Showing papers on "Mobile robot navigation published in 2023"

Multi-Scale Fully Convolutional Network-Based Semantic Segmentation for Mobile Robot Navigation

Abstract: In computer vision and mobile robotics, autonomous navigation is crucial. It enables the robot to navigate its environment, which consists primarily of obstacles and moving objects. Robot navigation employing impediment detections, such as walls and pillars, is not only essential but also challenging due to real-world complications. This study provides a real-time solution to the problem of obtaining hallway scenes from an exclusive image. The authors predict a dense scene using a multi-scale fully convolutional network (FCN). The output is an image with pixel-by-pixel predictions that can be used for various navigation strategies. In addition, a method for comparing the computational cost and precision of various FCN architectures using VGG-16 is introduced. The binary semantic segmentation and optimal obstacle avoidance navigation of autonomous mobile robots are two areas in which our method outperforms the methods of competing works. The authors successfully apply perspective correction to the segmented image in order to construct the frontal view of the general area, which identifies the available moving area. The optimal obstacle avoidance strategy is comprised primarily of collision-free path planning, reasonable processing time, and smooth steering with low steering angle changes.

A SLAM-Based Localization and Navigation System for Social Robots: The Pepper Robot Case

Abstract: Robot navigation in indoor environments has become an essential task for several applications, including situations in which a mobile robot needs to travel independently to a certain location safely and using the shortest path possible. However, indoor robot navigation faces challenges, such as obstacles and a dynamic environment. This paper addresses the problem of social robot navigation in dynamic indoor environments, through developing an efficient SLAM-based localization and navigation system for service robots using the Pepper robot platform. In addition, this paper discusses the issue of developing this system in a way that allows the robot to navigate freely in complex indoor environments and efficiently interact with humans. The developed Pepper-based navigation system has been validated using the Robot Operating System (ROS), an efficient robot platform architecture, in two different indoor environments. The obtained results show an efficient navigation system with an average localization error of 0.51 m and a user acceptability level of 86.1%.

Chase and Track: Toward Safe and Smooth Trajectory Planning for Robotic Navigation in Dynamic Environments

Abstract: In this article presents a trajectory planning approach toward safe and smooth robot motion in dynamic environments. We develop a hierarchical planning framework with a global planner generating the shortest path between the robot and the navigation target. Specially, a virtual target (VT) is set to run on the global path with a designed velocity. At the local level, the robot chases the VT and tracks the global path when traveling through the dynamic environment. We employ the model predictive control (MPC) framework for the local path generation. In particular, the prediction horizon of the MPC is adaptively changed concerning the distance between the robot and the VT. It implicitly reflects the crowdedness of the environment, which helps reduce the environmental uncertainty. Besides, we develop an event-triggered mechanism that executes the local plan aperiodically to release the computational burden. Based on the local chasing and tracking performance, we develop a global path replanning scheme in response to the untraversable area emerging in the dense environment. The developed framework is validated through extensive experiments in dynamic environments, demonstrating that the robot can reach the target faster and showcase a safer and smoother trajectory in the navigation.

Collision-Free Navigation in Human-Following Task Using a Cognitive Robotic System on Differential Drive Vehicles

Abstract: As human–robot collaboration increases tremendously in real-world applications, a fully autonomous and reliable mobile robot for the collaboration has been a central research topic and investigated extensively in a large number of studies. One of the most pressing issues in such topic is the collision-free navigation that has a moving goal and unknown obstacles under the unstructured environment. In this article, a cognitive robotic system (CRS) is proposed for the robot to navigate itself to the moving target person without obstacle collision. This CRS consists of a cognitive agent, which is created based on the Soar cognitive architecture to reason its current situation and make action decision for the robot to avoid obstacles and reach the target position, and a speed planning module, which is based on dynamic window approach (DWA) to generate appropriate linear and angular velocities for driving the robot’s motors. For the implementation of the proposed system, we use a differential drive wheel robot equipped with two ultrawideband (UWB) sensors and a color depth camera as the experimental platform. Finally, to evaluate the performance of our system in actual operating conditions, we conduct experiments with a scenario that includes main tasks: avoiding consecutive unknown obstacles and turning at corner while the robot follows continuously human user along the corridor.

