Navigation Control Research Articles

An Unmanned Surface Vehicle (USV): Development of an Autonomous Boat with a Sensor Integration System for Bathymetric Surveys.

A reliable yet economical unmanned surface vehicle (USV) has been developed for the bathymetric surveying of lakes. The system combines an autonomous navigation framework, environmental sensors, and a multibeam echosounder to collect submerged topography, temperature, and wind speed and monitor the vehicle's status during prescribed path-planning missions. The main objective of this research is to provide a methodological framework to build an autonomous boat with independent decision-making, efficient control, and long-range navigation capabilities. Integration of sensors with navigation control enabled the automatization of position, orientation, and velocity. A solar power integration was also tested to control the duration of the autonomous missions. The results of the solar power compared favorably with those of the standard LiPO battery system. Extended and autonomous missions were achieved with the developed platform, which can also evaluate the danger level, weather circumstances, and energy consumption through real-time data analysis. With all the incorporated sensors and controls, this USV can make self-governing decisions and improve its safety. A technical evaluation of the proposed vehicle was conducted as a measurable metric of the reliability and robustness of the prototype. Overall, a reliable, economic, and self-powered autonomous system has been designed and built to retrieve bathymetric surveys as a first step to developing intelligent reconnaissance systems that combine field robotics with machine learning to make decisions and adapt to unknown environments.

Impact of backwater on water surface profile in curved channels

Owing to extensive construction of dams, the impact of backwater on flow may lead to navigation or flood control issues in curved channels. To date, the impact of backwater on the water surface profile in curved channels remains unknown and requires investigation. In this study, experiments were conducted in a glass-walled recirculating flume with a length of 19.4 m, a width of 0.6 m, and a depth of 0.8 m, and the impact of backwater on the water surface profile in a 90° channel bend was investigated. The experimental results showed that the backwater degree had a significant impact on the transverse and longitudinal flow depth distributions in the bend. The transverse slope of the flow (Jr) increased linearly with an increase in the Froude number of the approach flow upstream of the bend. Jr increased with the longitudinal location parameter ξ when −0.2 < ξ < 0.5, and decreased with ξ when 0.5 < ξ < 1.2. Furthermore, the results showed that Jr asymptotically decreased to zero with an increase in the degree of backwater. An equation was formulated to estimate the transverse slope of the flow in a 90° bend in backwater zones.

A New Approach for Stabilization Criteria of n-Order Function Differential Equation by Distributed Control Function

In the current paper, we demonstrate a new approach for an stabilization criteria for n-order functional-differential equation with distributed feedback control in the integral form. We present a correlation between the order of the functional-differential equation and degree of freedom of the distributed control function. We present two cases of distributed control function in the integral form. Such a case of stabilization control functions plays a very important role in physics, aeronautics, aerospace, ship navigation and traffic network control management. Structure of functional-differential equations is based on the symmetry properties.

Advanced, Innovative AIoT and Edge Computing for Unmanned Vehicle Systems in Factories

Post-COVID-19, there are frequent manpower shortages across industries. Many factories pursuing future technologies are actively developing smart factories and introducing automation equipment to improve factory manufacturing efficiency. However, the delay and unreliability of existing wireless communication make it difficult to meet the needs of AGV navigation. Selecting the right sensor, reliable communication, and navigation control technology remains a challenging issue for system integrators. Most of today’s unmanned vehicles use expensive sensors or require new infrastructure to be deployed, impeding their widespread adoption. In this paper, we have developed a self-learning and efficient image recognition algorithm. We developed an unmanned vehicle system that can navigate without adding any specialized infrastructure, and tested it in the factory to verify its usability. The novelties of this system are that we have developed an unmanned vehicle system without any additional infrastructure, and we developed a rapid image recognition algorithm for unmanned vehicle systems to improve navigation safety. The core contribution of this system is that the system can navigate smoothly without expensive sensors and without any additional infrastructure. It can simultaneously support a large number of unmanned vehicle systems in a factory.

Intelligent robot for glass cleaning at high altitude based on rotating shaft connection and stm32 control

At present, there are few high-altitude glass cleaning equipment on the market that can cross obstacles, usually requiring manual transfer. Even with intelligent obstacle avoidance system, it is still difficult to climb over the floor, the frame between the glass and so on. The automation of the project is low. To solve the above problems, the glass cleaning robot designed in this paper can autonomic obstacle avoidance transfer through the stm32f407 core control board, and Raspberry PI to jointly complete the obstacle avoidance, navigation, movement and other control tasks. And the use of a new mechanical structure, through the quadcopter wing folding contraction, to complete the process of wiping glass push and pull. During the transition from flight to climbing, horizontal to vertical changes are achieved by folding the fuselage. Through many experiments, the design is reliable and practical.

