Topic Editors

School of Mechanical and Control Engineering, Handong Global University, Pohang, Republic of Korea
College of Aerospace, Beijing Institute of Technology, Beijing 100081, China
SARTI Research Group, Electronics Department, Universitat Politècnica de Catalunya, 08800 Vilanova i la Geltrú, Spain

Target Tracking, Guidance, and Navigation for Autonomous Systems

Abstract submission deadline
closed (30 November 2023)
Manuscript submission deadline
20 February 2024
Viewed by
19659

Topic Information

Dear Colleagues,

Growing civilian and military demand for autonomous systems, including unmanned vehicles, has promoted the development of modern target tracking, guidance, and navigation technologies. Target information is vital for autonomous systems to interact with their surrounding environments, enabling them to complete their missions. However, the quality of target information is affected by the motion of the system itself, which implies that the target-tracking problem is inseparable from the guidance and navigation of autonomous systems. Modern technologies such as model-/data-driven estimation, heterogeneous data fusion, optimization, and artificial intelligence can improve target-tracking systems and, subsequently, change the overall performance of guidance and navigation. This Special Issue aims to identify recent theoretical and technical advances in target tracking, guidance, and navigation, which provide autonomous systems with a high degree of autonomy. Related topics include, but are not limited to: 

  • Tracking maneuvering targets in cluttered/jammed environments;
  • Joint target tracking and classification using heterogenous sensors;
  • Centralized/distributed multi-sensor fusion;
  • Optimal sensor arrangement;
  • Guidance, navigation, and control of autonomous vehicles;
  • Integrated target tracking and guidance;
  • Dynamic model-based navigation;
  • Swarm localization;
  • Dynamic object tracking using SLAM (simultaneous localization and mapping);
  • Applied artificial intelligence in target tracking, guidance, and navigation.

Prof. Dr. Won-Sang Ra
Prof. Dr. Shaoming He
Dr. Ivan Masmitja
Topic Editors

Keywords

  • target tracking
  • target classification
  • heterogeneous sensor fusion
  • sensor arrangement
  • autonomous navigation
  • autonomous vehicle guidance
  • swarm localization
  • applied artificial intelligence

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Aerospace
aerospace
2.6 3.0 2014 24.5 Days CHF 2400 Submit
Applied Sciences
applsci
2.7 4.5 2011 15.8 Days CHF 2300 Submit
Automation
automation
- - 2020 21.8 Days CHF 1000 Submit
Drones
drones
4.8 6.1 2017 14.8 Days CHF 2600 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 21.1 Days CHF 2700 Submit
Sensors
sensors
3.9 6.8 2001 16.4 Days CHF 2600 Submit

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Published Papers (19 papers)

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25 pages, 5679 KiB  
Article
Memory-Enhanced Twin Delayed Deep Deterministic Policy Gradient (ME-TD3)-Based Unmanned Combat Aerial Vehicle Trajectory Planning for Avoiding Radar Detection Threats in Dynamic and Unknown Environments
Remote Sens. 2023, 15(23), 5494; https://doi.org/10.3390/rs15235494 - 25 Nov 2023
Viewed by 441
Abstract
Unmanned combat aerial vehicle (UCAV) trajectory planning to avoid radar detection threats is a complicated optimization problem that has been widely studied. The rapid changes in Radar Cross Sections (RCSs), the unknown cruise trajectory of airborne radar, and the uncertain distribution of radars [...] Read more.