Artificial Potential Field Path Planning Algorithm in Differential Drive Mobile Robot Platform for Dynamic Environment

Abstract: Mobile robots need path-planning abilities to achieve a collision-free trajectory. Obstacles between the robot and the goal position must be passed without crashing into them. The Artificial Potential Field (APF) algorithm is a method for robot path planning that is usually used to control the robot for avoiding obstacles in front of the robot. The APF algorithm consists of an attractive potential field and a repulsive potential field. The attractive potential fields work based on the predetermined goals that are generated to attract the robot to achieve the goal position. Apart from it, the obstacle generates a repulsive potential field to push the robot away from the obstacle. The robot's localization in producing the robot's position is generated by the differential drive kinematic equations of the mobile robot based on encoder and gyroscope data. In addition, the mapping of the robot's work environment is embedded in the robot's memory. According to the experiment's results, the mobile robot's differential drive can pass through existing obstacles. In this research, four test environments represent different obstacles in each environment. The track length is 1.5 meters. The robot's tolerance to the goal is 0.1 m, so when the robot is in the 1.41 m position, the robot's speed is 0 rpm. The safe distance between the robot and the obstacle is 0.2 m, so the robot will find a route to get away from the obstacle when the robot reaches that safe distance. The speed of the resulting robot decreases as the distance between the robot and the destination gets closer according to the differential drive kinematics equation of the mobile robot.

Vision Navigation Based PID Control for Line Tracking Robot

Abstract: In a controlled indoor environment, line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots. A line tracking robot is a self-mobile machine that can recognize and track a painted line on the floor. In general, the path is set and can be visible, such as a black line on a white surface with high contrasting colors. The robot’s path is marked by a distinct line or track, which the robot follows to move. Several scientific contributions from the disciplines of vision and control have been made to mobile robot vision-based navigation. Localization, automated map generation, autonomous navigation and path tracking is all becoming more frequent in vision applications. A visual navigation line tracking robot should detect the line with a camera using an image processing technique. The paper focuses on combining computer vision techniques with a proportional-integral-derivative (PID) controller for automatic steering and speed control. A prototype line tracking robot is used to evaluate the proposed control strategy.

Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot

Abstract: This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.

Socially Aware Robot Navigation Framework: Where and How to Approach People in Dynamic Social Environments

Abstract: This paper proposes a human approaching robot navigation framework that enables a mobile service robot to (i) estimate a socially optimal approaching pose, and (ii) navigate safely and socially to the estimated approaching pose. In the first stage, the robot estimates potential approaching poses of a human or a human group, which the robot can safely and socially approach, using the dynamic social zone model. In the second stage, the proposed framework selects a socially optimal approaching pose, then estimate a socially optimal trajectory of the robot using the proposed goal-oriented timed elastic band (GTEB) model. The developed GTEB model takes into account the current robot’s states, robot dynamics, dynamic social zone, regular obstacles and potential approaching poses to generate the socially optimal robot trajectory from the robot’s current pose to the selected optimal approaching pose. The motion control command extracted from the socially optimal trajectory is then utilized to drive the mobile robot to approach the individual humans or human groups, while safely and socially avoiding regular obstacles, human and human groups during the navigation process. The proposed approaching human framework is verified in the both simulation and real robots. The results illustrate that, the mobile robot equipped with our developed GTEB model is able to safely and socially approach and avoid individual humans and human groups, while guaranteeing the comfortable safety for the humans and socially acceptable behaviors for the robot. Note to Practitioners—Although our proposed GTEB model is capable of estimating a socially optimal approaching pose and social robot trajectory, driving the robot to approach a human and a human group, and providing the safety and comfort for humans and socially acceptable behaviors of the robot, there exists a few drawbacks if we wish to apply the proposed approaching human framework in dynamic social environments. First, the optimizer for the GTEB model should be improved in terms of computational time and accuracy to avoid generating unpredictable robot trajectories, especially in dynamic social environments. Second, the socio-spatio-temporal characteristics of the humans including human position, motion and orientation, and human group and human–object interactions play an important role in the proposed GTEB model. However, the existing techniques are only suitable in quasi-dynamic social environments. Hence, highly accurate, robust and real-time algorithms for human detection and tracking, and social interaction detection are necessary. Third, social interactive intentions such as human–robot, human–human and human–object interactive intentions should be predicted and incorporated into the proposed framework to improve the performance of the developed framework in the dynamic social environments. Last but not least, human and human group identification algorithms should be proposed to enable the robot to identify the humans, whose the mobile robot is requested to approach. In the future, the social interactive intentions, the human’s future states and its trajectories and will be predicted using deep learning algorithms and incorporated into the approaching human framework to improve the performance of the proposed framework.