Abstract 5679: Tetra-specific antibody GNC-035: guidance and navigation control (GNC) molecule development for treatment of ROR1+ malignancies

Abstract Cancer-intrinsic immune escape mechanisms and immune cell suppression can progressively diminish the curative potential of currently available T cell-based therapies. Barriers to successful T cell checkpoint therapies may be addressed by redirection of T cells toward tumor antigens using T cell engagers that function independently of MHC presented T cell epitopes. Here we demonstrate that an octavalent, tetraspecific Guidance and Navigation Control (GNC) antibody, GNC-035, binds to ROR1, CD3, PD-L1, and 4-1BB and mediates redirected T cell cytolysis of human solid tumor and leukemia and lymphoma cell lines in a ROR1 specific manner. Experiments using GNC-035 to redirect T cell cytotoxicity toward ROR1+ cancer cell targets show the T cells in PBMC are highly functionalized by pre-exposure to GNC-035. This pre-exposure of PBMC to GNC-035 results in greater tumor cell killing efficacy compared to concurrent exposure of tumor cells in the presence of T cell effectors. This result suggests that the systemic delivery of GNC-035 can condition the T cell compartment to increase the therapeutic impact of T cells migrating to solid tumors, with or without preexisting infiltrating T cells. This beneficial conditioning of T cells by pre-exposure to GNC-035 is not observed with pre-exposure to CD3xROR1 bi-specific T cell engager controls. To evaluate the potential for GNC-035 to mediate cytokine release syndrome, the molecule is evaluated in soluble formats in the presence of PBMC and the ROR1+ A549 cancer cells, or HUVEC cells. Under these conditions, the cytolysis of A549 target cells is detectable after exposure to GNC-035 at 100 fM concentrations as well as the release of IFN-γ and certain other inflammatory cytokines at 24 or 48 hours post-treatment. However, consistent with Blinatumomab treatment, PBMC exposed to soluble GNC-035 for 24 or 48 hours on a monolayer of HUVEC cells, produced significantly greater amounts of IFN-γ and IL-6 at concentrations greater than 10 pM. These results indicate GNC-035 has a therapeutic window of activity that is ROR1 dependent, spanning cytolytic activity, and IFN-γ release without a production of IL-6 and which is wider than that indicated by Blinatumomab in PBMC. Collectively, the GNC-035 represents a class of multi-specific and multi-modal immune cell engagers with potential to mediate ROR1+ cancer regression, overcome TCR-based immune escape and reverse T cell immune suppression in tumor microenvironment. The clinical phase I-b study of GNC-035 is under way in breast cancer and hematologic cancers and the available data exhibit strong signals of efficacy with acceptable tolerability. Citation Format: Jahan Salar Khalili, Sa Xiao, Yi Zhu. Tetra-specific antibody GNC-035: guidance and navigation control (GNC) molecule development for treatment of ROR1+ malignancies. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5679.

Abstract 5680: Tetra-specific antibody GNC-039: guidance and navigation control (GNC) molecule development for treatment of EGFRvIII+ malignancies