Unmanned combat aerial vehicle (UCAV) trajectory planning to avoid radar detection threats is a complicated optimization problem that has been widely studied. The rapid changes in Radar Cross Sections (RCSs), the unknown cruise trajectory of airborne radar, and the uncertain distribution of radars exacerbate the complexity of this problem. In this paper, we propose a novel UCAV trajectory planning method based on deep reinforcement learning (DRL) technology to overcome the adverse impacts caused by the dynamics and randomness of environments. A predictive control model is constructed to describe the dynamic characteristics of the UCAV trajectory planning problem in detail. To improve the UCAV’s predictive ability, we propose a memory-enhanced twin delayed deep deterministic policy gradient (ME-TD3) algorithm that uses an attention mechanism to effectively extract environmental patterns from historical information. The simulation results show that the proposed method can successfully train UCAVs to carry out trajectory planning tasks in dynamic and unknown environments. Furthermore, the ME-TD3 algorithm outperforms other classical DRL algorithms in UCAV trajectory planning, exhibiting superior performance and adaptability. Full article
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18 pages, 8277 KiB  
Article
Trajectory-BERT: Trajectory Estimation Based on BERT Trajectory Pre-Training Model and Particle Filter Algorithm
Sensors 2023, 23(22), 9120; https://doi.org/10.3390/s23229120 - 11 Nov 2023
Viewed by 347
Abstract
In the realm of aviation, trajectory data play a crucial role in determining the target’s flight intentions and guaranteeing flight safety. However, the data collection process can be hindered by noise or signal interruptions, thus diminishing the precision of the data. This paper [...] Read more.
In the realm of aviation, trajectory data play a crucial role in determining the target’s flight intentions and guaranteeing flight safety. However, the data collection process can be hindered by noise or signal interruptions, thus diminishing the precision of the data. This paper uses the bidirectional encoder representations from transformers (BERT) model to solve the problem by masking the high-precision automatic dependent survey broadcast (ADS-B) trajectory data and estimating the mask position value based on the front and rear trajectory points during BERT model training. Through this process, the model acquires knowledge of intricate motion patterns within the trajectory data and acquires the BERT pre-training Model. Afterwards, a refined particle filter algorithm is utilized to generate alternative trajectory sets for observation trajectory data that is prone to noise. Ultimately, the BERT trajectory pre-training model is supplied with the alternative trajectory set, and the optimal trajectory is determined by computing the maximum posterior probability. The results of the experiment show that the model has good performance and is stronger than traditional algorithms. Full article
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21 pages, 3958 KiB  
Article
Three-Dimensional Path-Following Control Method for Flying–Walking Power Line Inspection Robot Based on Improved Line of Sight
Aerospace 2023, 10(11), 945; https://doi.org/10.3390/aerospace10110945 - 06 Nov 2023
Viewed by 600
Abstract
The Flying–Walking Power Line Inspection Robot (FPLIR) faces challenges in maintaining stability and reliability when operating in harsh transmission line environments with complex conditions. The robot often switches modes frequently to land accurately on the line, resulting in increasing following errors and premature [...] Read more.
The Flying–Walking Power Line Inspection Robot (FPLIR) faces challenges in maintaining stability and reliability when operating in harsh transmission line environments with complex conditions. The robot often switches modes frequently to land accurately on the line, resulting in increasing following errors and premature or delayed switching caused by reference path switching. To address these issues, a path-following control method based on improved line of sight (LOS) is proposed. The method features an adaptive acceptance circle strategy that adjusts the radius of the acceptance circle of the path point based on the angle of the path segment and the flight speed at the time of switching, improving path-following accuracy during reference trajectory switching. Also, an adaptive heading control with vertical distance feedback is designed to prioritize different path-following methods in real time based on variations in vertical distance, achieving rapid convergence along the following path. The state feedback following control law, based on the improved LOS, achieves the stable following of the reference path, which was validated by simulations. The simulation results show that the improved LOS reduces the convergence time by 0.8 s under controllable error conditions for path angles of θ ∈ (0, π⁄2). For path angles of θ ∈ (π⁄2, π), the following error is reduced by 0.3 m, and the convergence time is reduced by 0.4 s. These results validate the feasibility and effectiveness of the proposed method. This method demonstrates advantages over the traditional LOS in terms of following accuracy and convergence speed, providing theoretical references for future 3D path following for path-following robots and aerial vehicles. Full article
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26 pages, 4597 KiB  
Article
A Multi-Constraint Guidance and Maneuvering Penetration Strategy via Meta Deep Reinforcement Learning
Drones 2023, 7(10), 626; https://doi.org/10.3390/drones7100626 - 08 Oct 2023
Viewed by 740
Abstract
In response to the issue of UAV escape guidance, this study proposed a unified intelligent control strategy synthesizing optimal guidance and meta deep reinforcement learning (DRL). Optimal control with minor energy consumption was introduced to meet terminal latitude, longitude, and altitude. Maneuvering escape [...] Read more.