Energy Consumption Analysis of the Selected Navigation Algorithms for Wheeled Mobile Robots

Abstract: The article presents the research on navigation algorithms of a wheeled mobile robot with the use of a vision mapping system and the analysis of energy consumption of selected navigation algorithms, such as RRT and A-star. Obstacle maps were made with the use of an RGBW camera, and binary occupation maps were also made, which were used to determine the traffic path. To recreate the routes in hardware, a programmed Pure Pursuit controller was used. The results of navigation were compared on the basis of the forward kinematics model and odometry measurements. Quantities such as current, except (x, y, phi), and linear and angular velocities were measured in real time. As a result of the conducted research, it was found that the RRT star algorithm consumes the least energy to reach the designated target in the designated environment.

Mobile Robot Navigation Based on Embedded Computer Vision

Abstract: The current computational advance allows the development of technological solutions using tools, such as mobile robots and programmable electronic systems. We present a design that integrates the Khepera IV mobile robot with an NVIDIA Jetson Xavier NX board. This system executes an algorithm for navigation control based on computer vision and the use of a model for object detection. Among the functionalities that this integration adds to the Khepera IV in generating guided driving are trajectory tracking for safe navigation and the detection of traffic signs for decision-making. We built a robotic platform to test the system in real time. We also compared it with a digital model of the Khepera IV in the CoppeliaSim simulator. The navigation control results show significant improvements over previous works. This is evident in both the maximum navigation speed and the hit rate of the traffic sign detection system. We also analyzed the navigation control, which achieved an average success rate of 93%. The architecture allows testing new control techniques or algorithms based on Python, facilitating future improvements.

Observation Model for Robust Localization of Mobile Robots based on Particle Filter in Changing Environments

Abstract: For autonomous mobile robot navigation, localization is an essential capability. Given a mobile robot equipped with a 3D LiDAR sensor, an environment map composed of point cloud is built beforehand. The robot is thus allowed to localize the position in the map using the sensor scan data. However, the environment sometimes changes due to obstacles. Under the changing environment, the localization capability of the robot might be decreased. For this challenge, we propose a sensor observation model in a framework of particle filter based localization. In the observation model, we focus on the distance and distribution of point clouds of the map and sensor scan data. In the experiments, a mobile robot is moved by an operator in a virtual environment with obstacles. The robot based on the proposed observation model is able to localize the position in both the original and changing environments with the same accuracy. From the results, we finally show the robustness of the localization capability for changing environments.

Obstacle Avoidance Learning for Robot Motion Planning in Human-Robot Integration Environments

Abstract: In the human-robot integration environment, it is essential for mobile robots to pass through the crowd and complete the navigation task smoothly. However, the current mainstream robot navigation algorithms only treat pedestrians as dynamic obstacles and passively avoid pedestrians in local planning. When encountering fast-moving pedestrians, local path planning often fails, causing the robot to stagnate, spin or shake in place, which in turn reduces the navigation efficiency and results in unnatural navigation trajectories. To address this problem, it is desirable for the robot to find a safe and convenient temporary target to avoid the collision with fast-moving pedestrians. In this paper, we propose an obstacle avoidance learning method with the temporary target for the robot motion planning in the human-robot integration environment. The temporary target distribution is learned from imitations by using a Conditional Variational Autoencoder (CVAE) framework, whereby the dynamic scenario information including pedestrian information, the environmental information, and the robot information are considered as the generation conditions. With the proposed method, the mobile robot first navigates to the temporary target area, and then plans the path toward the final target point. Experimental studies reveal that the proposed method can achieve satisfactory performance with respect to different scenario conditions.

Development and Verification of an Autonomous and Controllable Mobile Robot Platform

Abstract: In this paper, we design a mobile robot platform, which employs a fully autonomous mechanical structure and electrical control system. Two driving wheels realize flexible steering movement with four universal wheels. A variety of sensors are built on the mobile robot platform, including the Inertial Measurement Unit (IMU) used to establish the inertial navigation coordinate system and the Velodyne’s Puck lidar sensor (VLP-16) used to obtain the three-dimensional (3D) point cloud information of the environment. Then, we build a software control architecture based on the Robot Operating System (ROS), using multi-node communication to perform positioning, environment perception, dynamic obstacle avoidance, path planning and motion control. Furthermore, a method of actively exploring the environment and constructing a map is proposed, using multi-path evaluation for real-time path planning and obstacle avoidance. In the end, we conduct autonomous exploration experiments to verify the performance of the designed mobile robot platform in indoor multi-obstacle scenes.

