Autonomous Navigation Capabilities Research Articles

Unmanned RC Rover-Bot for Surveillance, Defense and Attack

The increasing demand for efficient and versatile robotic systems has led to the development of unmanned remote-controlled rover-bots. These advanced robotic platforms serve a crucial role in surveillance, defense, and attack operations. This paper presents a comprehensive overview of an unmanned remote-controlled rover-bot designed for versatile missions in the military and security sectors. The rover-bot integrates state-of-the-art technologies, including remote control, autonomous navigation, surveillance sensors, and offensive capabilities. The surveillance capability of the rover-bot is achieved through the integration of high- resolution cameras. These sensors enable real-time video streaming and enhanced situational awareness to the operator. The remote-control functionality allows operators to manipulate the rover-bot's movements, direction, and speed from a safe distance, reducing human exposure in hazardous environments. For defense purposes, the rover-bot is equipped with robust armour plating, providing protection against various threats. Additionally, it can detect and neutralize attacks, ensuring the safety of personnel. In offensive operations, the rover-bot is armed with advanced weapon systems, such as long-range projectiles and non-lethal munitions, enabling it to engage targets effectively. It can be operated at long range distance and can be made in to a succeed bot. To enhance the rover-bot's capabilities, enabling it to navigate complex terrains and avoid obstacles intelligently. This feature allows the rover-bot to operate in both urban and outdoor environments with minimal human intervention. The presented unmanned remote- controlled rover- bot demonstrates great potential in enhancing the effectiveness and safety of surveillance, defense, and attack operations. The integration of advanced technologies and its versatile design make it a valuable asset in military and security scenarios, improving the efficiency and effectiveness of missions while minimizing human risk.

Field experiment of autonomous ship navigation in canal and surrounding nearshore environments

AbstractIn this paper, we present the development of autonomous navigation capabilities for small cruise boats, and their verification by field experiments in a canal and its surrounding waters. A cruise boat was converted to an autonomous surface vehicle (ASV) by installing various sensors and actuators to enable autonomous navigation. Navigation and perception sensors, such as global positioning system, attitude and heading reference system, radar, light detection and ranging (LiDAR), and cameras, were mounted on the ASV to estimate its motion and perceive the surrounding environment. Motors and potentiometers were installed for active control of the ASV. Software system components including navigation filters, object‐detection, path‐planning, and control algorithms were designed and implemented. In the narrow canal region, LiDARs were used to detect the side walls and boundaries of the canal. In open areas outside the canal, obstacles and object features were detected using various combinations of onboard sensors. A model‐based path‐planning algorithm was designed to avoid the detected obstacles, and the line‐of‐sight guidance was employed to control the vehicle. The performance of the developed system was verified through a field experiment in a real‐world maritime environment.

Acoustic Localization of Drones in Precise Landing: The Research and Practice with MicNest

Delivery drones have the potential to revolutionize transportation and distribution of goods. With their autonomous navigation capabilities, they offer an efficient solution to bypass the challenges posed by complex urban traffic and enable instant package delivery. Many industrial firms are actively exploring the commercial feasibility of instant drone deliveries. Meituan, one of the largest companies in this area, devised a systematic approach to instant delivery using drones. The process begins with loading the package onto the drone, which then takes off, ascends to cruising altitude, and sets a direct course towards the designated destination. Typically, this destination is a Meituan-operated self-collection station located near the customer.

A multi-sensor indoor tracking system for autonomous marine model-scale vehicles

Attitude estimation is a popular topic in marine engineering and robotics; the position and orientation of a vehicle are required as feedback from several control algorithms to improve autonomous navigation capabilities, such as dynamic positioning, track keeping, and autodocking. Typically, position and heading angles are provided by the Global Positioning System and compass. Usually, during the development and testing, the experiments are performed in a controlled environment, such as an indoor test tank. However, Global Positioning System systems can be unreliable due to non-negligible model scale errors or the absence of line-of-sight with the satellites. This article presents an experimental tracking system setup suitable for indoor testing facilities. In particular, the paper presents a tracking system based on a GigE camera and ArUco markers detection and a LiDAR-based tracking system relying on unsupervised machine learning techniques. The MQTT broker-based publish/subscribe message-queuing protocol allows real-time data communication and sharing. The proposed system was developed, installed, and tested in the COMPASS laboratory (University of Genoa). The two tracking systems’ outcomes have been compared. Eventually, an accuracy analysis was performed by comparing the results to the ground truth in purpose-built experiments. The proposed approach can estimate the degrees of freedom of a self-propelled model-scale vessel in an indoor testing facility without requiring active or powered markers and share the information acquired with multiple entities in real-time at a high frame rate.