In response to the issue of UAV escape guidance, this study proposed a unified intelligent control strategy synthesizing optimal guidance and meta deep reinforcement learning (DRL). Optimal control with minor energy consumption was introduced to meet terminal latitude, longitude, and altitude. Maneuvering escape was realized by adding longitudinal and lateral maneuver overloads. The Maneuver command decision model is calculated based on soft-actor–critic (SAC) networks. Meta-learning was introduced to enhance the autonomous escape capability, which improves the performance of applications in time-varying scenarios not encountered in the training process. In order to obtain training samples at a faster speed, this study used the prediction method to solve reward values, avoiding a large number of numerical integrations. The simulation results demonstrated that the proposed intelligent strategy can achieve highly precise guidance and effective escape. Full article
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30 pages, 2913 KiB  
Article
Robust Collision-Free Guidance and Control for Underactuated Multirotor Aerial Vehicles
Drones 2023, 7(10), 611; https://doi.org/10.3390/drones7100611 - 27 Sep 2023
Viewed by 629
Abstract
This paper is concerned with the robust collision-free guidance and control of underactuated multirotor aerial vehicles in the presence of moving obstacles capable of accelerating, linear velocity and rotor thrust constraints, and matched model uncertainties and disturbances. We address this problem by using [...] Read more.
This paper is concerned with the robust collision-free guidance and control of underactuated multirotor aerial vehicles in the presence of moving obstacles capable of accelerating, linear velocity and rotor thrust constraints, and matched model uncertainties and disturbances. We address this problem by using a hierarchical flight control architecture composed of a supervisory outer-loop guidance module and an inner-loop stabilizing control one. The inner loop is designed using a typical hierarchical control scheme that nests the attitude control loop inside the position one. The effectiveness of this scheme relies on proper time-scale separation (TSS) between the closed-loop (faster) rotational and (slower) translational dynamics, which is not straightforward to enforce in practice. However, by combining an integral sliding mode attitude control law, which guarantees instantaneous tracking of the attitude commands, with a smooth and robust position control one, we enforce, by construction, the satisfaction of the TSS, thus avoiding the loss of robustness and use of a dull trial-and-error tweak of gains. On the other hand, the outer-loop guidance is built upon the continuous-control-obstacles method, which is incremented to respect the velocity and actuator constraints and avoid multiple moving obstacles that can accelerate. The overall method is evaluated using a numerical Monte Carlo simulation and is shown to be effective in providing satisfactory tracking performance, collision-free guidance, and the satisfaction of linear velocity and actuator constraints. Full article
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24 pages, 6662 KiB  
Article
An Online Generation Method of Terminal-Area Trajectories for Wave-Rider Using Deep Neural Networks
Aerospace 2023, 10(7), 654; https://doi.org/10.3390/aerospace10070654 - 23 Jul 2023
Viewed by 515
Abstract
This paper presents a deep neural network-based online trajectory generation method for the aerodynamic characteristic description and terminal-area energy management of wave-rider aircrafts. First, the flight dynamics equations in the energy domain are linearized and discretized to generate numerous aircraft trajectory samples with [...] Read more.
