Autonomous Navigation Capabilities Research Articles

Advances and challenges in UAV navigation based on visual SLAM

Abstract. In recent years, unmanned aerial vehicles (UAVs) have seen extensive use in fields such as agriculture, search and rescue, commercial, and military operations, driving the demand for autonomous navigation capabilities. Though GPS is the traditional method used for navigation, it becomes problematic in harsh environments like deserts. The Visual Simultaneous Localization and Mapping technology offers a solution to enhance UAV navigation in complex environments by constructing maps and localizing the UAV simultaneously in real time. This paper presents state-of-the-art visual SLAM technology developed for UAV navigation regarding algorithms like Oriented FAST and Rotated BRIEF SLAM (ORB-SLAM) and Large-Scale Direct Monocular SLAM (LSD-SLAM), whereby their performance is also discussed, with its positives and negatives. In this regard, the latest progress and challenges in applications are reviewed and analyzed through relevant literature from the databases of PubMed, IEEE, and Google Scholar in the past five years. The novelty of this paper lies in the comprehensive evaluation of the application performance of different visual SLAM algorithms in UAV navigation and the proposal of future research directions.

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.

Perform assessment of COLREGs onboard a maritime autonomous surface ship: Narrow Channels and Traffic Separation Schemes

Abstract The usage of Maritime Autonomous Surface Ships in coastal and heavily used maritime environments necessitates advanced navigation systems capable of fully complying with the International Regulations for Preventing Collisions at Sea 1972 and not only open sea encounters. A significant challenge in this domain is enabling autonomous vessels to understand and navigate complex collision avoidance scenarios dictated by Rule 9 Narrow Channels and Rule 10 Traffic Separation Schemes. These rules are not well covered by research and only partial solutions have been proposed. To address this, we propose a ruleset for a system that can blend the traffic situation and geometric constraints extracted from Electronic Navigational Charts in S-57 format. The proposed ruleset extends our previous work on operationalization of COLREGs by introducing spatial features describing the maritime geometries relevant for traffic situations in Narrow Channels and Traffic Separation Schemes. We apply the ruleset to various traffic situations, showing how the inclusion of geometric context improves decision-making by reducing ambiguity and therefore enables the assessment of traffic situations and ship encounters in the vicinity of Narrow Channels and Traffic Separation Schemes. Our work outlines how the integration of spatial features into a rule-based system enables autonomous navigation capabilities of MASS for safe operations and compliance with maritime regulations.

Optimizing Autonomous UAV Navigation with D* Algorithm for Sustainable Development

Autonomous navigation for Unmanned Aerial Vehicles (UAVs) has emerged as a critical enabler in various industries, from agriculture, delivery services, and surveillance to search and rescue operations. However, navigating UAVs in dynamic and unknown environments remains a formidable challenge. This paper explores the application of the D* algorithm, a prominent path-planning method rooted in artificial intelligence and widely used in robotics, alongside comparisons with other algorithms, such as A* and RRT*, to augment autonomous navigation capabilities in UAVs’ implication for sustainability development. The core problem addressed herein revolves around enhancing UAV navigation efficiency, safety, and adaptability in dynamic environments. The research methodology involves the integration of the D* algorithm into the UAV navigation system, enabling real-time adjustments and path planning that account for dynamic obstacles and evolving terrain conditions. The experimentation phase unfolds in simulated environments designed to mimic real-world scenarios and challenges. Comprehensive data collection, rigorous analysis, and performance evaluations paint a vivid picture of the D* algorithm’s efficacy in comparison to other navigation methods, such as A* and RRT*. Key findings indicate that the D* algorithm offers a compelling solution, providing UAVs with efficient, safe, and adaptable navigation capabilities. The results demonstrate a path planning efficiency improvement of 92%, a 5% reduction in collision rates, and an increase in safety margins by 2.3 m. This article addresses certain challenges and contributes by demonstrating the practical effectiveness of the D* algorithm, alongside comparisons with A* and RRT*, in enhancing autonomous UAV navigation and advancing aerial systems. Specifically, this study provides insights into the strengths and limitations of each algorithm, offering valuable guidance for researchers and practitioners in selecting the most suitable path-planning approach for their UAV applications. The implications of this research extend far and wide, with potential applications in industries such as agriculture, surveillance, disaster response, and more for sustainability.

