Obstacle Distance Research Articles

IOT Based Distance Measuring Robot

The paper describes the development of a distance measurement system that uses ultrasonic waves and is interfaced with a microcontroller module ATmega328P and the ATMega 16U2 Processor. We know that the human audible range is 20 Hz to 20 kHz. We can use these frequency range waves with an ultrasonic sensor. The advantages of this sensor are that when it is connected to an Arduino, which is a control and sensing system, a pro per distance measurement can be done using new methodologies. As vast sums of money are spent on hundreds of inflexible circuit boards, the Arduino will enable businesses to introduce many more unique items. This distance measurement system has a wide range of industrial applications, including range meters and proximity detectors. The ultrasonic sensor's hardware is interfaced with the Arduino. This method of measurement is an effective way to accurately measure small distances. The distance between an obstruction and the sensor is measured using an ultrasonic sensor. After knowing the speed of sound, the distance can be computed. In this project, The Bluetooth sensor receives a code from the Arduino app, and the Arduino operates in accordance with the code. Additionally, an ultrasonic sensor is utilized, which transmits an ultrasonic wave towards the object. When the ultrasonic wave returns after hitting the item, the transmitter detects the obstacle's distance and displays it on the LCD in centimeters. Using the ESP32 camera module, the user may log in to a specific IP address and see the object from a remote location while also controlling the robot.

Improved 3D laser point cloud reconstruction for autonomous mobile robot applications by using SVM-R technique

PurposeIn autonomous mobile robots, high-level accuracy and precision in 3D perception are required for object detection, shape estimation and obstacle distance measurement. However, the existing methods suffer from limitations like inaccurate point clouds, noise in sensor data and synchronization problems between 2D LiDAR and servomotor. These factors can lead to the wrong perception and also introduce noise during sensor registration. Thus, the purpose of this study is to address these limitations and enhance the perception in autonomous mobile robots.Design/methodology/approachA new sensor mounting structure is developed for 3D mapping by using a 2D LiDAR and servomotor. The proposed method uses a support vector machine regression (SVM-R) technique to optimize the waypoints of the servomotor for the point cloud reconstruction process and to obtain a highly accurate and detailed representation of the environment.FindingsThe study includes an analysis of the SVM-R model, including Linear, radial basis function (RBF) and Polynomial kernel. Results show that the Linear kernel performs better with the lowest 3.67 mean absolute error (MAE), 26.24 mean squared error (MSE) and 5.12 root mean squared error (RMSE) values than the RBF and Polynomial kernels. The 2D to 3D point cloud reconstruction shows that the proposed method with a new sensor mounting structure performs better in perception accuracy and achieves an error of 0.45% in measuring the height of the target objects whereas in previous techniques the error was very large.Originality/valueThe study shows the effectiveness of SVM-R in the 3D point cloud reconstruction process and exhibits remarkable performance for object height measurement. Further, the proposed technique is applicable for future advanced visual applications and has a superior performance over the other conventional methods.

Improved DDPG algorithm-based path planning for unmanned surface vehicles

As a promising mode of water transportation, unmanned surface vehicles (USVs) are used in various fields owing to their small size, high flexibility, favorable price, and other advantages. Traditional navigation algorithms are affected by various path planning issues. To address the limitations of the traditional deep deterministic policy gradient (DDPG) algorithm, namely slow convergence speed and sparse reward and punishment functions, we proposed an improved DDPG algorithm for USV path planning. First, the principle and workflow of the DDPG deep reinforcement learning (DRL) algorithm are described. Second, the improved method (based on the USVs kinematic model) is proposed, and a continuous state and action space is designed. The reward and punishment function are improved, and the principle of collision avoidance at sea is introduced. Dynamic region restriction is added, distant obstacles in the state space are ignored, and the nearby obstacles are observed to reduce the number of algorithm iterations and save computational resources. The introduction of a multi-intelligence approach combined with a prioritized experience replay mechanism accelerates algorithm convergence, thereby increasing the efficiency and robustness of training. Finally, through a combination of theory and simulation, the DDPG DRL is explored for USV obstacle avoidance and optimal path planning.