This paper presents a deep neural network-based online trajectory generation method for the aerodynamic characteristic description and terminal-area energy management of wave-rider aircrafts. First, the flight dynamics equations in the energy domain are linearized and discretized to generate numerous aircraft trajectory samples with sequential convex optimization (SCO) methods. Then, an optimization objective function is designed to promote the smoothness of the control variables and improve the trajectory similarity. Compared to the nonlinear programming (NLP), the proposed trajectory sample generation method is more suitable for the training of deep neural networks (DNNs). Finally, deep neural networks are formulated and trained for the control variables and state variables, using the generated obtained trajectory samples, so that the reference trajectories can be obtained online during the energy management process of the wave-rider’s terminal phase. Numerical simulations validate the high accuracy of the trajectories generated with the deep neural network. Meanwhile, this proposed method enables smaller storage usage, which is highly suitable for integration into on-board flight control systems. Full article
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16 pages, 1106 KiB  
Article
Trajectory Optimization for Multi-Sensor Multi-Target Search and Tracking with Bearing-Only Measurements
Aerospace 2023, 10(7), 652; https://doi.org/10.3390/aerospace10070652 - 20 Jul 2023
Viewed by 720
Abstract
This paper proposes a trajectory optimization approach for multi-sensor multi-target search and tracking using bearing-only sensors. Based on the framework of the joint integrated probabilistic data association (JIPDA) filter, the intensity of potential unknown targets is updated according to the trajectories of the [...] Read more.
This paper proposes a trajectory optimization approach for multi-sensor multi-target search and tracking using bearing-only sensors. Based on the framework of the joint integrated probabilistic data association (JIPDA) filter, the intensity of potential unknown targets is updated according to the trajectories of the UAVs. The performance indices for target search and tracking are constructed based on, respectively, the intensity of unknown targets in the search area and the tracking error covariance. A dimensionless criterion, evaluating the search and tracking performance, is formulated and leveraged as the objective function of the UAV trajectory optimization problem. Simulations were carried out in different search and tracking scenarios to demonstrate the effectiveness of the proposed approach. Full article
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19 pages, 1104 KiB  
Article
Optimal Geometry and Motion Coordination for Multisensor Target Tracking with Bearings-Only Measurements
Sensors 2023, 23(14), 6408; https://doi.org/10.3390/s23146408 - 14 Jul 2023
Viewed by 506
Abstract
This paper focuses on the optimal geometry and motion coordination problem of mobile bearings-only sensors for improving target tracking performance. A general optimal sensor–target geometry is derived with uniform sensor–target distance using D-optimality for arbitrary n (n2) bearings-only sensors. [...] Read more.
This paper focuses on the optimal geometry and motion coordination problem of mobile bearings-only sensors for improving target tracking performance. A general optimal sensor–target geometry is derived with uniform sensor–target distance using D-optimality for arbitrary n (n2) bearings-only sensors. The optimal geometry is characterized by the partition cases dividing n into the sum of integers no less than two. Then, a motion coordination method is developed to steer the sensors to reach the circular radius orbit (CRO) around the target with a minimum sensor–target distance and move with a circular formation. The sensors are first driven to approach the target directly when outside the CRO. When the sensor reaches the CRO, they are then allocated to different subsets according to the partition cases through matching the optimal geometry. The sensor motion is optimized under constraints to achieve the matched optimal geometry by minimizing the sum of the distance traveled by the sensors. Finally, two illustrative examples are used to demonstrate the effectiveness of the proposed approach. Full article
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17 pages, 3645 KiB  
Article
Tracking a Maneuvering Object by Indirect Observations with Random Delays
Drones 2023, 7(7), 468; https://doi.org/10.3390/drones7070468 - 13 Jul 2023
Viewed by 567
Abstract
A mathematical model for the target tracking problem is proposed. The model makes it possible to describe conditions when the time for an observer to receive the results of indirect observations of a moving object depends not only on the state of the [...] Read more.
A mathematical model for the target tracking problem is proposed. The model makes it possible to describe conditions when the time for an observer to receive the results of indirect observations of a moving object depends not only on the state of the observation environment but also on the state of the object itself. The source of such a model is the observation process, by stationary means, of an autonomous underwater vehicle, in which the time for obtaining up-to-date data depends on the unknown distance between the object and the observer. As part of the study of the problem, the equations of the optimal Bayesian filter are obtained. But this filter is not possible to implement. For practical purposes, it is proposed to use the conditionally minimax nonlinear filter, which has shown promising results in other complex tracking models. The conditions for the filter’s evaluation and its accuracy characteristics are given. A large-scale numerical experiment illustrating the filter’s operation and the observation system’s features with random delays are described. Full article
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22 pages, 27075 KiB  
Article
Deep Dyna-Q for Rapid Learning and Improved Formation Achievement in Cooperative Transportation
Automation 2023, 4(3), 210-231; https://doi.org/10.3390/automation4030013 - 10 Jul 2023
Viewed by 959
Abstract
Nowadays, academic research, disaster mitigation, industry, and transportation apply the cooperative multi-agent concept. A cooperative multi-agent system is a multi-agent system that works together to solve problems or maximise utility. The essential marks of formation control are how the multiple agents can reach [...] Read more.