Multi-Sensors System and Deep Learning Models for Object Tracking.

Autonomous navigation relies on the crucial aspect of perceiving the environment to ensure the safe navigation of an autonomous platform, taking into consideration surrounding objects and their potential movements. Consequently, a fundamental requirement arises to accurately track and predict these objects' trajectories. Three deep recurrent network architectures were defined to achieve this, fine-tuning their weights to optimize the tracking process. The effectiveness of this proposed pipeline has been assessed, with diverse tracking scenarios demonstrated in both sub-urban and highway environments. The evaluations have yielded promising results, affirming the potential of this approach in enhancing autonomous navigation capabilities.

The Polarized Light Field Enables Underwater Unmanned Vehicle Bionic Autonomous Navigation and Automatic Control

In response to the critical need for autonomous navigation capabilities of underwater vehicles independent of satellites, this paper studies a novel navigation and control method based on underwater polarization patterns. We propose an underwater course angle measurement algorithm and develop underwater polarization detection equipment. By establishing the automatic control model of an ROV (Remote Operated Vehicle) with polarization information, we develop a strapdown navigation method combining polarization and inertial information. We verify the feasibility of angle measurement based on polarization in the water tank. The measurement accuracy of polarization azimuth is less than 0.69°. Next, we conduct ROV navigation at different water depths in a real underwater environment. At a depth of 5 m, the MSE (Mean Square Error) and SD (Standard Deviation) of angle error are 16.57° and 4.07°, respectively. Underwater navigation accuracy of traveling 100 m is better than 5 m within a depth of 5 m. Key technologies such as underwater polarization detection, multi-source information fusion, and the ROV automatic control model with polarization have been broken through. This method can effectively improve ROV underwater work efficiency and accuracy.

Robust Centroid Estimation Method with the Rough and Refined Estimation for Interplanetary Optical Navigation

As the interest in probing deep space increases, it is necessary to enhance the autonomous navigation capabilities of the spacecraft. Since traditional navigation methods rely on ground-based radiometric tracking, the vehicle has a significant communication delay resulting in no ability to handle unexpected situations on time. Image-based optical navigation allows interplanetary spacecraft to determine their orbits autonomously. This paper explores how to accurately extract optical observations from the original images to perform autonomous navigation. First, we introduce a simple and efficient idea to locate valuable contours of the celestial body based on gradient variations. Then, we establish a rough estimation with RANSAC to remove the outliers around the edges. Next, we propose a refined estimation based on the hybrid genetic algorithm to precisely estimate the navigation observations. Lastly, numerous experiments have confirmed that our method achieves outstanding accuracy and robustness.

Path Avoidance System of Intelligent Robot Based on Computer Vision

At present, obstacle avoidance technology is widely used in the military field, scientific detection, traffic control, industrial manufacturing, medical service, and many other fields, and various machines with autonomous navigation and obstacle avoidance capabilities have been used to varying degrees, thus replacing some human daily production activities and bringing many conveniences to people’s lives. The key to autonomous obstacle avoidance is to obtain the direction information of the obstacle in its direction of advance in real time, which is also the precondition of obstacle avoidance system in obstacle avoidance path planning. In this paper, an intelligent robot path avoidance system is designed based on computer vision (CV) theory. The binocular vision ranging experiment verifies that the system has a less ranging error and can effectively detect the location of obstacles within a certain range, and the success rate of intelligent robot avoidance of obstacles reaches more than 96%, achieving a good avoidance effect.

Design and Implementation of an Integrated Control System for Omnidirectional Mobile Robots in Industrial Logistics

The integration of intelligent robots in industrial production processes has the potential to significantly enhance efficiency and reduce human adversity. However, for such robots to effectively operate within human environments, it is critical that they possess an adequate understanding of their surroundings and are able to navigate through narrow aisles while avoiding both stationary and moving obstacles. In this research study, an omnidirectional automotive mobile robot has been designed for the purpose of performing industrial logistics tasks within heavy traffic and dynamic environments. A control system has been developed, which incorporates both high-level and low-level algorithms, and a graphical interface has been introduced for each control system. A highly efficient micro-controller, namely myRIO, has been utilized as the low-level computer to control the motors with an appropriate level of accuracy and robustness. Additionally, a Raspberry Pi 4, in conjunction with a remote PC, has been utilized for high-level decision making, such as mapping the experimental environment, path planning, and localization, through the utilization of multiple Lidar sensors, IMU, and odometry data generated by wheel encoders. In terms of software programming, LabVIEW has been employed for the low-level computer, and the Robot Operating System (ROS) has been utilized for the design of the higher-level software architecture. The proposed techniques discussed in this paper provide a solution for the development of medium- and large-category omnidirectional mobile robots with autonomous navigation and mapping capabilities.