Large eddy simulation of droplet dispersion and deposition over street canyons

Predicting droplet transport in the atmosphere is a major issue for a large number of dispersion problems such as spray cooling systems in streets, urban lawn sprinklers [Milesi et al., “Interferometric laser imaging for respiratory droplets sizing,” Environ. Manage. 36, 426–438 (2005)], and dispersion of virus such as COVID or from a terrorist attack [Balachandar et al., “Host-to-host airborne transmission as a multiphase flow problem for science-based social guidelines,” Int. J. Multiphase Flow 132, 103439 (2020)]. Here, we investigate the propagation of droplets issued from a source near the ground between square obstacles with different spacings. The aerodynamic field is determined by large eddy simulation coupled with an immersed boundary method for the obstacles. Droplets are tracked by a Lagrangian approach. An evaporation model is used to account for varying humidity conditions. Droplet concentration, mass flux, and deposition rates are obtained for different evaporation conditions and obstacle spacings. The results show high deposition rates on the internal wall of the first obstacle due to droplet advection by the reverse flow of the primary vortex. In addition to this, droplets may escape the canyon and propagate downstream as far as eight times the obstacle distance (8H). The concentration of air-borne droplets downstream the canyon is higher for smaller street canyon openings. When evaporation is accounted for, the ground-level droplet concentration may be reduced by up to 30% in the case of small canyon openings.

Research on obstacle avoidance of underactuated autonomous underwater vehicle based on offline reinforcement learning

Abstract The autonomous navigation and obstacle avoidance capabilities of autonomous underwater vehicles (AUVs) are essential for ensuring their safe navigation and long-term, efficient operation. However, the complexity of the marine environment poses significant challenges to safe and effective obstacle avoidance. To address this issue, this study proposes an AUV obstacle avoidance control algorithm based on offline reinforcement learning. This method adopts the Conservative Q-learning (CQL) algorithm, which is based on the Soft Actor-Critic (SAC) framework. It learns from obtained historical obstacle avoidance data and ultimately achieves a favorable obstacle avoidance control strategy. In this method, PID and SAC control algorithms are utilized to generate expert obstacle avoidance data to construct a diversified offline database. Additionally, based on the line-of-sight (LOS) guidance method and artificial potential field (APF) method, information regarding the distance and orientation of targets and obstacles is incorporated into the state space, and heading and obstacle avoidance reward terms are integrated into the reward function design. The algorithm successfully guides the AUV in autonomous navigation and dynamic obstacle avoidance in three-dimensional space. Furthermore, the algorithm exhibits a certain degree of anti-interference capability against uncertain disturbances and ocean currents, enhancing the safety and robustness of the AUV system. Simulation results fully demonstrate the feasibility and effectiveness of the intelligent obstacle avoidance method based on offline reinforcement learning. This study highlights the profound significance of offline reinforcement learning in enabling robust and reliable control systems for AUVs, paving the way for enhanced operational capabilities in challenging marine environments.

Implementation of smart wheelchair with obstacle avoidance

The primary goal of this work is to develop a dependable and efficient solution for maintaining optimal mobility in wheelchair systems. The aim is to enhance functionality and reduce the user’s physical effort required to move the wheelchair wheels. The proposed system integrates advanced sensing technology, intelligent control algorithms, and robust hardware components to manage the wheelchair, detect obstacles, and eliminate the need for external assistance. This wheelchair employs joystick control and halts when the system senses an obstacle. The system enhances user autonomy by providing ease of control and ensures safety by detecting and avoiding obstacles. Motor speeds are adjusted based on joystick input, and the motor direction (forward, backward, left, right) is determined accordingly. An ultrasonic sensor measures obstacle distance, and if an obstacle is detected (distance <= 20cm), both motors stop, and a warning buzzer activates for 1 second. If no obstacle is detected, the warning buzzer turns off, and motor speeds are adjusted based on joystick input. The system effectively demonstrated obstacle avoidance by changing direction when a command was issued away from the obstacle. The outcomes of this study contribute valuable insights to the field of wheelchairs, specifically in the realm of obstacle avoidance.

Port environmental path planning based on key obstacles

This paper proposes an improved hybrid algorithm for automated guided vehicles (AGVs) in port environments based on the concept of key obstacles for the JPS and DWA algorithms. Given the complexity of the port environment and the abundance of obstacles, the traditional heuristic function of the JPS algorithm is improved by adding the key obstacle heuristic function. Simultaneously, improvements are made to the evaluation function of the traditional DWA algorithm, where the braking distance is segmented into key obstacle distance and non-key obstacle distance, utilizing the concept of key obstacles. Simulation experiments are conducted using Matlab to demonstrate the effectiveness of the improved algorithm. Moreover, the performance of the hybrid algorithm is compared with five mainstream algorithms in a real simulated port environment, and the final results show the significant enhancement of this paper’s algorithm in several key performance metrics. Thus, this study provides a feasible strategy for improved path planning efficiency for AGV in the port environment.