Nowadays, academic research, disaster mitigation, industry, and transportation apply the cooperative multi-agent concept. A cooperative multi-agent system is a multi-agent system that works together to solve problems or maximise utility. The essential marks of formation control are how the multiple agents can reach the desired point while maintaining their position in the formation based on the dynamic conditions and environment. A cooperative multi-agent system closely relates to the formation change issue. It is necessary to change the arrangement of multiple agents according to the environmental conditions, such as when avoiding obstacles, applying different sizes and shapes of tracks, and moving different sizes and shapes of transport objects. Reinforcement learning is a good method to apply in a formation change environment. On the other hand, the complex formation control process requires a long learning time. This paper proposed using the Deep Dyna-Q algorithm to speed up the learning process while improving the formation achievement rate by tuning the parameters of the Deep Dyna-Q algorithm. Even though the Deep Dyna-Q algorithm has been used in many applications, it has not been applied in an actual experiment. The contribution of this paper is the application of the Deep Dyna-Q algorithm in formation control in both simulations and actual experiments. This study successfully implements the proposed method and investigates formation control in simulations and actual experiments. In the actual experiments, the Nexus robot with a robot operating system (ROS) was used. To confirm the communication between the PC and robots, camera processing, and motor controller, the velocities from the simulation were directly given to the robots. The simulations could give the same goal points as the actual experiments, so the simulation results approach the actual experimental results. The discount rate and learning rate values affected the formation change achievement rate, collision number among agents, and collisions between agents and transport objects. For learning rate comparison, DDQ (0.01) consistently outperformed DQN. DQN obtained the maximum −170 reward in about 130,000 episodes, while DDQ (0.01) could achieve this value in 58,000 episodes and achieved a maximum −160 reward. The application of an MEC (model error compensator) in the actual experiment successfully reduced the error movement of the robots so that the robots could produce the formation change appropriately. Full article
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22 pages, 5608 KiB  
Article
Omni-Directional Capture for Multi-Drone Based on 3D-Voronoi Tessellation
Drones 2023, 7(7), 458; https://doi.org/10.3390/drones7070458 - 10 Jul 2023
Viewed by 908
Abstract
This paper addresses the multi-drone formation capture in three-dimensional (3D) environments. The omni-directional minimum volume (ODMV) 3D-Voronoi diagram algorithm is proposed for the first time to achieve the two goals of (1) forming and keeping a capture and (2) planning the control action [...] Read more.
This paper addresses the multi-drone formation capture in three-dimensional (3D) environments. The omni-directional minimum volume (ODMV) 3D-Voronoi diagram algorithm is proposed for the first time to achieve the two goals of (1) forming and keeping a capture and (2) planning the control action within its safe, collision region for each drone. First, we extend the traditional 2D Voronoi diagram to the 3D environment and use the non-overlapping spatial division property of 3D Voronoi diagram to inherently avoid the collision between drones. Second, we make improvements to the problem of capture angle in our minimum area strategy and propose an omni-directional minimum volume strategy to accomplish the effective capture of a target by constraining the capture angle. Finally, the wolf pack algorithm (WPA) with variable step size is introduced to provide a movement strategy for multi-drone formations. Thus, the proposed ODMV can also achieve dynamic target and multi target capture in environments with obstacles. The Optitrack motion capture system and Crazyflie drones are used to conduct the multi-drone capture experiment. Both simulation and experimental results are included to demonstrated the effectiveness of the proposed ODMV method. Full article
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16 pages, 15413 KiB  
Article
EF-TTOA: Development of a UAV Path Planner and Obstacle Avoidance Control Framework for Static and Moving Obstacles
Drones 2023, 7(6), 359; https://doi.org/10.3390/drones7060359 - 29 May 2023
Cited by 1 | Viewed by 962
Abstract
With the increasing applications of unmanned aerial vehicles (UAVs) in surveying, mapping, rescue, etc., the security of autonomous flight in complex environments becomes a crucial issue. Deploying autonomous UAVs in complex environments typically requires them to have accurate dynamic obstacle perception, such as [...] Read more.