Verification of the ROS NavFn planner using executable specification languages

The Robot Operating System (ROS) is a framework for building robust software for complex robot systems in several domains. The Navigation Stack stands out among the different libraries available in ROS, providing a set of components that can be reused to build robots with autonomous navigation capabilities. This library is a critical component, as navigation failures could have catastrophic consequences for applications like self-driving cars where safety is crucial.Here we devise a general methodology for verifying this kind of complex systems by specifying them in different executable specification languages with verification support and validating the equivalence between the specifications and the original system using differential testing techniques. The complex system can then be indirectly analyzed using the verification tools of the specification languages like model checking, semi-automated functional verification based on Hoare logic, and other formal techniques. In this paper we apply this verification methodology to the NavFn planner, which is the main planner component of the Navigation Stack of ROS, using Maude and Dafny as specification languages. We have formally proved several desirable properties of this planner algorithm like the absence of obstacles in the planned path. Moreover, we have found counterexamples for other concerns like the optimality of the path cost.

LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint

With the renewed interest in lunar surface exploration, the European Space Agency envisions to stimulate the creation of lunar communications and navigation services (LCNS) to enable, among others, autonomous navigation capabilities for lunar rovers. As the number of satellites foreseen in such a service is much smaller compared to Earth-based global navigation satellite systems, different complementary technologies are pursued to improve the attainable navigation accuracy for lunar rovers. One way to improve the position accuracy provided by the LCNS satellites is to constrain their vertical position using a high resolution digital elevation model (DEM). This article presents the results of a variance covariance analysis of an extended Kalman filter implementation in which the LCNS ranging measurements are used together with the altitude provided by a DEM from the Lunar Orbiter Laser Altimeter instrument of the Lunar Reconnaissance Orbiter. Assuming a realistic orbit determination and time synchronization accuracy of the LCNS satellites, the usage of a navigation-grade inertial measurement unit and an oven-controlled crystal oscillator, a 3-sigma position accuracy of less than 10 m can be obtained. Furthermore, the availability is substantially improved as the DEM-aided solution enables a position solution in case of only 3 visible satellites.

무인 수상선의 수중 추진기 고장 진단 연구를 위한 고장 특성 인자 선정 및 실험

As interest in unmanned vessel research has increased, emphasis on autonomous navigation capability and the sustainability of normal operation has been highlighted. However, research on fault detection and diagnosis (FDD) for unmanned surface vehicles (USVs) is insufficient compared with research on land machinery or unmanned aerial vehicles. Among prior studies on USV FDD, intensive analysis or diagnostics for individual elements with high failure probability were barely conducted. This study focus on fault detecting features of a USV thruster, one of the most failure-prone elements other than internal causes or software malfunctions. A hypothesis for thruster fault detecting features was made based on general thruster-related equations; the features were tested on a stand-alone thruster test to assess the feature’s acceptability. However, a stand-alone thruster test lacks important effects such as structural vibratory influence of an actual USV, thruster load difference due to inflow velocity and wave-induced vibrations as the USV slides on the water’s surface. Hence, USV capable of acquiring fine quality real-time data for FDD practice was developed, and a surface driving test in a water basin was conducted to confirm the eligibility of the selected features.

A Cascaded and Adaptive Visual Predictive Control Approach for Real-Time Dynamic Visual Servoing

In the past two decades, Unmanned Aerial Vehicles (UAVs) have gained attention in applications such as industrial inspection, search and rescue, mapping, and environment monitoring. However, the autonomous navigation capability of UAVs is aggravated in GPS-deprived areas such as indoors. As a result, vision-based control and guidance methods are sought. In this paper, a vision-based target-tracking problem is formulated in the form of a cascaded adaptive nonlinear Model Predictive Control (MPC) strategy. The proposed algorithm takes the kinematics/dynamics of the system, as well as physical and image constraints into consideration. An Extended Kalman Filter (EKF) is designed to estimate uncertain and/or time-varying parameters of the model. The control space is first divided into low and high levels, and then, they are parameterised via orthonormal basis network functions, which makes the optimisation- based control scheme computationally less expensive, therefore suitable for real-time implementation. A 2-DoF model helicopter, with a coupled nonlinear pitch/yaw dynamics, equipped with a front-looking monocular camera, was utilised for hypothesis testing and evaluation via experiments. Simulated and experimental results show that the proposed method allows the model helicopter to servo toward the target efficiently in real-time while taking kinematic and dynamic constraints into account. The simulation and experimental results are in good agreement and promising.