Research on SLAM Localization Algorithm for Orchard Dynamic Vision Based on YOLOD-SLAM2

With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter-row environments for agricultural vehicles. This paper proposes a method for extracting navigation lines and measuring pedestrian obstacles. The improved YOLOv5 algorithm is used to detect tree trunks between left and right rows in orchards. The experimental results show that the average angle deviation of the extracted navigation lines was less than 5 degrees, verifying its accuracy. Due to the variable posture of pedestrians and ineffective camera depth, a distance measurement algorithm based on a four-zone depth comparison is proposed for pedestrian obstacle distance measurement. Experimental results showed that within a range of 6 m, the average relative error of distance measurement did not exceed 1%, and within a range of 9 m, the maximum relative error was 2.03%. The average distance measurement time was 30 ms, which could accurately and quickly achieve pedestrian distance measurement in orchard environments. On the publicly available TUM RGB-D dynamic dataset, YOLOD-SLAM2 significantly reduced the RMSE index of absolute trajectory error compared to the ORB-SLAM2 algorithm, which was less than 0.05 m/s. In actual orchard environments, YOLOD-SLAM2 had a higher degree of agreement between the estimated trajectory and the true trajectory when the vehicle was traveling in straight and circular directions. The RMSE index of the absolute trajectory error was less than 0.03 m/s, and the average tracking time was 47 ms, indicating that the YOLOD-SLAM2 algorithm proposed in this paper could meet the accuracy and real-time requirements of agricultural vehicle positioning in orchard environments.

Intelligent controller design of an autonomous system using a social spider optimizer for path navigation and obstacle avoidance

This research paper proposes a hybrid fuzzy logic controller for achieving autonomous path navigation and obstacle avoidance through the use of the Social Spider Optimizer algorithm. The proposed controller employs kinematic modelling to determine the mobile robot’s path navigation and utilizes a fuzzy logic system for effective control. The Social Spider Optimizer algorithm optimizes the parameters of the fuzzy controller, while the FLC is responsible for obstacle avoidance. The effectiveness of the proposed controller has been analyzed, and a comparative study has been carried out with optimization techniques like particle swarm optimization (PSO) and cuckoo search optimization (CSO) controllers. The study aims to propose a hybrid fuzzy logic controller, that provides efficient navigation and obstacle avoidance for mobile robots. In a simulation, the starting point is considered as (0,0) and the destination point is set as Xk = 1.1 and Yk = 1.2. The performance of the proposed method is compared with FLC and methods like PSO and CSO. With the SSO-based FLC, the proposed mobile robot identifies the obstacle distance and travels towards the destination with smooth navigation. The results show the efficacy of the proposed controller in comparison to other controllers for mobile robots in terms of path navigation and obstacle avoidance.

ANN‐PID based automatic braking control system for small agricultural tractors

AbstractBraking system is a crucial component of tractors as it ensures safe operation and control of the vehicle. The limited space availability in the workspace of a small tractor exposes the operator to undesirable posture and a maximum level of vibration during operation. The primary cause of road accidents, particularly collisions, is attributed to the tractor operator's insufficient capacity to provide the necessary pedal power for engaging the brake pedal. During the process of engaging the brake pedal, the operator adjusts the backrest support to facilitate access to the brake pedal while operating under stressed conditions. In the present study, a linear actuator‐assisted automatic braking system was developed for the small tractors. An integrated artificial neural network proportional–integral–derivative (ANN‐PID) controller‐based algorithm was developed to control the position of the brake pedal based on the input parameters like terrain condition, obstacle distance, and forward speed of the tractor. The tractor was operated at four different speeds (i.e., 10, 15, 20, and 25 km/h) in different terrain conditions (i.e., dry compacted soil, tilled soil, and asphalt road). The performance parameters like sensor digital output (SDO), force applied on the brake pedal (), and deceleration were considered as dependent parameters. The SDO was found to good approximation for sensing the position of the brake pedal during braking. The optimized network topology of the developed multilayer perceptron neural network (MLPNN) was 3‐6‐2 for predicting SDO and deceleration of the tractor with a coefficient of determination () for the training and testing datasets of the SDO and deceleration were obtained as 0.9953 and 0.9854, and 0.9254 and 0.9096, respectively. The Ziegler–Nichols (Z‐N method) method was adopted to determine the initial optimal gains of the PID controller and later these coefficients were optimized using response surface methodology. The optimized proportional (), integral (), and derivative () coefficient values were 4.8, 6.782, and 3.15, respectively. The developed integrated ANN, that is, MLPNN and PID‐based algorithm could successfully control the position of the brake pedal during braking. The stopping distance and slip of the tractor during automatic braking increased with an increase in the forward speed for the tractor from 10 to 25 km/h in all the selected terrain conditions.