Abstract One of the primary challenges in the targeting of the tumor specific antigen EGFRvIII is the expression of the antigen among tumor cells in cranial glioblastoma tumors. The development of GNC-039 is based on the capability of this protein to redirect T cell cytolysis toward the tumor specific antigen EGFRvIII and guide T cells in the tumor microenvironment. Here we demonstrate that the tetraspecific Guidance and Navigation Control (GNC) antibody, GNC-039, binds to EGFRvIII, CD3, PD-L1, and 4-1BB and mediates T cell cytolysis of the human glioblastoma cancer cell line U87 expressing EGFRvIII in the in vitro tumor spheroid model. When delivered intravenously, the biodistribution of GNC-039 is an important factor in the development of this intracranial tumor targeting biologic. To better understand the biodistribution of GNC-039, Orthotopic Patient Derived Xenograft (PDX) models of Glioma were utilized and vivo-tag680XL-labelled GNC-039 was evaluable by total flux in the Brain area. Multiple IV infusions of GNC-039 were carried out over the study period (29 Days). Groups received either GNC-039 (n=5), GNC-039 with engrafted PBMC (n=5), Temozolomide(n=5), or Vehicle (n=5). The PDX model was sensitive to Temozolomide, 0/5 mice residual tumor, medial overall survival (mOS) of 15 days. In the Vehicle treatment group, 5/5 mice had residual tumor, mOS was 26 days. Mice receiving GNC-039 without PBMC, could partially respond to the treatment, in this group 3/5 animal had residual brain tumors by end of study, but with only a mOS of 15 days. In these animals, the GNC-039 accumulated in the brain region to its maximal level by the third dose on Day 7 and stayed consistently at that level for the duration of the study period. However, in the mouse group with engraft PBMC, the level of GNC-039 in the brain region could exceed that of the drug when infused alone. Mice receiving GNC-039 with engrafted PBMC completely responded to treatment, 5/5 mice in the group had no residual brain tumor, and a mOS of 20 days. In the presence of the engrafted PBMC, the increased level of GNC-039 in the brain region was delayed compared with treatment in the absence of PBMC. As a point of comparison, the Day 7 levels from GNC-039 treatment alone were not reached until Day 15 in presence of PBMC. However, beyond this timepoint, the level of GNC-039 in the brain region was significantly increased due to the engrafted PBMC. Collectively this data indicates the functionality of GNC-039 as a multi-specific T cell engager with the potential to target EGFRvIII+ cancer cell cytolysis in primary brain disease. The clinical phase I-b study of GNC-039 is under way and the available data exhibit strong signals of efficacy with acceptable tolerability. Citation Format: Jahan Salar Khalili, Sa Xiao, Yi Zhu. Tetra-specific antibody GNC-039: guidance and navigation control (GNC) molecule development for treatment of EGFRvIII+ malignancies. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5680.

Abstract 5681: Tetra-specific antibody GNC-038: guidance and navigation gontrol (GNC) molecule development for treatment of CD19+ malignancies

Abstract B cell malignancies treated with CD19-directed immunotherapies can relapse, in some cases due to clonal selection for reduced CD19 antigen expression or enhancement of immunosuppressive phenotypes. Here we demonstrate that a Guidance and Navigation Control (GNC) tetra-specific antibody, GNC-038, binds to CD19, CD3, PD-L1, and 4-1BB and mediates cytolysis of human leukemia and lymphoma cells by T cells. Redirected T cell cytolysis (RTCC) occurs in the presence of GNC-038 (Emfizatamab), resulting in the killing of CD19+ leukemia and lymphoma cell lines. The cytolytic functions induced by GNC-038 are similar to Blinatumomab in vitro, as indicated by T cell degranulation and production of IFN-gamma. Human T cells in PBMC exposed to GNC-038 in vitro proliferate in a dose-dependent fashion. Proliferation is further enhanced upon rechallenge with leukemic target cells. Proliferation of T cells from individuals with higher % PD-1+ and Effector polarized compartments is enhanced by GNC-038 compared to Blinatumomab. To evaluate the contribution of each binding domain of GNC-038 in mediating RTCC function, versions of GNC-038 were prepared, replacing each antigen binding domain with anti-FITC binding domains. Under assay conditions using PBMC for RTCC toward the CD19+ target cell line Nalm-6, the results demonstrate the contribution of each domain to the overall, anti-leukemic cytolytic activity. To evaluate the potential for GNC-038 to mediate cytokine release syndrome, the molecule is evaluated in soluble, and plate bound formats in the presence of PBMC and CD19+ leukemic targets cells. In comparison to Blinatumomab, the production of cytokines is comparable, with some notable differences. PBMC exposed to soluble GNC-038 for 48 hours produced more IFN-γ, IL-2 and TNF-α, while showing no significant difference in production of IL-6. Based on these results, the primary indicator of CRS, IL-6, does not suggest increased risk compared to Blinatumomab, while the type of T cell activity induced by GNC-038 in PBMC with leukemia cells is distinct. Collectively, the GNC-038 represents a class of multi-specific and multi-modal immune cell engagers with potential to mediate CD19+ cancer killing, while also increasing the T cell compartment’s therapeutic potential to respond to T cell redirection upon subsequent cycles of therapy. The clinical phase I-b study of GNC-038 is under way and the available data exhibit strong signals of efficacy with acceptable tolerability. Citation Format: Jahan Salar Khalili, Sa Xiao, Yi Zhu. Tetra-specific antibody GNC-038: guidance and navigation gontrol (GNC) molecule development for treatment of CD19+ malignancies. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5681.