A real-time obstacle avoidance and path tracking strategy for a mobile robot using machine-learning and vision-based approach

In this paper, an obstacle avoidance and target tracking method for both indoor and outdoor mobile robots in dynamic environment is presented, and it aims to enhance autonomous navigation capability of such robots. In the proposed method, image processing and machine-learning approaches are considered. Since obstacles have differences in color and texture and in order to identify non-navigable areas, a monocular onboard camera is used to capture the road lanes by dealing with an image processing technique. The position of the robot with respect to road lane center during navigation is calculated on the basis of a proposed fuzzy logic rules set. In order to provide fast and robust computation, the Haar cascade classifier-based machine-learning technique has been exploited to detect the different sizes and shapes of the obstacles faced by the robot during its movement from source to destination. The dynamic model of a four-wheel mobile robot is initially developed using bond graph theory and then, the proposed obstacle-avoidance strategy is applied. The effectiveness and performance of the proposed method are tested under various simulation and experimentation scenarios. The behavior of the mobile robot for detecting and avoiding static obstacle for single and double road lane change environment is analyzed and discussed.

Fully Onboard AI-Powered Human-Drone Pose Estimation on Ultralow-Power Autonomous Flying Nano-UAVs

Many emerging applications of nano-sized unmanned aerial vehicles (UAVs), with a few cm² form-factor, revolve around safely interacting with humans in complex scenarios, for example, monitoring their activities or looking after people needing care. Such sophisticated autonomous functionality must be achieved while dealing with severe constraints in payload, battery, and power budget (&#x007E;<b>100mW</b>). In this work, we attack a complex task going from perception to control: to estimate and maintain the nano-UAV&#x2019;s relative 3-D pose with respect to a person while they freely move in the environment&#x2014;a task that, to the best of our knowledge, has never previously been targeted with fully onboard computation on a nano-sized UAV. Our approach is centered around a novel vision-based deep neural network (DNN), called Frontnet, designed for deployment on top of a parallel ultra-low power (PULP) processor aboard a nano-UAV. We present a vertically integrated approach starting from the DNN model design, training, and dataset augmentation down to 8-bit quantization and deployment in-field. PULP-Frontnet can operate in real-time (up to <b>135 frame/s</b>), consuming less than <b>87 mW</b> for processing at peak throughput and down to <b>0.43 mJ/frame</b> in the most energy-efficient operating point. Field experiments demonstrate a closed-loop top-notch autonomous navigation capability, with a tiny 27-g Crazyflie 2.1 nano-UAV. Compared against an ideal sensing setup, onboard pose inference yields excellent drone behavior in terms of median absolute errors, such as positional (onboard: <b>41cm</b>, ideal: <b>26 cm</b>) and angular (onboard: <b>3.7&#x00B0;</b>, ideal: <b>4.1&#x00B0;</b>). We publicly release videos and the source code of our work.

Rosetta CONSERT Data as a Testbed for In Situ Navigation of Space Probes and Radiosciences in Orbit/Escort Phases for Small Bodies of the Solar System

Many future space missions to asteroids and comets will implement autonomous or near-autonomous navigation, in order to save costly observation time from Earth tracking stations, improve the security of spacecraft and perform real-time operations. Existing Earth-Spacecraft-Earth tracking modes rely on severely limited Earth tracking station resources, with back-and-forth delays of up to several hours. In this paper, we investigate the use of CONSERT ranging data acquired in direct visibility between the lander Philae and the Rosetta orbiter, in the frame of the ESA space mission to comet 67P/Churyumov-Gerasimenko, as a proxy of autonomous navigation and orbitography science capability.

Agricultural rout planning with variable rate pesticide application in a greenhouse environment

The use of robotics in executing agricultural tasks has significantly improved productivity over the years as a result of automation in performing such activities as spray, harvesting, planting etc. In order to optimize both crop yield and quality while minimizing costs, there will be need for the application of navigation strategies. These will provide optimal as well as autonomous navigation capability which is built entirely upon field coverage plan thereby making robot navigation approach a paramount scheme. In this paper, the autonomy of an agricultural mobile robot is enhanced in a structured environment (greenhouse farm) to locate an optimum route such that the robot performs a selective and variable spray of pesticides to the plants. To realize this, a robust vehicle routing problem (VRP) scheme is designed to navigate the robot autonomously while making intelligent decisions to fulfil the pesticide demands at each node (infected plants). The improved non-dominated sorting genetic algorithm (INSGA-III) is adopted to solve this fully integer problem based on three (3) test cases carried out with 8, 32 and 56 infected plants respectively for validation. The results obtained show a trade-off solution as the Optimal INSGA-III is significantly lower than NSGA-III in terms of solution quality. On the other hand, a significant reduction in run times of between 66% and 76% and 76–93% was obtained for all test case scenarios for population sizes of 100 and 1500 respectively.