Research on Unmanned Vehicle Path Planning Based on the Fusion of an Improved Rapidly Exploring Random Tree Algorithm and an Improved Dynamic Window Approach Algorithm

Aiming at the problem that the traditional rapidly exploring random tree (RRT) algorithm only considers the global path of unmanned vehicles in a static environment, which has the limitation of not being able to avoid unknown dynamic obstacles in real time, and that the traditional dynamic window approach (DWA) algorithm is prone to fall into a local optimum during local path planning, this paper proposes a path planning method for unmanned vehicles that integrates improved RRT and DWA algorithms. The RRT algorithm is improved by introducing strategies such as target-biased random sampling, adaptive step size, and adaptive radius node screening, which enhance the efficiency and safety of path planning. The global path key points generated by the improved RRT algorithm are used as the subtarget points of the DWA algorithm, and the DWA algorithm is optimized through the design of an adaptive evaluation function weighting method based on real-time obstacle distances to achieve more reasonable local path planning. Through simulation experiments, the fusion algorithm shows promising results in a variety of typical static and dynamic mixed driving scenarios, can effectively plan a path that meets the driving requirements of an unmanned vehicle, avoids unknown dynamic obstacles, and shows higher path optimization efficiency and driving stability in complex environments, which provides strong support for an unmanned vehicle’s path planning in complex environments.

Smart insect-computer hybrid robots empowered with enhanced obstacle avoidance capabilities using onboard monocular camera

Insect-computer hybrid robots are receiving increasing attention as a potential alternative to small artificial robots due to their superior locomotion capabilities and low manufacturing costs. Controlling insect-computer hybrid robots to travel through terrain littered with complex obstacles of various shapes and sizes is still challenging. While insects can inherently deal with certain obstacles by using their antennae to detect and avoid obstacles, this ability is limited and can be interfered with by control signals when performing navigation tasks, ultimately leading to the robot being trapped in a specific place and having difficulty escaping. Hybrid robots need to add additional sensors to provide accurate perception and early warning of the external environment to avoid obstacles before getting trapped, ensuring smooth navigation tasks in rough terrain. However, due to insects’ tiny size and limited load capacity, hybrid robots are very limited in the sensors they can carry. A monocular camera is suitable for insect-computer hybrid robots because of its small size, low power consumption, and robust information acquisition capabilities. This paper proposes a navigation algorithm with an integrated obstacle avoidance module using a monocular camera for the insect-computer hybrid robot. The monocular cameras equipped with a monocular depth estimation algorithm based on deep learning can produce depth maps of environmental obstacles. The navigation algorithm generates control commands that can drive the hybrid robot away from obstacles according to the distribution of obstacle distances in the depth map. To ensure the performance of the monocular depth estimation model when applied to insect-computer hybrid robotics scenarios, we collected the first dataset from the viewpoint of a small robot for model training. In addition, we propose a simple but effective depth map processing method to obtain obstacle avoidance commands based on the weighted sum method. The success rate of the navigation experiment is significantly improved from 6.7% to 73.3%. Experimental results show that our navigation algorithm can detect obstacles in advance and guide the hybrid robots to avoid them before they get trapped.

Enhancing autonomous driving through intelligent navigation: A comprehensive improvement approach

In this paper, an intelligent navigation system is developed to achieve accurate and rapid response to autonomous driving. The system is improved with three modules: target detection, distance measurement, and navigation obstacle avoidance. In the target detection module, the YOLOv7x-CM model is proposed to improve the efficiency and accuracy of target detection by introducing the CBAM attention mechanism and MPDioU loss function. In the obstacle distance measurement module, the concept of an off-center angle is introduced to optimize the traditional monocular distance measurement method. In the obstacle avoidance module, acceleration jump and steering speed constraints are introduced into the local path planning algorithm TEB, and the TEB-S algorithm is proposed. Finally, this paper evaluates the performance of the system modules using the KITTI dataset and the BDD100K dataset. It is demonstrated that YOLOv7x-CM improves the mAP @ 0.5 metrics by 5.3% and 6.8% on the KITTI dataset and the BDD100K dataset, respectively, and the FPS also increases by 35.4%. Secondly, for the optimized monocular detection method, the average relative distance error is reduced by 9 times. In addition, the proposed TEB-S algorithm has a shorter obstacle avoidance path and higher efficiency than the normal TEB algorithm.

