Robot Navigation Control Research Articles

Improved Multi-Sensor Fusion Dynamic Odometry Based on Neural Networks

High-precision simultaneous localization and mapping (SLAM) in dynamic real-world environments plays a crucial role in autonomous robot navigation, self-driving cars, and drone control. To address this dynamic localization issue, in this paper, a dynamic odometry method is proposed based on FAST-LIVO, a fast LiDAR (light detection and ranging)–inertial–visual odometry system, integrating neural networks with laser, camera, and inertial measurement unit modalities. The method first constructs visual–inertial and LiDAR–inertial odometry subsystems. Then, a lightweight neural network is used to remove dynamic elements from the visual part, and dynamic clustering is applied to the LiDAR part to eliminate dynamic environments, ensuring the reliability of the remaining environmental data. Validation of the datasets shows that the proposed multi-sensor fusion dynamic odometry can achieve high-precision pose estimation in complex dynamic environments with high continuity, reliability, and dynamic robustness.

A Soft Actor-Critic Deep Reinforcement-Learning-Based Robot Navigation Method Using LiDAR

When there are dynamic obstacles in the environment, it is difficult for traditional path-generation algorithms to achieve desired obstacle-avoidance results. To solve this problem, we propose a robot navigation control method based on SAC (Soft Actor-Critic) Deep Reinforcement Learning. Firstly, we use a fast path-generation algorithm to control the robot to generate expert trajectories when the robot encounters danger as well as when it approaches a target, and we combine SAC reinforcement learning with imitation learning based on expert trajectories to improve the safety of training. Then, for the hybrid data consisting of agent data and expert data, we use an improved prioritized experience replay method to improve the learning efficiency of the policies. Finally, we introduce RNN (Recurrent Neural Network) units into the network structure of the SAC Deep Reinforcement-Learning navigation policy to improve the agent’s transfer inference ability in a new environment and obstacle-avoidance ability in dynamic environments. Through simulation and practical experiments, it is fully verified that our method has a higher training efficiency and navigation success rate compared to state-of-the-art reinforcement-learning algorithms, which further enhances the obstacle-avoidance capability of the robot system.

Geometry-Aware Safety-Critical Local Reactive Controller for Robot Navigation in Unknown and Cluttered Environments

Geometry-Aware Safety-Critical Local Reactive Controller for Robot Navigation in Unknown and Cluttered Environments

Paraffin-Enabled Superlattice Customization for a Photostimulated Gradient-Responsive Artificial Reflex Arc.

The development of photostimulated-motion artificial reflex arcs - a neural circuit inspired by light-driven motion reflexes - holds significant promises for advancements in robotic perception, navigation, and motion control. However, the fabrication of such systems, especially those that accommodate multiple actions and exhibit gradient responses, remains challenging. Here, a gradient-responsive photostimulated-motion artificial reflex arc is developed by integrating a programmable and tunable photoreceptor based on folded MoS2 at different twist angles. The twisted folded bilayer MoS2 used as photoreceptors can be customized via the transfer technique using patternable paraffin, where the twist angle and fold-line could be controlled. The photoluminescence (PL) intensity is 3.7 times higher at a twist angle of 29° compared to that at 0°, showing a monotonically decreasing indirect bandgap. Through tunable interlayer carrier transport, photoreceptors fabricated using folded bilayer MoS2 at different twist angles demonstrate gradient response time, enabling the photostimulated-motion artificial reflex arc for multiaction responses. They are transformed to digital command flow and studied via machine learning to control the gestures of a robotic hand, showing a prototype of photostimulated gradient-responsive artificial reflex arcs for the first time. This work provides a unique idea for developing intelligent soft robots and next-generation human-computer interfaces.

