Type-2 Fuzzy Logic (DT2FL) method and its application for MRI data analysis under a massively parallel and distributed virtual mobile agent architecture. Indeed, the paper presents some experimental results which highlight the accuracy and efficiency of the proposed method.
... Parameter ranges and system uncertainties in the position control environments are required to deal with the controller [40]. Soft computing methods like fuzzy logic [41][42][43][44][45][46] are one of these controllers. These controllers have powerful advantages such as being low cost, being easy to control and being designable without knowing the exact mathematical model of the process. ...
Mobile robot motion planning in an unstructured, static, and dynamic environment is faced with a large amount of uncertainties. In an uncertain working area, a method should be selected to address the existing uncertainties in order to plan a collision-free path between the desired two points. In this paper, we propose a mobile robot path planning method in the visualize plane using an overhead camera based on interval type-2 fuzzy logic (IT2FIS). We deal with a visual-servoing based technique for obstacle-free path planning. It is necessary to determine the location of a mobile robot in an environment surrounding the robot. To reach the target and for avoiding obstacles efficiently under different shapes of obstacle in an environment, an IT2FIS is designed to generate a path. A simulation of the path planning technique compared with other methods is performed. We tested the algorithm within various scenarios. Experiment results showed the efficiency of the generated path using an overhead camera for a mobile robot.