Model Predictive Collision Avoidance Control for Object Transport of Unmanned Underwater Vehicle-Dual-Manipulator Systems

Unmanned underwater vehicle-dual-manipulator systems (UVDMSs) have attracted much research due to their humanoid operation capabilities, which have the advantage of cooperative manipulations and transporting underwater objects. Meanwhile, collision avoidance of UVDMSs is more challenging than that of unmanned underwater vehicle-dual manipulator systems (UVMSs). In this work, a model predictive control (MPC) approach is proposed for collision avoidance in objects transporting tasks of UVDMSs. The minimum distances of mutual manipulators and frame obstacles are handled as velocity constraints in the optimization of the UVDMS’s object tracking control. The command velocity generated by the model predictive kinematic controller is tracked by a dynamic inversion control scheme while model uncertainties are compensated by a neural network. Moreover, the tracking errors of the proposed dynamic controller are proved to be convergent by the Lyapunov method. At last, a three-dimensional (3D) UVDMS simulation platform is developed to verify the effectiveness of the proposed control strategy in the tasks of collision avoidance and object transport.

Detection of Obstacle Distance and Position in Surveillance Radar Using IOT

Surveillance radar is most prominently utilized to detect aircraft and ships, including those in aviation, military, maritime, and security applications. Surveillance radar systems are cutting-edge technology for detecting various entities using radio waves. The main intention of this project is to design and construct a radar system to detect stationary objects by measuring their distance and angle of rotation. These mechanisms consist of a sonar-established tracking system that continuously supervises the object. This project will employ an ultrasonic sensor that is placed on a servomotor for the rotations. If the sensor detects the object, it will display the target distance and angle of rotation on the LCD with a graphical representation. The main directing tool is the Arduino UNO, which is filled with programs written in embedded C.

Probabilistic Higher-Order Velocity Obstacle for Dynamic Collision Avoidance in Uncertain Environments

Autonomous systems like unmanned aircraft systems are touted as front-runners in terms of efficiency and agility in challenging operations like emergency response. The ability of autonomous agents in such systems to avoid collisions with dynamic obstacles while navigating through an obstacle-cluttered environment is critical for success in any mission. In addition to that, further challenges are posed by environments with uncertainties in obstacles’ motion, sensing, and occluded regions. To this end, a higher-order velocity obstacle–based novel motion planner is presented in this paper in a probabilistic setup for smooth, collision-free navigation of the agent with acceleration constraints in uncertain, unstructured environments with an element of anticipation for the future environment. The effectiveness of the developed algorithm for safe, collision-free, and smooth navigation of the agent is investigated in simulation studies in two different kinds of environments, one with known trajectories of obstacles and the other with unknown maneuvering trajectories of obstacles. Extensive simulation studies are performed in the presence of dynamic obstacles to elucidate the performance of the proposed algorithm on four crucial parameters—mission time, computational time, minimum obstacle distance, and an overall control effort under varied obstacle densities. With satisfactory performance in all these aspects, the developed algorithm possesses strong potential for real-time implementation.

Path Planning of Self-driving Vehicles Combining Ant Colony and DWA Algorithms in Complex Dense Obstacles

INTRODUCTION: To solve the problems of low quality and weak global optimization of the DWA algorithm, especially the problems of unreasonable path planning and the inability to give consideration to speed and driving safety in the process of vehicles passing through dense obstacles, this paper proposed an improved DWA algorithm based on ant colony algorithm.
 OBJECTIVES: The traffic capacity and computing efficiency of Self-driving Vehicles in complex dense obstacles can be greatly improved.
 METHODS: Through the obstacle density and distance information obtained by high-precision sensors on the vehicle, the speed objective function is updating in real time by using ant colony algorithm. And the maneuverability and safety performance of vehicles passing through are considering by the way.
 RESULTS: The experimental results show that this method can obviously improve the vehicle's traveling ability and uneven path planning in the case of dense obstacles, and the number of iterations of the algorithm is reduced by more than 16%.
 CONCLUSION: The improved DWA algorithm integrated with the ant colony algorithm can effectively improve the operating efficiency of the algorithm, reduce the distance the car must go around outside the obstacles, and improve Car driving safety. The effectiveness and universality of the improved DWA algorithm were verified through experiments.