Pengontrolan Robot Bulu Tangkis menggunakan Mikrokontroller ATmega128 berbasis Android Mobile

In Indonesia, badminton was one of the most popular sport in various circles. Along with the times, robots can be designed as a device that can be used to play badminton. Robominton was badminton robot which is operated manually by human using a remote control or fully automatic. For a robot that controlled by manual-based, bluetooth or cable connection using joystick was used. This refers to the rules that have been set by DIKTI in “Robot Abu Indonesia (KRAI) 2015” Contest. As for the robot control system that we develop, are robotic system with manual control using android as a transmitter and bluetooth module HC-05 as a receiver. There is a robotic navigation control systems, transmission control systems shuttlecock, shuttlecock and servicing of control systems. ATmega 128 processor, four DC motors, with the EMS driver 30A H-Bridges was used as a hardware on shuttlecock transmission syste,. For shuttlecock services, self-designed pneumatic driver with solenoid valve was used. From the test results, the robot was able to navigate premises 11 formation movement, five pieces of shuttlecock transmission, and can serve up and down with a pneumatic

Fuzzy Logic Control for Mobile Robot Navigation in Automated Storage

Fuzzy Logic Control for Mobile Robot Navigation in Automated Storage

A comprehensive review on materialistic and sustainable approach for drone sanitizations

Military, law enforcement, surveillance, leisure, scientific, and research have benefited from drones. Recent decades have seen considerable advances in aerial robot structure, working method, flying features, and navigation control, notably small UAVs and drones. Their use in civilized places needs regulation. Early covid products delivery and sanitization involved drones. This thorough analysis of UAV applications in numerous domains from respected journals and scientific reports during the past decade. Summary of UAV-based sanitization research inputs. This review helps inexperienced researchers evaluate studies. This concise review of materialistic and sustainable drone sanitization literature is useful for novice researchers and practitioners.

A Review on the Environment Perception and Control Technologies for the Hyperredundant Manipulators in Limited Space

As a typical kind of special robot with high flexibility and maneuverability, the hyperredundant manipulators (HRM) which can work in the narrow and complex space arose much related research work. Due to the particularity of the environment and the structural complexity of the manipulators, there are many problems for the HRM in specific applications. This paper summarizes some representative research works for HRM, including the mechanical design, environment perception, robotic navigation, and trajectory control. In order to make the design of the HRM systems more suitable for applications, the technical problems of current research are analyzed to address the key issues for the improvement. Finally, the prospect of spatial reachability, structural compactness, operation accuracy, and interaction friendliness of hyperredundant manipulators are presented.

Navigation Control Method of Indoor Mobile Robot Based on Visual Servo

In order to improve the navigation control effect of indoor mobile robot, this paper studies the navigation control method of indoor mobile robot combined with visual servo technology. For the problem that the target may be out of the camera’s field of view during the servo process, a control algorithm with field-of-view constraints is proposed, which assigns a certain weight to the image feature error at each moment. Moreover, this paper realizes the servo process that the image features are always located in the field of view by setting the variable values involved in the weight value calculation method. Through the simulation test, it is verified that the indoor mobile robot navigation control method based on visual servo has certain effects.

Autonomous Robot System Based on Room Nameplate Recognition Using YOLOv4 Method on Jetson Nano 2GB

The prediction of COVID-19 cases will continue to experience a surge, inseparable from the presence of a new variant of the coronavirus in the world. One of the best ways to prevent transmission of the virus is to avoid or limit contact with people showing symptoms of COVID-19 or any respiratory infection. The number of medical personnel infected when interacting with patients directly also needs to be an essential concern. Hence, an autonomous robot based on room nameplate recognition systems is a solution. It can be used as an intermediary medium for medical personnel with patients to reduce the intensity of direct contact primarily can be implemented in the hospital. It is expected to reduce the spread of the COVID-19 virus, especially among health workers. Each patient room in the hospital has its room nameplate to be used as a robot reference in navigating. This research aims to make a room nameplate recognition system using the YOLOv4 method on NVIDIA Jetson Nano 2GB that produces an output for 4-wheeled robot navigation control to move. This system is designed to detect rooms within a range of 1-3 meters using 5W and 10W power modes. The testing results based on recognition is obtained an average accuracy value of 95.34%. The system performance test results based on the power mode resulted in the best average computing time of 0.149 seconds. The average value of the accuracy of output integration with the system is 94.73%.

Motion planning and control for mobile robot navigation using machine learning: a survey

Moving in complex environments is an essential capability of intelligent mobile robots. Decades of research and engineering have been dedicated to developing sophisticated navigation systems to move mobile robots from one point to another. Despite their overall success, a recently emerging research thrust is devoted to developing machine learning techniques to address the same problem, based in large part on the success of deep learning. However, to date, there has not been much direct comparison between the classical and emerging paradigms to this problem. In this article, we survey recent works that apply machine learning for motion planning and control in mobile robot navigation, within the context of classical navigation systems. The surveyed works are classified into different categories, which delineate the relationship of the learning approaches to classical methods. Based on this classification, we identify common challenges and promising future directions.