Medical Assistant Robot With Patient Trajectory Reading Based on Odometry using Photovoltaic source

Robotics technology is growing and being applied in various fields such as education, industry, household, and health. In the world of health, mobile robots are one of the robotics technologies that can provide solutions to overcome the problem of treatment carried out on patients infected with infectious diseases. With the medical assistant robot, treatment efforts can be carried out by not making direct contact with patients and nurses so that the risk of nurses contracting the disease can be reduced. Mobile robot is one type of robot that can move in all directions. Autonomous mobile robot is a type of robot that is able to move to a destination automatically. By utilizing this robot in health services, it can help several tasks of medical personnel, such as monitoring patients, as a delivery person for medicines, food, and goods. For the implementation of this autonomous mobile robot, a good navigation system and control system algorithm is needed. In this study, the principle of odometry will be implemented in tracking path on a medical assistant robot. In the experiment of adjusting the position of the robot’s motion, it can be seen that the robot can move according to the expected position by adding a PID control to correct the error value in the position of the robot.

Bio-Inspired Autonomous Navigation and Formation Controller for Differential Mobile Robots.

This article proposes a decentralized controller for differential mobile robots, providing autonomous navigation and obstacle avoidance by enforcing a formation toward trajectory tracking. The control system relies on dynamic modeling, which integrates evasion forces from obstacles, formation forces, and path-following forces. The resulting control loop can be seen as a dynamic extension of the kinematic model for the differential mobile robot, producing linear and angular velocities fed to the mobile robot's kinematic model and thus passed to the low-level wheel controller. Using the Lyapunov method, the closed-loop stability is proven for the non-collision case. Experimental and simulated results that support the stability analysis and the performance of the proposed controller are shown.

YOLO-Based UAV Technology: A Review of the Research and Its Applications

In recent decades, scientific and technological developments have continued to increase in speed, with researchers focusing not only on the innovation of single technologies but also on the cross-fertilization of multidisciplinary technologies. Unmanned aerial vehicle (UAV) technology has seen great progress in many aspects, such as geometric structure, flight characteristics, and navigation control. The You Only Look Once (YOLO) algorithm was developed and has been refined over the years to provide satisfactory performance for the real-time detection and classification of multiple targets. In the context of technology cross-fusion becoming a new focus, researchers have proposed YOLO-based UAV technology (YBUT) by integrating the above two technologies. This proposed integration succeeds in strengthening the application of emerging technologies and expanding the idea of the development of YOLO algorithms and drone technology. Therefore, this paper presents the development history of YBUT with reviews of the practical applications of YBUT in engineering, transportation, agriculture, automation, and other fields. The aim is to help new users to quickly understand YBUT and to help researchers, consumers, and stakeholders to quickly understand the research progress of the technology. The future of YBUT is also discussed to help explore the application of this technology in new areas.

Navigation and closed-loop control of magnetic microrobot in plant vein mimic environment.

In recent years, research on the manipulation and control of microrobot has gradually matured. In order to improve the intelligence of microrobots, navigation study also becomes an important research topic. In practice, microrobots could be disturbed by the flowing liquid when it moves in a microfluidic environment. As a result, the actual trajectory of microrobots will deviate from the intended one. In this paper, firstly, different algorithms for the navigation of microrobots in a simulated plant leaf vein environment are investigated. According to the simulation results, RRT*-Connect is then selected as the path planning algorithm with a relatively better performance. Based on the pre-planned trajectory, a fuzzy PID controller is further designed for precise trajectory tracking, which can effectively eliminate the random disturbance caused by micro-fluid flow during the motion and make it quickly recover to a stable movement state.

Ground Crash Area Estimation of Quadrotor Aircraft Under Propulsion Failure

Small unmanned aircraft systems or drones are expected to be used for different applications, such as parcel delivery, inspection, and aerial photography, in urban areas. However, drones usually use an electric system to power up the propulsion, communications, navigation, and flight control system, which means that it is not as reliable as the manned aircraft system and may result in failure during operation and then crash to the ground. At present, there is almost no extensive publication about the high-fidelity modeling used by drones to calculate the crash trajectory and point of crash. The experimental data for modeling and simulation verification of multirotor aircraft are limited. So far, crash trajectory prediction has been limited to point mass or ballistic methods, and these methods are usually only suitable for complete power failure and without any control system. This study intends to investigate the effects of different multirotor drones’ failure modes on its crash trajectory and crash area compared to the ballistic model by using ADAMS and MATLAB cosimulation methods. Conclusions from the study show the crash trajectory, flight distance, and impact speed of the drones under four failure modes, which are quite different from the ballistic trajectory. The findings can potentially contribute to better risk assessment of the multirotor drones for the urban environment operation.