With the increasing applications of unmanned aerial vehicles (UAVs) in surveying, mapping, rescue, etc., the security of autonomous flight in complex environments becomes a crucial issue. Deploying autonomous UAVs in complex environments typically requires them to have accurate dynamic obstacle perception, such as the detection of birds and other flying vehicles at high altitudes, as well as humans and ground vehicles at low altitudes or indoors. This work’s primary goal is to cope with both static and moving obstacles in the environment by developing a new framework for UAV planning and control. Firstly, the point clouds acquired from the depth camera are divided into dynamic and static points, and then the velocity of the point cloud clusters is estimated. The static point cloud is used as the input for the local mapping. Path finding is simplified by identifying key points among static points. Secondly, the design of a trajectory tracking and obstacle avoidance controller based on the control barrier function guarantees security for moving and static obstacles. The path-finding module can stably search for the shortest path, and the controller can deal with moving obstacles with high-frequency. Therefore, the UAV can deal with both long-term planning and immediate emergencies. The framework proposed in this work enables a UAV to operate in a wider field, with better security and real-time performance. Full article
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21 pages, 9356 KiB  
Article
Integrated Path Tracking Controller of Underground Articulated Vehicle Based on Nonlinear Model Predictive Control
Appl. Sci. 2023, 13(9), 5340; https://doi.org/10.3390/app13095340 - 25 Apr 2023
Viewed by 1004
Abstract
This paper proposes an integrated path tracking controller for articulated vehicles. A nonlinear model-predictive control (NMPC)-based reference state tracker is designed as an upper-level controller to solve the vehicle’s longitudinal velocity and steering rate. A terminal cost is introduced into the NMPC to [...] Read more.
This paper proposes an integrated path tracking controller for articulated vehicles. A nonlinear model-predictive control (NMPC)-based reference state tracker is designed as an upper-level controller to solve the vehicle’s longitudinal velocity and steering rate. A terminal cost is introduced into the NMPC to improve the controller’s stability. A lower-level controller is developed to translate upper-level solutions into vehicle actuators’ signals, including steering and driving controllers. The steering controller translates the steering rate into the linear velocity of the cylinder to calculate the required fluid volume and ultimately into the rotation speed of the steering motor. The neural network method is applied in the driving controller to ensure accuracy under different loadings. In order to investigate the effects of the path tracking controller, an articulated dump truck is adapted for the field tests by adding the steering-by-wire system and driving-by-wire system, respectively. Experimental verifications of the lower-level controller are performed. The results show that the controller can accurately satisfy the demand. Finally, the tracking performance of the integrated path tracking controller is analyzed experimentally under different reference velocities. The results indicate that tracking accuracy can be guaranteed. Full article
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20 pages, 6841 KiB  
Article
Modified ADRC Design of Permanent Magnet Synchronous Motor Based on Improved Memetic Algorithm
Sensors 2023, 23(7), 3621; https://doi.org/10.3390/s23073621 - 30 Mar 2023
Cited by 3 | Viewed by 1279
Abstract
In this paper, a novel modified auto disturbance rejection control (ADRC) design of a permanent magnet synchronous motor based on the improved memetic algorithm (IMA) is proposed. Firstly, there is an obvious system ripple caused by the defect that the optimal control function [...] Read more.