An aerial/ground robot team for autonomous firefighting in urban GNSS-denied scenarios

This paper presents a framework for urban firefighting with a heterogeneous aerial/ground robot team. The system was developed to address Challenge 3 of the MBZIRC’20. The challenge required autonomously detecting, locating, and extinguishing fires on multiple interior floors and on exterior facade and ground surfaces. Our multi-robot system consists of a robot team of up to three Unmanned Aerial Vehicles (UAV) and one Unmanned Ground Vehicle (UGV). We describe the main hardware and software components for UAV and UGV platforms and present the system’s main algorithmic components: a 3D LIDAR-based Mapping and Localization module able to work in GPS-denied scenarios, a Global Planner and a fast local re-planning system for robot navigation, Infrared-Based Perception and robot actuation control for fire extinguishing, and Mission Executive and coordination module based on Behavior Trees. Finally we describe our results from the competition, where the system worked fully autonomously and scored in all the trials performed. The presented system ended in 7 th place (out of 20 teams) in Challenge 3 and 5 th place (out of 17 teams) in the Grand Challenge’s Challenge 3 portion.

Autonomous Navigation System of Greenhouse Mobile Robot Based on 3D Lidar and 2D Lidar SLAM.

The application of mobile robots is an important link in the development of intelligent greenhouses. In view of the complex environment of a greenhouse, achieving precise positioning and navigation by robots has become the primary problem to be solved. Simultaneous localization and mapping (SLAM) technology is a hot spot in solving the positioning and navigation in an unknown indoor environment in recent years. Among them, the SLAM based on a two-dimensional (2D) Lidar can only collect the environmental information at the level of Lidar, while the SLAM based on a 3D Lidar demands a high computation cost; hence, it has higher requirements for the industrial computers. In this study, the robot navigation control system initially filtered the information of a 3D greenhouse environment collected by a 3D Lidar and fused the information into 2D information, and then, based on the robot odometers and inertial measurement unit information, the system has achieved a timely positioning and construction of the greenhouse environment by a robot using a 2D Lidar SLAM algorithm in Cartographer. This method not only ensures the accuracy of a greenhouse environmental map but also reduces the performance requirements on the industrial computer. In terms of path planning, the Dijkstra algorithm was used to plan the global navigation path of the robot while the Dynamic Window Approach (DWA) algorithm was used to plan the local navigation path of the robot. Through the positioning test, the average position deviation of the robot from the target positioning point is less than 8 cm with a standard deviation (SD) of less than 3 cm; the average course deviation is less than 3° with an SD of less than 1° at the moving speed of 0.4 m/s. The robot moves at the speed of 0.2, 0.4, and 0.6 m/s, respectively; the average lateral deviation between the actual movement path and the target movement path is less than 10 cm, and the SD is less than 6 cm; the average course deviation is <3°, and the SD is <1.5°. Both the positioning accuracy and the navigation accuracy of the robot can meet the requirements of mobile navigation and positioning in the greenhouse environment.

Navigational control strategy of humanoid robots using average fuzzy-neuro-genetic hybrid technique

In this research paper navigational path planning of humanoid robots using developed average fuzzy-neuro-genetic hybrid technique has been analysed. Inputs to the hybrid controller are front, front-left and front-right obstacle distances and target location obtained from ultrasonic and image sensors of humanoid robot. Three Artificial Intelligence (AI) controllers such as, fuzzy logic, neural network and genetic algorithms have been used in parallel for robot navigation control. The outputs from sensors are fed as inputs to the hybrid controllers. The average output from the controller in the form of steering angle is used for robot dynamic movements while avoiding obstacles and reaching targets. A close agreement has been observed during comparison of simulation and experimental results.