Model-based and machine learning-based high-level controller for autonomous vehicle navigation: lane centering and obstacles avoidance

&lt;p&gt;Researchers have been attempting to make the car drive autonomously. The environment perception together with safe guidance and control is an important task and are one of the big challenges when developing this kind of system. Geometrical or physical based models, machine learning based models and those based on a mixture of both models, are the three types of navigation methods used to resolve this problem. The last method takes advantage of the learning capability of machine learning models and uses the safeness of geometric models in order to better perform the navigation task. This paper presents a hybrid autonomous navigation methodology, which takes advantage of the learning capability of machine learning and uses the safeness of the dynamic window approach geometric method. Using a single camera and a 2D lidar sensor, this method actuates as a high-level controller, where optimal vehicle velocities are found, then applied by a low-level controller. The final algorithm is validated on CARLA Simulator environment, where the system proved to be capable to guide the vehicle in order to achieve the following tasks: lane keeping and obstacle avoidance.&lt;/p&gt;

Design and Evaluation of the SoftSCREEN Capsule for Colonoscopy

Colonoscopy is considered the golden standard for cancer screening of the lower gastrointestinal (GI) tract. Nonetheless, conventional colonoscopy can cause discomfort to patients due to the forces occurring between colonoscopes and the walls of the colon. Robotic solutions have been proposed to reduce discomfort, and improve accessibility and image quality. Aiming at addressing the limitations of traditional and robotic colonoscopy, in this paper, we present the SoftSCREEN System – a novel Soft Shape-shifting Capsule Robot for Endoscopy based on Eversion Navigation. A plurality of tracks surrounds the body of the system. These tracks are driven by a single motor paired with a worm gear and evert from the internal rigid chassis, enabling full-body track-based navigation. Two inflatable toroidal chambers enclosing this rigid chassis and passing through the tracks, cause them to displace when inflated. This displacement can be used to regulate the contact with the surrounding wall, thus enabling traction control and adjustment of the overall system diameter to match the local lumen size. The design of the first tethered prototype at 2:1 scale of the SoftSCREEN system is presented in this work. The experimental results show efficient navigation capabilities for different lumen diameters and curvatures, paving the way for a novel robot capable of robust navigation and reliable control of the imaging, with potential for applications beyond colonoscopy, including gastroscopy and capsule endoscopy.

Deep reinforcement learning based controller for ship navigation

A majority of marine accidents that occur can be attributed to errors in human decisions. Through automation, the occurrence of such incidents can be minimized. Therefore, automation in the marine industry has been receiving increased attention in the recent years. This paper investigates the automation of the path following action of a ship. A deep Q-learning approach is proposed to solve the path-following problem of a ship. This method comes under the broader area of deep reinforcement learning (DRL) and is well suited for such tasks, as it can learn to take optimal decisions through sufficient experience. This algorithm also balances the exploration and the exploitation schemes of an agent operating in an environment. A three-degree-of-freedom (3-DOF) dynamic model is adopted to describe the ship’s motion. The Krisco container ship (KCS) is chosen for this study as it is a benchmark hull that is used in several studies and its hydrodynamic coefficients are readily available for numerical modeling. Numerical simulations for the turning circle and zig-zag maneuver tests are performed to verify the accuracy of the proposed dynamic model. A reinforcement learning (RL) agent is trained to interact with this numerical model to achieve waypoint tracking. Finally, the proposed approach is investigated not only by numerical simulations but also by model experiments using 1:75.5 scaled model.

Intelligent PIV Fuzzy Navigation and Attitude Controller for an Octorotor Mini-UAV

In this research, a proportional plus integral plus velocity (PIV) fuzzy gain scheduling flight controller for an octorotor mini-unmanned aerial vehicle is developed. The designed flight controller scheme, with a PIV term, is combined with a fuzzy gain scheduling approach. The tracking controller PIV fuzzy gain scheduling is based on two controllers connected in cascade with a saturation approach. The Newton–Euler equations of motion are applied to obtain a mathematical model for the octorotor mini-unmanned aerial vehicle (mini-UAV). The flight controller approach is applied to obtain coupling moments and forces with interconnected attitude and navigation tracking trajectory. In the design of a flight navigation controller with two layers, the inner layer consists of a PIV fuzzy gain scheduling controller that is applied to the attitude dynamics, obtaining the references for the coupling outer layer PIV fuzzy gain scheduling controller, which manipulates the translational dynamics. The navigation PIV fuzzy gain scheduling controller is saturated for bounding in translational forces to avoid large deviations of commands to Euler angles pitch and roll, and another saturated controller is implemented for the bounded thrust rotor to avoid the excessive angular speed of these rotors. The octorotor mini-UAV flight navigation simulation is performed to validate the tracking control of a sequence of motions in each axis, which is presented as a validation for the proposed control scheme.