A review: On bio-inspired optimization methods for path planning of mobile robot

Abstract: In recent years, researchers have paid attention to algorithms inspired by nature where these algorithms have proven their efficiency in solving many optimization problems, especially in complex situations, due to their high precision, speed of optimization, simplicity of the techniques, and efficiency in agent cooperation. The primary issue in the field of autonomous mobile robots is navigation. An autonomous robot's navigation ability is one of its most crucial and distinctive features. There are four components of the autonomous robot navigation issue: vision, localization, cognition, and path planning. Many academics have used bio-inspired methods to solve navigation difficulties in mobile robots in recent years, including path planning where they considered the path planning problem as an optimization problem. Many novel path-planning methods have been created, and those using bio-inspired algorithms have received much attention. These algorithms have been shown to be useful in solving complex problems where the solution space isn't always adequately characterized and the problem necessitates solving complex mathematical models of live processes. More intricate optimization methods that transcend the constraints of classical procedures must be applied as the complexities of the optimization problem increase. This work contributes to presenting a group of algorithms inspired by nature that has been used to solve the problem of planning the path of mobile robots, and then making a comparison between these algorithms based on three factors (cost, time, and path length). Choosing an appropriate path-planning method contributes to ensuring safe and efficacious navigation from one point to another.

SLAM (Simultaneous localisation and mapping) Using E-PUCK Robot In Webots Software

Abstract: Traditional agriculture has been the global Abstract- This paper presents a SLAM (Simultaneous localisation and mapping) using an E-puck robot in Webots software. The area of autonomous mobile robots has recently piqued numerous researchers' interest. Due to the unique requirements of many applications of mobile robot systems, particularly in the area of localisation, robot mapping and line following have become the backbone of directing robots in an unfamiliar environment. A theoretical basis for SLAM and occupancy grids, which are employed in this work to build maps, is provided in this paper. The E-Puck robot is utilised in this project, and the simulation is done in the Webots program using Python. The E-Puck robot was successfully programmed and controlled in this experiment to locate and map an unknownarea. To cope with the uncertainty of robot posture in SLAM and self-localisation during navigation, we employ the ExtendedKalman filter (EKF). The mapping (occupancy grid) has been completed, and the robot may now travel through the surroundings using it. Using simulation results, we demonstratethat the robot system can find itself, construct an environment map, and navigate (teleoperation) in the environments.

Behavior of Delivery Robot in Human-Robot Collaborative Spaces During Navigation

Abstract: Navigation is an essential skill for robots. It becomes a cumbersome task for the robot in a human-populated environment, and Industry 5.0 is an emerging trend that focuses on the interaction between humans and robots. Robot behavior in a social setting is the key to human acceptance while ensuring human comfort and safety. With the advancement in robotics technology, the true use cases of robots in the tourism and hospitality industry are expanding in number. There are very few experimental studies focusing on how people perceive the navigation behavior of a delivery robot. A robotic platform named “PI” has been designed, which incorporates proximity and vision sensors. The robot utilizes a real-time object recognition algorithm based on the You Only Look Once (YOLO) algorithm to detect objects and humans during navigation. This study is aimed towards evaluating human experience, for which we conducted a study among 36 participants to explore the perceived social presence, role, and perception of a delivery robot exhibiting different behavior conditions while navigating in a hotel corridor. The participants’ responses were collected and compared for different behavior conditions demonstrated by the robot and results show that humans prefer an assistant role of a robot enabled with audio and visual aids exhibiting social behavior. Further, this study can be useful for developers to gain insight into the expected behavior of a delivery robot.

Application of Laser Ranging in Path Planning of Mobile Medical Robot

Abstract: Compared with traditional industrial robots, mobile robots with autonomous sensing, decision-making and execution functions have broad application prospects. With the continuous development of computer science and sensor technology, the research of mobile medical robot is also developing in the direction of intelligence and autonomy. The mobile medical robot senses the environmental information and its own state through sensors, and realizes the autonomous movement facing the target in the environment with obstacles, thus completing the medical functions specified by the designer. When applied to some practical scenes, the environment faced by mobile medical robots is often full of complexity and unknowns. Accurate 3D scene model is beneficial for robots to obtain accurate pose estimation by data matching and other methods, and can generate collision-free navigation paths based on environmental information. The mobile medical robot can move autonomously safely and effectively only if it can accurately and automatically position itself, the obstacle information in its working environment and its movement state.