Smart element aware gate controller for intelligent wheeled robot navigation

&lt;p&gt;The directing of a wheeled robot in an unknown moving environment with physical barriers is a difficult proposition. In particular, having an optimal or near-optimal path that avoids obstacles is a major challenge. In this paper, a modified neuro-controller mechanism is proposed for controlling the movement of an indoor mobile robot. The proposed mechanism is based on the design of a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. This controller is updated to overcome the rigid and dynamic barriers in the indoor area. The proposed controller is implemented with a mobile robot known as Khepera IV in a practical manner. The practical results demonstrate that the proposed mechanism is very efficient in terms of providing shortest distance to reach the goal with maximum velocity as compared with the MENN. Specifically, the MEEG is better than MENN in minimizing the error rate by 58.33%.&lt;/p&gt;

Optimization of a fuzzy controller for autonomous robot navigation using a new competitive multi-metaheuristic model

Optimization of a fuzzy controller for autonomous robot navigation using a new competitive multi-metaheuristic model

GWO-Potential Field Method for Mobile Robot Path Planning and Navigation Control

GWO-Potential Field Method for Mobile Robot Path Planning and Navigation Control

Using Ultrasonic Sensors and a Knowledge-Based Neural Fuzzy Controller for Mobile Robot Navigation Control

This study proposes a knowledge-based neural fuzzy controller (KNFC) for mobile robot navigation control. An effective knowledge-based cultural multi-strategy differential evolution (KCMDE) is used for adjusting the parameters of KNFC. The KNFC is applied in PIONEER 3-DX mobile robots to achieve automatic navigation and obstacle avoidance capabilities. A novel escape approach is proposed to enable robots to autonomously avoid special environments. The angle between the obstacle and robot is used and two thresholds are set to determine whether the robot entries into the special landmarks and to modify the robot behavior for avoiding dead ends. The experimental results show that the proposed KNFC based on the KCMDE algorithm has improved the learning ability and system performance by 15.59% and 79.01%, respectively, compared with the various differential evolution (DE) methods. Finally, the automatic navigation and obstacle avoidance capabilities of robots in unknown environments were verified for achieving the objective of mobile robot control.

Mapless LiDAR Navigation Control of Wheeled Mobile Robots Based on Deep Imitation Learning

This paper addresses the problems related to the mapless navigation control of wheeled mobile robots based on deep learning technology. The traditional navigation control framework is based on a global map of the environment, and its navigation performance depends on the quality of the global map. In this paper, we proposes a mapless Light Detection and Ranging (LiDAR) navigation control method for wheeled mobile robots based on deep imitation learning. The proposed method is a data-driven control method that directly uses LiDAR sensors and relative target position for mobile robot navigation control. A deep convolutional neural network (CNN) model is proposed to predict motion control commands of the mobile robot without the requirement of the global map to achieve navigation control of the mobile robot in unknown environments. While collecting the training dataset, we manipulated the mobile robot to avoid obstacles through manual control and recorded the raw data of the LiDAR sensor, the relative target position, and the corresponding motion control commands. Next, we applied a data augmentation method on the recorded samples to increase the number of training samples in the dataset. In the network model design, the proposed CNN model consists of a LiDAR CNN module to extract LiDAR features and a motion prediction module to predict the motion behavior of the robot. In the model training phase, the proposed CNN model learns the mapping between the input sensor data and the desired motion behavior through end-to-end imitation learning. Experimental results show that the proposed mapless LiDAR navigation control method can safely navigate the mobile robot in four unseen environments with an average success rate of 75%. Therefore, the proposed mapless LiDAR navigation control system is effective for robot navigation control in an unknown environment without the global map.

Design and analysis of a potential-based controller for safe robot navigation in unknown GPS-denied environments with strictly convex obstacles

Abstract In this paper, we propose an obstacle avoidance controller for a disk-shaped holonomic robot with double-integrator dynamics and local sensing. The control objective is for the robot to converge to a target velocity while avoiding collisions with strictly convex obstacles in an unbounded environment. We assume that the robot has no information about the location and geometry of the obstacles, has no localization capabilities, and can only measure its own velocity and its relative position vector to the closest point on any obstacles in its sensing range. We first propose a potential-based controller for the case with a single obstacle, and we prove that the robot safely navigates past the obstacle and attains the desired velocity. For the case with multiple obstacles, we propose a switching control scheme in which the robot applies the single-obstacle controller for the closest obstacle at each instant. We investigate the correctness of this switching control law and demonstrate the absence of local stable equilibrium points that would trap the robot. We validate our analytical results through simulations of a robot that uses the proposed controllers to successfully navigate through an environment with strictly convex obstacles of various shapes and sizes.