In this paper, a novel modified auto disturbance rejection control (ADRC) design of a permanent magnet synchronous motor based on the improved memetic algorithm (IMA) is proposed. Firstly, there is an obvious system ripple caused by the defect that the optimal control function used in traditional ADRC cannot be differentiable and smooth at the segment point; aiming at weakening the system ripple effectively, the proposed method constructs a novel differentiable and smooth optimal control function to modify the ADRC design. Furthermore, aiming at improving the integration parameters optimization effect effectively, a novel improved memetic algorithm is proposed for obtaining the optimal parameters of ADRC. Specifically, an IMA with high-quality balance based on an adaptive nonlinear decreasing strategy for the convergence factor, Gaussian mutation mechanism, improved learning mechanism with the high-quality balance between competitive and opposition-based learning (OBL) and an elite set maintenance mechanism based on fusion distance is proposed so that these strategies can improve the optimization precision by a large margin. Finally, the experiment results of the PMSM speed control practical cases show that the ADRC based on IMA has an apparent better optimization effect than that of fuzzy PI, traditional ADRC based on the genetic algorithm and an improved ADRC based on improved moth–flame optimization. Full article
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20 pages, 5139 KiB  
Article
Lane Level Positioning Method for Unmanned Driving Based on Inertial System and Vector Map Information Fusion Applicable to GNSS Denied Environments
Drones 2023, 7(4), 239; https://doi.org/10.3390/drones7040239 - 29 Mar 2023
Cited by 1 | Viewed by 1644
Abstract
With the development of vehicle sensors, unmanned driving has become a research hotspot. Positioning is also considered to be one of the most challenging directions in this field. Aiming at the poor positioning accuracy of vehicles under GNSS denied environments, a lane-level positioning [...] Read more.
With the development of vehicle sensors, unmanned driving has become a research hotspot. Positioning is also considered to be one of the most challenging directions in this field. Aiming at the poor positioning accuracy of vehicles under GNSS denied environments, a lane-level positioning method based on inertial system and vector map information fusion is proposed. A dead reckoning model based on optical fiber IMU and odometer is established, and its positioning error is regarded as a priori information. Furthermore, a map matching model based on HMM is built up. Three validation experiments are carried out and experimental results show that the positioning error can be reduced to less than 30 cm when driving for about 7 min, which proves the effectiveness of the proposed method. Our work may provide a reference for the further improvement of positioning for unmanned driving under GNSS denied environments. Full article
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23 pages, 2872 KiB  
Article
Attitude-Orbit Coupled Control of Gravitational Wave Detection Spacecraft with Communication Delays
Sensors 2023, 23(6), 3233; https://doi.org/10.3390/s23063233 - 17 Mar 2023
Cited by 1 | Viewed by 1075
Abstract
In order to meet the position and attitude requirements of spacecrafts and test masses for gravitational-wave detection missions, the attitude-orbit coordination control of multiple spacecrafts and test masses is studied. A distributed coordination control law for spacecraft formation based on dual quaternion is [...] Read more.
In order to meet the position and attitude requirements of spacecrafts and test masses for gravitational-wave detection missions, the attitude-orbit coordination control of multiple spacecrafts and test masses is studied. A distributed coordination control law for spacecraft formation based on dual quaternion is proposed. By describing the relationship between spacecrafts and test masses in the desired states, the coordination control problem is converted into a consistent-tracking control problem in which each spacecraft or test mass tracks its desired states. An accurate attitude-orbit relative dynamics model of the spacecraft and the test masses is proposed based on dual quaternions. A cooperative feedback control law based on a consistency algorithm is designed to achieve the consistent attitude tracking of multiple rigid bodies (spacecraft and test mass) and maintain the specific formation configuration. Moreover, the communication delays of the system are taken into account. The distributed coordination control law ensures almost global asymptotic convergence of the relative position and attitude error in the presence of communication delays. The simulation results demonstrate the effectiveness of the proposed control method, which meets the formation-configuration requirements for gravitational-wave detection missions. Full article
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35 pages, 8107 KiB  
Article
Continuous Low-Thrust Maneuver Planning for Space Gravitational Wave Formation Reconfiguration Based on Improved Particle Swarm Optimization Algorithm
Sensors 2023, 23(6), 3154; https://doi.org/10.3390/s23063154 - 15 Mar 2023
Cited by 1 | Viewed by 1288
Abstract
This study proposes a three-spacecraft formation reconfiguration strategy of minimum fuel for space gravitational wave detection missions in the high Earth orbit (105 km). For solving the limitations of measurement and communication in long baseline formations, a control strategy of a virtual [...] Read more.