Trajectory tracking control based on genetic algorithm and proportional integral derivative controller for two-wheel mobile robot

This paper uses the genetic algorithm (GA) to optimize the proportional integral derivative (PID) controller parameters to present the motion control design for a two-wheeled mobile robot autonomous system. The GA algorithm determines a collision-free travel curve for a robot with a tangential velocity restriction constraint. A trajectory-tracking controller based on the PID control structure is developed to monitor the calculated route curves for the mobile robot. Simulation results show the effectiveness of the GA-PID controller compared to the PID controller. The GA-PID controller demonstrates improved performance in trajectory tracking and collision avoidance, making it suitable for controlling the motion of two-wheeled mobile robots. The GA's optimization process allows for better tuning of the PID controller parameters, resulting in more efficient and accurate robot motion control. The results suggest that the proposed GA-PID controller is a promising approach for enhancing mobile robots' autonomous navigation capabilities.

Enhancing mobile robot navigation: integrating reactive autonomy through deep learning and fuzzy behavior

Objective: This study aimed to develop a control architecture for reactive autonomous navigation of a mobile robot by integrating Deep Learning techniques and fuzzy behaviors based on traffic signal recognition. Materials: The research utilized transfer learning with the Inception V3 network as a base for training a neural network to identify traffic signals. The experiments were conducted using a Donkey-Car, an Ackermann-steering-type open-source mobile robot, with inherent computational limitations. Results: The implementation of the transfer learning technique yielded a satisfactory result, achieving a high accuracy of 96.2% in identifying traffic signals. However, challenges were encountered due to delays in frames per second (FPS) during testing tracks, attributed to the Raspberry Pi's limited computational capacity. Conclusions: By combining Deep Learning and fuzzy behaviors, the study demonstrated the effectiveness of the control architecture in enhancing the robot's autonomous navigation capabilities. The integration of pre-trained models and fuzzy logic provided adaptability and responsiveness to dynamic traffic scenarios. Future research could focus on optimizing system parameters and exploring applications in more complex environments to further advance autonomous robotics and artificial intelligence technologies.

VR map construction for orchard robot teleoperation based on dual-source positioning and sparse point cloud segmentation

The unstructured nature of orchard environments presents significant challenges for autonomous navigation of orchard robots. Teleoperation, combined with virtual reality (VR), has emerged as a promising solution to overcome the limitations of on-board autonomous navigation capabilities in general-purpose robots. However, constructing accurate and semantically meaningful VR maps for orchard environments remains a challenge. This study proposes a novel VR map construction framework for orchard robot teleoperation visualization. It comprises two key components: dual-source combined positioning and semantic segmentation based on sparse point clouds. First, a novel sliding window-based data fusion approach is proposed to address the positioning challenges in semi-obstructed orchard environments. This approach combines GNSS data and laser matching to achieve robust and accurate positioning for orchard robots. Second, the framework capitalizes on the installation characteristics of mobile robot radars to achieve real-time classification of sparse point clouds. This allows for the removal of unstable elements, such as leaves, which would hinder effective teleoperation. By integrating these components, a VR map suitable for VR teleoperation of orchard robots is achieved. To validate the effectiveness of the proposed method, a VR teleoperation platform was constructed. The experimental results demonstrate that the method successfully fulfills the fundamental requirements for map visualization during VR remote operation of orchard robots. Furthermore, this study provides a valuable reference for the application of digital twins in agricultural robotics.

A Sensor Fusion Approach to Observe Quadrotor Velocity.