Introduction to path planning in three methods

Abstract: All services provided by robots to humans are based on navigation control. Navigation control includes positioning and navigation. Path planning is a key part of navigation. The navigation control algorithm is at the heart of determining the behaviour of the robot. The navigation control module includes global path planning and local path planning. Global path planning is the creation of a feasible path from the start point to the target point using an existing electronic map as a standard. Local path planning, also known as local obstacle avoidance, is the process by which sensors scan for unknown obstacles during the robot's operation and redefine a local path around the obstacles towards the target point. This paper describes some of the main algorithms that are more widely used in motion planning, including sampling-based search methods such as RRT and its range of optimisation methods. Each of these methods has its own search process and results. There is also a search algorithm called the Markov decision process model, which we tried to combine with RRT but failed due to different application areas.

Autonomous Robot Navigation Function

Abstract: Robots often need to move autonomously in a given space to respond to calls and deliver objects, which requires autonomous positioning and navigation capabilities. To achieve navigation functions, having a mobile base and a perceptron to collect environmental information is necessary. This book uses the robot TurtleBot2, i.e., a Kobuki base for development, and robot collects environmental information through its vision sensor, Kinect Xbox 360, in order to achieve navigation.

Visual Navigation Based on Language Assistance and Memory

Abstract: In order to solve outdoor mobile robots’ dependence on geographic information systems, and to realize automatic navigation in the face of complex and changeable scenes, we propose a method that selects landmark and adds prompt guidance so that the mobile robot can navigate relying on visual-language and memory. Visual-language can guide the direction of the mobile robot’s movement, obeying the annotation of people and according to its memory of the scene, which refers to the strategy of selecting passed-by landmarks for the route and remembering the scene features. When passing it, the agent can ascertain the position and match it to carry out the action. Experiments showed that our proposed method can achieve the purpose of independent navigation without GIS, and is superior to existing methods.

Challenges and Solutions for Autonomous Ground Robot Scene Understanding and Navigation in Unstructured Outdoor Environments: A Review

Abstract: The capabilities of autonomous mobile robotic systems have been steadily improving due to recent advancements in computer science, engineering, and related disciplines such as cognitive science. In controlled environments, autonomous robots have been able to achieve relatively high levels of autonomy. In more unstructured environments, however, the realisation of autonomous mobile robots remains challenging due to limitations in the robots’ external environment understanding. Many autonomous mobile robots use classical, learning-based or hybrid approaches for navigation. The classical navigation approach typically includes robot perception, localisation, environmental mapping, path planning and motion control stages. More recent learning-based methods may replace the complete navigation pipeline or selected stages of the classical approach. For effective deployment, autonomous robots need to be able to understand their external environments at a sophisticated level according to their intended applications. Therefore, in addition to robot perception, scene analysis and higher-level scene understanding (e.g., traversable/non-traversable, and rough or smooth terrain) are required for autonomous robot navigation in unstructured outdoor environments. A wide number of alternative approaches have been proposed in recent years to attempt to address these scene understanding requirements. This paper provides a comprehensive review and critical analysis of these methods in the context of their applications to the problems of robot perception and scene understanding in unstructured environments, and the related problems of localisation, environment mapping and path planning. State-of-the-art sensor fusion methods and multimodal scene understanding approaches are also discussed and evaluated within this context. The paper concludes with an in-depth discussion regarding the current state of the autonomous ground robot navigation challenge in unstructured outdoor environments and the most promising future research directions to overcome these challenges.

A navigation scheme for autonomous mobile service robots working in GPS denied commercial indoor spaces

Abstract: Autonomous mobile service robots assist humans in retail, logistics, hospitality, and healthcare, especially for laborious, mundane, and hazardous tasks. In indoor environments, such robots need to navigate effectively to different locations without the availability of a GPS signal. This paper presents a navigation scheme for a mobile robot that includes odometry and a go-to-goal controller. The odometry precisely gauges the bearing angle and distance travelled by the mobile robot. The go-to-goal controller assists the mobile robot in navigating towards the set target point with minimal error. The effectiveness of the presented navigation system is implemented and tested on a differential steering mobile robot. The results confirm the reliable performance of the presented method in all four quadrants with an error of less than one centimeter in each quadrant.