This study proposes a three-spacecraft formation reconfiguration strategy of minimum fuel for space gravitational wave detection missions in the high Earth orbit (105 km). For solving the limitations of measurement and communication in long baseline formations, a control strategy of a virtual formation is applied. The virtual reference spacecraft provides a desired relative state between the satellites, which is then used to control the motion of the physical spacecraft to maintain the desired formation. A linear dynamics model based on relative orbit elements’ parameterization is used to describe the relative motion in the virtual formation, which facilitates the inclusion of J2, SRP, and lunisolar third-body gravity effects and provides a direct insight into the relative motion geometry. Considering the actual flight scenarios of gravitational wave formations, a formation reconfiguration strategy based on continuous low thrust is investigated to achieve the desired state at a given time while minimizing interference to the satellite platform. The reconfiguration problem is considered a constrained nonlinear programming problem, and an improved particle swarm algorithm is developed to solve this problem. Finally, the simulation results demonstrate the performance of the proposed method in improving the maneuver sequence distribution and optimizing maneuver consumption. Full article
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18 pages, 7627 KiB  
Article
Development of a Real-Time 6-DOF Motion-Tracking System for Robotic Computer-Assisted Implant Surgery
Sensors 2023, 23(5), 2450; https://doi.org/10.3390/s23052450 - 22 Feb 2023
Cited by 2 | Viewed by 1934
Abstract
In this paper, we investigate a motion-tracking system for robotic computer-assisted implant surgery. Failure of the accurate implant positioning may result in significant problems, thus an accurate real-time motion-tracking system is crucial for avoiding these issues in computer-assisted implant surgery. Essential features of [...] Read more.
In this paper, we investigate a motion-tracking system for robotic computer-assisted implant surgery. Failure of the accurate implant positioning may result in significant problems, thus an accurate real-time motion-tracking system is crucial for avoiding these issues in computer-assisted implant surgery. Essential features of the motion-tracking system are analyzed and classified into four categories: workspace, sampling rate, accuracy, and back-drivability. Based on this analysis, requirements for each category have been derived to ensure that the motion-tracking system meets the desired performance criteria. A novel 6-DOF motion-tracking system is proposed which demonstrates high accuracy and back-drivability, making it suitable for use in computer-assisted implant surgery. The results of the experiments confirm the effectiveness of the proposed system in achieving the essential features required for a motion-tracking system in robotic computer-assisted implant surgery. Full article
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24 pages, 16828 KiB  
Article
Precision Landing Tests of Tethered Multicopter and VTOL UAV on Moving Landing Pad on a Lake
Sensors 2023, 23(4), 2016; https://doi.org/10.3390/s23042016 - 10 Feb 2023
Cited by 3 | Viewed by 1889
Abstract
Autonomous take-off and landing on a moving landing pad are extraordinarily complex and challenging functionalities of modern UAVs, especially if they must be performed in windy environments. The article presents research focused on achieving such functionalities for two kinds of UAVs, i.e., a [...] Read more.
Autonomous take-off and landing on a moving landing pad are extraordinarily complex and challenging functionalities of modern UAVs, especially if they must be performed in windy environments. The article presents research focused on achieving such functionalities for two kinds of UAVs, i.e., a tethered multicopter and VTOL. Both vehicles are supported by a landing pad navigation station, which communicates with their ROS-based onboard computer. The computer integrates navigational data from the UAV and the landing pad navigational station through the utilization of an extended Kalman filter, which is a typical approach in such applications. The novelty of the presented system is extending navigational data with data from the ultra wide band (UWB) system, and this makes it possible to achieve a landing accuracy of about 1 m. In the research, landing tests were carried out in real conditions on a lake for both UAVs. In the tests, a special mobile landing pad was built and based on a barge. The results show that the expected accuracy of 1 m is indeed achieved, and both UAVs are ready to be tested in real conditions on a ferry. Full article
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