The growing use of Unmanned Aerial Vehicles (UAVs) raises the need to improve their autonomous navigation capabilities. Visual odometry allows for dispensing positioning systems, such as GPS, especially on indoor flights. This paper reports an effort toward UAV autonomous navigation by proposing a translational velocity observer based on inertial and visual measurements for a quadrotor. The proposed observer complementarily fuses available measurements from different domains and is synthesized following the Immersion and Invariance observer design technique. A formal Lyapunov-based observer error convergence to zero is provided. The proposed observer algorithm is evaluated using numerical simulations in the Parrot Mambo Minidrone App from Simulink-Matlab.

A Low-Cost 3D SLAM System Integration of Autonomous Exploration Based on Fast-ICP Enhanced LiDAR-Inertial Odometry

Advancements in robotics and mapping technology have spotlighted the development of Simultaneous Localization and Mapping (SLAM) systems as a key research area. However, the high cost of advanced SLAM systems poses a significant barrier to research and development in the field, while many low-cost SLAM systems, operating under resource constraints, fail to achieve high-precision real-time mapping and localization, rendering them unsuitable for practical applications. This paper introduces a cost-effective SLAM system design that maintains high performance while significantly reducing costs. Our approach utilizes economical components and efficient algorithms, addressing the high-cost barrier in the field. First, we developed a robust robotic platform based on a traditional four-wheeled vehicle structure, enhancing flexibility and load capacity. Then, we adapted the SLAM algorithm using the LiDAR-inertial Odometry framework coupled with the Fast Iterative Closest Point (ICP) algorithm to balance accuracy and real-time performance. Finally, we integrated the 3D multi-goal Rapidly exploring Random Tree (RRT) algorithm with Nonlinear Model Predictive Control (NMPC) for autonomous exploration in complex environments. Comprehensive experimental results confirm the system’s capability for real-time, autonomous navigation and mapping in intricate indoor settings, rivaling more expensive SLAM systems in accuracy and efficiency at a lower cost. Our research results are published as open access, facilitating greater accessibility and collaboration.

Autonomous Agricultural Drone with Sprinkling System

The quadcopter is one of the most widely used varieties of Unmanned Aerial Vehicles, or UAVs. This paper aims to build a drone with autonomous navigation capabilities. A sprinkling system was installed over the quadrotor to spray the desired fertilizers in the given area. The process of creating a quadrotor using a mechatronic design methodology—which considers the design of mechanical, electrical, software, and control components simultaneously—is explained in the article. The simulation findings for pitch and roll altitude control are also presented in the study. The investigation's findings include suggestions for choosing motors, propellers, batteries, and electrical speed controls wisely so that anyone with a desire might construct one by themselves.

Design and Testing of an Autonomous Navigation Unmanned Surface Vehicle for Buoy Inspection

In response to the inefficiencies and high costs associated with manual buoy inspection, this paper presents the design and testing of an Autonomous Navigation Unmanned Surface Vehicle (USV) tailored for this purpose. The research is structured into three main components: Firstly, the hardware framework and communication system of the USV are detailed, incorporating the Robot Operating System (ROS) and additional nodes to meet practical requirements. Furthermore, a buoy tracking system utilizing the Kernelized Correlation Filter (KCF) algorithm is introduced. Secondly, buoy image training is conducted using the YOLOv7 object detection algorithm, establishing a robust model for accurate buoy state recognition. Finally, an improved Line-of-Sight (LOS) method for USV path tracking, assuming the presence of an attraction potential field around the inspected buoy, is proposed to enable a comprehensive 360-degree inspection. Experimental testing includes validation of buoy image target tracking and detection, assessment of USV autonomous navigation and obstacle avoidance capabilities, and evaluation of the enhanced LOS path tracking algorithm. The results demonstrate the USV’s efficacy in conducting practical buoy inspection missions. This research contributes insights and advancements to the fields of maritime patrol and routine buoy inspections.