Research and verification of the autonomous navigation technology of GNSS

This paper discusses the important role of autonomous navigation capability in the development of Global Navigation Satellite Systems (GNSS) based on the current status of major GNSS designs. The architecture design for autonomous navigation by a GNSS, mainly comprising the system composition and operational process, is discussed in detail. The key technology that maintains the time and spatial reference of an autonomous navigation system is also discussed. This paper then studies the filtering algorithm, the filtering state noise covariance matrix, and the processing method of satellite-borne data based on inter-satellite observations. It also proposes a semi-autonomous navigation method based on ground observation data (anchor stations), and compares the performances of automatic navigation with different layouts and numbers of anchor stations and different observation periods. The autonomous navigation performance was evaluated in a ground-simulation test system under different GNSS deployment stages and operation modes. The accuracy of orbit determination and time synchronization of GNSS were then verified in fully autonomous and semi-autonomous navigation modes. The results provide an important reference for automating the navigation service of GNSS.

Haptic teleoperation of a multirotor aerial robot using path planning with human intention estimation

Classical haptic teleoperation systems heavily rely on operators’ intelligence and efforts in aerial robot navigation tasks, thereby posing significantly users’ workloads. In this paper, a novel shared control scheme is presented facilitating a multirotor aerial robot haptic teleoperation system that exhibits autonomous navigation capability. A hidden Markov model filter is proposed to identify the intention state of operator based on human inputs from haptic master device, which is subsequently adopted to derive goal position for a heuristic sampling based local path planner. The human inputs are considered as commanded velocity for a trajectory servo controller to drive the robot along the planned path. In addition, vehicle velocity is perceived by the user via haptic feedback on master device to enhance situation awareness and navigation safety of the user. An experimental study was conducted in a simulated and a physical environment, and the results verify the effectiveness of the novel scheme in safe navigation of aerial robots. A user study was carried out between a classical haptic teleoperation system and the proposed approach in the identical simulated complex environment. The flight data and task load index (TLX) are acquired and analyzed. Compared with the conventional haptic teleoperation scheme, the proposed scheme exhibits superior performance in safe and fast navigation of the multirotor vehicle, and is also of low task and cognitive loads.

Formal approach to thwart against drone discovery attacks: A taxonomy of novel 3D obfuscation mechanisms

The pervasive capabilities and myriad of mission performance abilities of Unmanned (Combat) Aerial Vehicles (UAVs/UCAVs) have exponentially grown their deployment possibilities in the recent past. Advancements in artificial intelligence, sensing technologies and autonomous guidance, navigation and control capabilities have further fueled wide-scale deployments of UAVs, both for military and commercial applications, ranging from autonomous air taxis and cargo deliveries to intelligence surveillance, reconnaissance, and combat missions. Most of these applications consume Global Navigation Satellite System (GNSS) based location information for their services, which is also shared in real-time with ground control stations and centralized service operators, often using insecure communication channels. This limitation has significantly raised the location privacy concerns of aerial vehicles, deployed to conduct user-centric, safety-critical and localization-sensitive operations. A compromise of location privacy of a UAV can pose serious threats, including stalking, theft or damage of UAV/payload or even use of GNSS-guided munitions. These emerging threats call for robust and trust-worthy solutions for preserving the location privacy of aerial vehicles.This paper proposes a novel obfuscation-based mechanism to safeguard location information against privacy attacks. Our proposed solution conceals actual information by transmitting modified location parameters, either after diluting their accuracy or by fabricating deceptive trajectories for a known eavesdropper. Based on these two broad categories, defined as Attenuation and Deception-based obfuscation techniques respectively, we also present a novel taxonomy of 3D obfuscation mechanisms, supported by formal descriptions of underlying operators. The operators can be used independently or in conjunction to satisfy diverse mission-specific obfuscation profiles. The proposed operators have been practically implemented and evaluated using a customizable obfuscator deployed over a Global Positioning System (GPS) guided UAV. The field experiments validate the efficacy, security and deployability of the proposed solution against location-privacy threats.