Force-torque (F-T) based multi-drone cooperative transport using fuzzy logic and low-cost and imprecise F-T sensor

This paper presents a novel approach to perform the task of cooperative transportation (CT) by using multiple quadcopter drones. A leader-follower approach is utilized. Considering that the leader drone can be controlled using different means, such as Proportional, Integral, and Derivative (PID) control or remote control by an operator, this paper focuses on designing the control scheme for the follower drone. This paper specifically considers outdoor application of such systems that requires usage of Global Positioning System (GPS) to receive the positional information of the drones. However, GPS has inherent errors of order of magnitude that can destabilize the system. In order to address this major limitation, a Force-Torque Feedback Controller (FT-FC) is proposed to control the follower drone. The FT-FC provides control based on the interaction forces and torques acting at the point of contact between the follower drone and the payload. Using such passive control schemes, drones are thus not required to communicate with each other. Fuzzy Logic (FL) is used to implement the FT-FC. Fuzzy Logic provides effective force-torque coordination between drones, emulates human behavior during CT, and allows use of noisy inexpensive force-torque sensors. This paper presents results from numerical simulations showing the effectiveness of the proposed controller for way-point navigation and complex trajectory tracking. Additionally, the effectiveness of the fuzzy-based FT-FC in terms of handling disturbances is also demonstrated. The proposed FL FT-FC is also experimentally validated for two applications viz. three-dimensional physical Human Drone Interaction (HDI) and CT.

Autonomous Machinery Management for Supervisory Risk Control Using Particle Swarm Optimization

Safe navigation for maritime autonomous surface ships (MASS) is a challenging task, and generally highly dependent on effective collaboration between multiple sub-systems in environments with various levels of uncertainty. This paper presents a novel methodology combining risk-based optimal control and path following with autonomous machinery management (AMM) for MASS navigation and supervisory risk control. Specifically, a risk-aware particle swarm optimization (PSO) scheme utilizes “time-to-grounding” predictions based on weather data and electronic navigational charts (ENC) to simultaneously control both the ship’s motion as well as the machinery system operation (MSO) mode during transit. The proposed autonomous navigation system (ANS) is comprised of an online receding horizon control that uses a PSO approach from previous works, which produces a dynamic risk-aware path with respect to grounding obstacles from a pre-planned MASS path, subsequently given as the input to a line-of-sight guidance controller for path following. Moreover, the MSO mode of the AMM system is simultaneously selected and assigned to explicit segments along the risk-aware path throughout the receding horizon, which effectively introduces into the optimization scheme an additional safety layer as well as another dimension for risk or resource minimization. The performance of the resulting ANS is demonstrated and verified through simulations of a challenging scenario and human assessment of the generated paths. The results show that the optimized paths are more efficient and in line with how human navigators would maneuver a ship close to nearby grounding obstacles, compared to the optimized paths of selected previous works.

Speed and navigation control of thymocyte development by the fetal T-cell gene regulatory network.

T-cell differentiation is a tightly regulated developmental program governed by interactions between transcription factors (TFs) and chromatin landscapes and affected by signals received from the thymic stroma. This process is marked by a series of checkpoints: T-lineage commitment, T-cell receptor (TCR)β selection, and positive and negative selection. Dynamically changing combinations of TFs drive differentiation along the T-lineage trajectory, through mechanisms that have been most extensively dissected in adult mouse T-lineage cells. However, fetal T-cell development differs from adult in ways that suggest that these TF mechanisms are not fully deterministic. The first wave of fetal T-cell differentiation occurs during a unique developmental window during thymic morphogenesis, shows more rapid kinetics of differentiation with fewer rounds of cell division, and gives rise to unique populations of innate lymphoid cells (ILCs) and invariant γδT cells that are not generated in the adult thymus. As the characteristic kinetics and progeny biases are cell-intrinsic properties of thymic progenitors, the differences could be based on distinct TF network circuitry within the progenitors themselves. Here, we review recent single-cell transcriptome data that illuminate the TF networks involved in T-cell differentiation in the fetal and adult mouse thymus.