REVIEWING THE ROLE OF AI IN DRONE TECHNOLOGY AND APPLICATIONS

This comprehensive review delves into the transformative impact of artificial intelligence (AI) on drone technology, examining its pivotal role in revolutionizing various applications. As drones continue to evolve from recreational gadgets to indispensable tools across industries, the integration of AI enhances their capabilities, enabling advanced functionalities and expanding their potential use cases. The convergence of AI and drone technology has given rise to a myriad of applications, transforming industries ranging from agriculture to surveillance. Machine learning algorithms empower drones with autonomous navigation capabilities, allowing them to navigate complex environments and adapt to dynamic scenarios. Computer vision technologies enable drones to perceive and analyze visual information, facilitating tasks such as object recognition, tracking, and environmental monitoring. These advancements significantly contribute to enhanced aerial surveying, precision agriculture, and disaster response efforts. In the realm of precision agriculture, AI-equipped drones aid in crop monitoring, disease detection, and yield estimation, optimizing resource allocation and boosting agricultural productivity. Drones with AI-driven capabilities are increasingly employed in environmental monitoring, wildlife conservation, and disaster response, providing real-time data for efficient decision-making. Recent trends in AI-infused drone technology highlight its dynamic evolution. Edge computing solutions empower drones to process data locally, reducing latency and enhancing real-time responsiveness. Reinforcement learning algorithms enable drones to learn from their experiences, adapting and optimizing their performance over time. Swarm intelligence, an emerging field in drone technology, leverages AI to enable coordinated and synchronized actions among multiple drones, expanding their capabilities for collaborative tasks. In conclusion, this review sheds light on the pivotal role of AI in transforming drone technology and expanding its applications. The synergy between AI and drones has unlocked new possibilities across various industries, ranging from agriculture to disaster response. As technology continues to advance, the collaborative integration of AI and drones promises to redefine the future of aerial technology, introducing unprecedented efficiencies and capabilities across diverse sectors.
 Keywords: Role, AI, Drone, Applications, Technology.

Dynamics of changes in broiler spatial distribution induced by a robot with autonomous navigation along the growing cycle

Welfare problems in broiler chickens are associated with accelerated growth in high density and barren environments. Encouraging broiler movement yields benefits by increasing locomotion, foraging, and environmental exploration. Robot sensors with autonomous navigation capabilities developed to collect husbandry information could collaterally induce movement of birds while traversing the chicken houses. This study examines the short-time dynamic of changes in broiler spatial distribution within the robot's zone of influence throughout the growing cycle. Two batches of mixed-sex Cobb-500 were raised in a commercial broiler farm until 42 d of age, in 2 houses divided into 4 equally sized sectors. In half of the sectors an AviSense robot sustained 2-h per day of autonomous navigation. The minute prior and the 4 min following the robot entering the zone of influence were video recorded weekly. Control sectors without a robot were analyzed equivalently. Number of individuals within the zone of influence of the robot were obtained at 1-s intervals and relative density (%) was estimated. Physical interactions between broilers and the robot, as well as interactions with the environment were also recorded. The entrance of the robot triggers within seconds a strong depopulation of the zone with birds walking to neighboring areas (P < 0.03, in all ages). The decreases in relative density induced by the robot appears more pronounced, and repopulation of the zone was slower, in younger than in older birds (P < 0.05). Broilers´ showed physical interactions towards the robot and were also touched and/or slightly pushed by the robots (19 and 84% of videos recorded, respectively). They were also found scratching and/or pecking the ground after the robot passed (64% of videos). Findings strongly suggest that robots, beyond their specific capabilities as environmental sensors, were effective in promoting increased movement in broilers along the growing cycle and could also favor additional exploratory behaviors. Thus, these robots could be considered as environmental enrichment elements that contribute to welfare improvements during intensive rearing.