Recent Advances in Mobile Robot Localization in Complex Scenarios

Abstract: Mobile robot localization refers to the process in which the robot body determines its localization in unknown environments. As a key part of automatic navigation technology, localization needs to be accurate and robust in the working environment of the robot. However, most mobile robots are required to operate in a complex environment due to the requirements of work. In this case, the traditional localization method is not easy to ensure the accuracy and robustness of localization. Therefore, it is necessary to research the localization of mobile robots in complex scenes. In this paper, the localization methods of mobile robots in complex scenes in recent years are reviewed, which are mainly divided into two categories: localization based on traditional fusion algorithm and localization based on deep learning algorithm. This paper summarizes the latest research progress of mobile robot localization in complex scenes in detail, and discusses its future development trend.

Growing Robot Navigation Based on Deep Reinforcement Learning

Abstract: The recent progress in materials and structures has kick-started the development of soft eversion robot with the ability to grow in size. However, despite its promising capability to navigate challenging terrains, this type of robot still lacks a navigation strategy due to the robot's complexity courtesy of its increasing degrees of freedom as it grows. In this paper, we develop a growing robot navigation strategy based on deep reinforcement learning. The reinforcement learning was specifically designed to work with growing robot even as its degrees of freedom increase. The algorithm was shown to work in navigating growing robot in a planar environment towards a random target. The results show that the reinforcement learning is a promising candidate to be used for growing robot navigation.

AMR Navigation Based on Dynamic Control Sampling Space

Abstract: This paper addresses the problem of local navigation for autonomous mobile robots in partially observable dynamic environments. The main contribution of this work is the expansion of a sampling based approach to deal with the robot and environment dynamic constraints and integrate a safety distance to keep the robot away from collisions. The developed algorithm has been implemented on a robotic platform with a Robot Operating System based embedded architecture, where it has been tested and validated in an indoor environment.

Localization and Navigation System for Indoor Mobile Robot

Abstract: Visually impaired people usually find it hard to travel independently in many public places such as airports and shopping malls due to the problems of obstacle avoidance and guidance to the desired location. Therefore, in the highly dynamic indoor environment, how to improve indoor navigation robot localization and navigation accuracy so that they guide the visually impaired well becomes a problem. One way is to use visual SLAM. However, typical visual SLAM either assumes a static environment, which may lead to less accurate results in dynamic environments or assumes that the targets are all dynamic and removes all the feature points above, sacrificing computational speed to a large extent with the available computational power. This paper seeks to explore marginal localization and navigation systems for indoor navigation robotics. The proposed system is designed to improve localization and navigation accuracy in highly dynamic environments by identifying and tracking potentially moving objects and using vector field histograms for local path planning and obstacle avoidance. The system has been tested on a public indoor RGB-D dataset, and the results show that the new system improves accuracy and robustness while reducing computation time in highly dynamic indoor scenes.

Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application

Abstract: With the advancement of science and technology, the development and application of unmanned mobile vehicles (UMVs) have emerged as topics of crucial concern in the global industry. The development goals and directions of UMVs vary according to their industrial uses, which include navigation, autonomous driving, and environmental recognition; these uses have become the priority development goals of researchers in various fields. UMVs employ sensors to collect environmental data for environmental analysis and path planning. However, the analysis function of a single sensor is generally affected by natural environmental factors, resulting in poor identification results. Therefore, this study introduces fusion technology that employs heterogeneous sensors in the Ackerman UMV, leveraging the advantages of each sensor to enhance accuracy and stability in environmental detection and identification. This study proposes a fusion technique involving heterogeneous imaging and LiDAR (laser imaging, detection, and ranging) sensors in an Ackerman UMV. A camera is used to obtain real-time images, and YOLOv4-tiny and simple online real-time tracking are then employed to detect the location of objects and conduct object classification and object tracking. LiDAR is simultaneously used to obtain real-time distance information of detected objects. An inertial measurement unit is used to gather odometry information to determine the position of the Ackerman UMV. Static maps are created using simultaneous localization and mapping. When the user commands the Ackerman UMV to move to the target point, the vehicle control center composed of the robot operating system activates the navigation function through the navigation control module. The Ackerman UMV can reach the destination and instantly identify obstacles and pedestrians when in motion.