Examining How Interaural Differences Owing to Head Rotation during Walking Improve the Distance of Auditory Obstacle Perceptions among Individuals with Visual Impairment: A Case Study in Small-Scale Blind Group.

The ability of individuals with visual impairment to recognize an obstacle by hearing is called "obstacle sense". This ability is facilitated while they are moving, though the exact reason remains unknown. This study aims to clarify which acoustical factors may contribute to obstacle sense, especially obstacle distance perception. First, we conducted a comparative experiment regarding obstacle distance localization by individuals who are blind (N = 5, five men with blindness aged 22-42 (average: 29.8)) while they were standing and walking. The results indicate that the localized distance was more accurate while walking than while standing. Subsequently, the head rotation angle while walking and acoustic characteristics with respect to obstacle distance and head rotation angle were investigated. The peaks of the absolute head rotation angle during walking ranged from 2.78° to 11.11° (average: 6.55°, S.D.: 2.05°). Regarding acoustic characteristics, acoustic coloration occurred, and spectral interaural differences and interaural intensity differences were observed in the blind participants (N = 4, four men including two blind and two control sighted persons aged 25-38 (average: 30.8)). To determine which acoustic factors contribute, we examined the threshold of changes for interaural differences in time (ITD) and intensity (IID) (N = 11, seven men and four women with blindness aged 21-35 (average: 27.4)), as well as coloration (ICD) (N = 6, seven men and a woman with blindness aged 21-38 (average: 29.9))-depending on the head rotation. Notably, ITD and IID thresholds were 86.2 μs and 1.28 dB; the corresponding head rotation angles were 23.5° and 9.17°, respectively. The angle of the ICD threshold was 6.30° on average. Consequently, IID might be a contributing factor and ICD can be utilized as the cue facilitating the obstacle distance perception while walking.

Advancing sanitary surveillance: Innovating a live-feed sewer monitoring framework for effective water level and chamber cover detections

Efficient sanitation system management relies on vigilant sewage surveillance to uphold environmental hygiene. The absence of robust monitoring infrastructure jeopardizes unimpeded conduit flow, leading to floods and contamination. The accumulation of harmful gases in sewer chambers, coupled with tampered lids, compounds sewer network challenges, resulting in structural damage, disruptions, and safety risks from accidents and gas inhalation. Notably, even vehicular transit is vulnerable, facing collisions due to inadequately secured manholes. The core objective of this research was to deconstruct and synthesize a prototype blueprint for a live-feed sewer monitoring framework (LSMF). This involves creating a data gathering nexus (DGN) and empirically assessing diverse wireless sensing implements (WSI) for precision. Simultaneously, a geographic information matrix (GIM) was developed with algorithms to detect sewer surges, blockages, and missing manhole covers. Three scrutinized sensors—the LiDar TF-Luna, laser TOF400 VL53L1X, and ultrasonic JSN-SR04T—were evaluated for their ability to measure water levels in sewer vaults. The results showed that the TF-Luna LiDar sensor performed favorably within the 1.0–5.0 m range, with a standard deviation of 0.44–1.15. The TOF400 laser sensor ranked second, with a more variable standard deviation of up to 104 as obstacle distance increased. In contrast, the JSN-SR04T ultrasonic sensor exhibited lower standard deviation but lacked consistency, maintaining readings of 0.22–0.23 m within the 2.0–5.0 m span. The insights from this study provide valuable guidance for sustainable solutions to sewer surveillance challenges. Moreover, employing a logarithmic function, TF-Luna Benewake exhibited reliability at approximately 84.5%, while TOF400 VL53L1X adopted an exponential equation, boasting reliability approaching approximately 89.6%. With this navigational tool, TF-Luna Benewake maintained accuracy within ±10 cm for distances ranging from 8 to 10 m, showcasing its exceptional performance.

Using a Robot for Indoor Navigation and Door Opening Control Based on Image Processing

This study used real-time image processing to realize obstacle avoidance and indoor navigation with an omnidirectional wheeled mobile robot (WMR). The distance between an obstacle and the WMR was obtained using a depth camera. Real-time images were used to control the robot’s movements. The WMR can extract obstacle distance data from a depth map and apply fuzzy theory to avoid obstacles in indoor environments. A fuzzy control system was integrated into the control scheme. After detecting a doorknob, the robot could track the target and open the door. We used the speeded up robust features matching algorithm to recognize the WMR’s movement direction. The proposed control scheme ensures that the WMR can avoid obstacles, move to a designated location, and open a door. Like humans, the robot performs the described task only using visual sensors.