Path planning of autonomous underwater vehicle in unknown environment based on improved deep reinforcement learning

In order to improve the autonomous navigation capability of autonomous underwater vehicles (AUV), this paper applies Deep Reinforcement Learning (DRL) to realize autonomous path planning. In response to the limited detection range and difficult tracking DRL policy in three-dimensional unknown environments, this paper proposes an improved deep reinforcement learning method. Firstly, performing AUV modeling and processing obstacle detection information. Second, under a framework of reinforcement learning with the control system, an improved environmental interaction method was designed by analyzing the motion characteristics and controller performance of the AUV. Finally, based on this method, a policy network that can generate paths that satisfy obstacle avoidance and navigation is trained in combination with a twin delayed deterministic policy gradient (TD3). The simulation results show that the proposed method can achieve AUV path planning in a three-dimensional unknown environment. The proposed framework can ensure that the planned path meets the dynamics constraints and is trackable. The improved algorithm can enhance the convergence of training and the attitude stability of AUV.

Accurate semi‐direct lidar‐inertial odometry based on distance and normal direction

AbstractTo improve the autonomous navigation capability of unmanned platforms, an accurate semi‐direct lidar‐inertial odometry algorithm with a new point cloud alignment evaluation metric is proposed. The lidar scan points are corrected through IMU (Inertial Measurement Unit) backward propagation, and the navigation states are predicted through IMU forward propagation. In the update of the navigation states, the point cloud alignment is evaluated based on the distances and the minimum normal direction deviations between the lidar scan points and the local fitted planes of the built map, achieving accurate observations for the navigation states. The proposed algorithm updates navigation states based on the iterative extended Kalman filter. Experiments indicate that the proposed algorithm has higher positioning accuracy than the state‐of‐the‐art method, which gets smaller positioning error (7.868 m) and 52.3% improvement on average.

Security Drone for Surveillance in Military

Abstract: This paper presents the design and development of a sophisticated military security drone equipped with advanced components including Pixhawk flight controller, GPS module, camera, receiver, telemetry system, BLDC (Brushless Direct Current) motors, and electronic speed controller (ESC). The Pixhawk flight controller serves as the brain of the drone, managing flight dynamics, navigation, and mission planning with remarkable precision and reliability. Integrated with a highaccuracy GPS module, the drone achieves autonomous navigation capabilities, enabling it to execute pre-defined flight paths and perform missions with minimal human intervention. A high-resolution camera system is incorporated into the drone, providing real-time video surveillance and reconnaissance capabilities. This camera system is complemented by a receiver module, facilitating remote control and data transmission between the drone and ground control station.Furthermore, the telemetry system enables remote monitoring of the drone's vital parameters, including altitude, speed, battery status, and sensor readings. This real-time telemetry data enhances situational awareness and facilitates informed decision-making during missions.The propulsion system of the drone consists of BLDC motors and electronic speed controllers, offering efficient and reliable propulsion for sustained flight operations. These components are carefully integrated to optimize power consumption, flight endurance, and maneuverability, ensuring the drone's effectiveness in various operational scenarios. In summary, the integration of Pixhawk flight controller, GPS module, camera, receiver, telemetry, BLDC motors, and electronic speed controller in the design of the military security drone represents a significant advancement in unmanned aerial technology. The resulting drone platform offers enhanced capabilities for surveillance, reconnaissance, and security applications, contributing to the effectiveness and efficiency of military operations in diverse environments

Vision System for a Forestry Navigation Machine.

This article presents the development of a vision system designed to enhance the autonomous navigation capabilities of robots in complex forest environments. Leveraging RGBD and thermic cameras, specifically the Intel RealSense 435i and FLIR ADK, the system integrates diverse visual sensors with advanced image processing algorithms. This integration enables robots to make real-time decisions, recognize obstacles, and dynamically adjust their trajectories during operation. The article focuses on the architectural aspects of the system, emphasizing the role of sensors and the formulation of algorithms crucial for ensuring safety during robot navigation in challenging forest terrains. Additionally, the article discusses the training of two datasets specifically tailored to forest environments, aiming to evaluate their impact on autonomous navigation. Tests conducted in real forest conditions affirm the effectiveness of the developed vision system. The results underscore the system's pivotal contribution to the autonomous navigation of robots in forest environments.

Collaborative Path Planning of Multiple AUVs Based on Adaptive Multi-Population PSO

Collaborative operations of multiple AUVs have been becoming increasingly popular and efficient in underwater tasks of marine applications. Autonomous navigation capability and cooperative control stability of multiple AUVs are crucial and challenging issues in underwater environments. To address the collaborative problem of path planning for multiple AUVs, this paper proposes an adaptive multi-population particle swarm optimization (AMP-PSO). In AMP-PSO, we design a grouping strategy of multi-population and an exchanging mechanism of particles between groups. We separate particles into one leader population and various follower populations according to their fitness. Firstly, in the grouping strategy, particles within the leader population are updated by both the leader population and follower populations so as to keep global optimization, while particles within the follower population are updated by their own group so as to keep local priority. Secondly, in the exchanging mechanism, particles are exchanged between the leader population and follower populations so as to improve multi-population diversity. To accommodate multi-population characteristics, an adaptive parameter configuration is also included to enhance the global search capability, convergence speed, and complex environment adaptability of AMP-PSO. In numerical experiments, we simulate various scenarios of collaborative path planning of multiple AUVs in an underwater environment. The simulation results convincingly demonstrate that AMP-PSO can obtain feasible and optimal path solutions compared to classic PSO and other improved PSO, which enable multiple AUVs to effectively achieve objectives under the conditions of collision avoidance and navigation constraint.

Microstructure-Assisted Vision: Adding New Senses to Low-Power Devices

Miniature mobile robots are emerging with new capabilities and skills. Insect-sized and soft robots are just a few inches in size but capable of a range of tasks. They can act as first responders to search for survivors in disaster debris, perform mining or agricultural foraging, and even move heavy objects to organize. When paired with autonomous navigation capabilities, they can enable a wide range of applications, including surveillance, environmental monitoring, precision agriculture and beyond.

Advanced 3D Navigation System for AGV in Complex Smart Factory Environments

The advancement of Industry 4.0 has significantly propelled the widespread application of automated guided vehicle (AGV) systems within smart factories. As the structural diversity and complexity of smart factories escalate, the conventional two-dimensional plan-based navigation systems with fixed routes have become inadequate. Addressing this challenge, we devised a novel mobile robot navigation system encompassing foundational control, map construction positioning, and autonomous navigation functionalities. Initially, employing point cloud matching algorithms facilitated the construction of a three-dimensional point cloud map within indoor environments, subsequently converted into a navigational two-dimensional grid map. Simultaneously, the utilization of a multi-threaded normal distribution transform (NDT) algorithm enabled precise robot localization in three-dimensional settings. Leveraging grid maps and the robot’s inherent localization data, the A* algorithm was utilized for global path planning. Moreover, building upon the global path, the timed elastic band (TEB) algorithm was employed to establish a kinematic model, crucial for local obstacle avoidance planning. This research substantiated its findings through simulated experiments and real vehicle deployments: Mobile robots scanned environmental data via laser radar and constructing point clouds and grid maps. This facilitated centimeter-level localization and successful circumvention of static obstacles, while simultaneously charting optimal paths to bypass dynamic hindrances. The devised navigation system demonstrated commendable autonomous navigation capabilities. Experimental evidence showcased satisfactory accuracy in practical applications, with positioning errors of 3.6 cm along the x-axis, 3.3 cm along the y-axis, and 4.3° in orientation. This innovation stands to substantially alleviate the low navigation precision and sluggishness encountered by AGV vehicles within intricate smart factory environments, promising a favorable prospect for practical applications.