Pub Date : 2019-06-01DOI: 10.1109/ICUAS.2019.8798103
P. Flores-Palmeros, P. Castillo, F. Castaños
A Backstepping controller based on SE(3) for achieving multi-agents consensus and flight formation of a drones fleet is developed in this paper. The controller is obtained using the nonlinear model of the quadrotor and derived with virtual inputs to converge the fleet to desired references. The stability analysis of the controller is analyzed and proved with the Lyapunov theory. Emulations of the control algorithm are carried out for validating the well performance of the closed-loop system.
{"title":"Backstepping-Based Controller for Flight Formation","authors":"P. Flores-Palmeros, P. Castillo, F. Castaños","doi":"10.1109/ICUAS.2019.8798103","DOIUrl":"https://doi.org/10.1109/ICUAS.2019.8798103","url":null,"abstract":"A Backstepping controller based on SE(3) for achieving multi-agents consensus and flight formation of a drones fleet is developed in this paper. The controller is obtained using the nonlinear model of the quadrotor and derived with virtual inputs to converge the fleet to desired references. The stability analysis of the controller is analyzed and proved with the Lyapunov theory. Emulations of the control algorithm are carried out for validating the well performance of the closed-loop system.","PeriodicalId":426616,"journal":{"name":"2019 International Conference on Unmanned Aircraft Systems (ICUAS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133771390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-01DOI: 10.1109/ICUAS.2019.8797915
L. Caraballo, J. Díaz-Báñez, R. Fabila-Monroy, C. Hidalgo-Toscano
A group of UAVs can be used to efficiently patrol a terrain, in which each robot flies around an assigned area and shares information with the neighbors periodically in order to protect or supervise it. To ensure robustness, previous works propose sending a robot to the neighboring area in case it detects a failure. In order to add unpredictability and to improve on the efficiency in the deterministic patrolling scheme, this paper presents random strategies to cover the areas distributed among the agents. We evaluate these strategies using three metrics: the idle-time, the isolation-time and the broadcast-time. The idle-time is the expected time between two consecutive observations of any point of the terrain. The isolation-time is the expected time that a robot is isolated (that is, without communication with any other robot). The broadcast-time is the expected time elapsed from the moment a robot emits a message until it is received by all the other robots of the team. Simulations show that the random strategies outperform the results obtained with the deterministic protocol.
{"title":"Patrolling a terrain with cooperrative UAVs using Random Walks","authors":"L. Caraballo, J. Díaz-Báñez, R. Fabila-Monroy, C. Hidalgo-Toscano","doi":"10.1109/ICUAS.2019.8797915","DOIUrl":"https://doi.org/10.1109/ICUAS.2019.8797915","url":null,"abstract":"A group of UAVs can be used to efficiently patrol a terrain, in which each robot flies around an assigned area and shares information with the neighbors periodically in order to protect or supervise it. To ensure robustness, previous works propose sending a robot to the neighboring area in case it detects a failure. In order to add unpredictability and to improve on the efficiency in the deterministic patrolling scheme, this paper presents random strategies to cover the areas distributed among the agents. We evaluate these strategies using three metrics: the idle-time, the isolation-time and the broadcast-time. The idle-time is the expected time between two consecutive observations of any point of the terrain. The isolation-time is the expected time that a robot is isolated (that is, without communication with any other robot). The broadcast-time is the expected time elapsed from the moment a robot emits a message until it is received by all the other robots of the team. Simulations show that the random strategies outperform the results obtained with the deterministic protocol.","PeriodicalId":426616,"journal":{"name":"2019 International Conference on Unmanned Aircraft Systems (ICUAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115728046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-01DOI: 10.1109/ICUAS.2019.8797844
Catrina Lim, Boyang Li, Ee Meng Ng, Xin Liu, K. Low
In this paper, a 3D dynamic obstacle perception is developed in a detect-and-avoid (DAA) framework for unmanned aerial vehicles (UAVs) or drones. The framework requires only an end point coordinate for collision-free path-planning and execution in an environment with dynamic obstacles. The sense portion of the DAA framework takes data from an mmWave sensor and a depth camera while the detect portion of the framework updates a probabilistic octree when static and dynamic obstacles are sensed. Perception of dynamic obstacle was achieved by implementing an algorithm that clears the sensor’s field of vision before computing the occupied voxels and populating the probabilistic octree. The avoidance portion of the framework is based on rapidly-exploring random tree (RRT) but the framework is flexible to allow other types of planners. This work develops the DAA framework for a UAV in a dynamic 3D environment by modifying the MoveIt framework. The framework is implemented on a UAV platform equipped with an on-board computational unit. The simulation and indoor experiments were conducted, which show that the modified DAA framework with dynamic 3D obstacle perception can successfully sense, detect and avoid obstacle. Additionally, the proposed perception method reduced the path re-plan time.
{"title":"Three-dimensional (3D) Dynamic Obstacle Perception in a Detect-and-Avoid Framework for Unmanned Aerial Vehicles","authors":"Catrina Lim, Boyang Li, Ee Meng Ng, Xin Liu, K. Low","doi":"10.1109/ICUAS.2019.8797844","DOIUrl":"https://doi.org/10.1109/ICUAS.2019.8797844","url":null,"abstract":"In this paper, a 3D dynamic obstacle perception is developed in a detect-and-avoid (DAA) framework for unmanned aerial vehicles (UAVs) or drones. The framework requires only an end point coordinate for collision-free path-planning and execution in an environment with dynamic obstacles. The sense portion of the DAA framework takes data from an mmWave sensor and a depth camera while the detect portion of the framework updates a probabilistic octree when static and dynamic obstacles are sensed. Perception of dynamic obstacle was achieved by implementing an algorithm that clears the sensor’s field of vision before computing the occupied voxels and populating the probabilistic octree. The avoidance portion of the framework is based on rapidly-exploring random tree (RRT) but the framework is flexible to allow other types of planners. This work develops the DAA framework for a UAV in a dynamic 3D environment by modifying the MoveIt framework. The framework is implemented on a UAV platform equipped with an on-board computational unit. The simulation and indoor experiments were conducted, which show that the modified DAA framework with dynamic 3D obstacle perception can successfully sense, detect and avoid obstacle. Additionally, the proposed perception method reduced the path re-plan time.","PeriodicalId":426616,"journal":{"name":"2019 International Conference on Unmanned Aircraft Systems (ICUAS)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117273023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The demand for mission critical tasks, especially for tracking on the UAVs, has been increasing due to their superior mobility. Out of necessity, the ability of processing large images emerges for object detection or tracking with UAVs. As such, the requirements of low latency and lack of Internet access under some circumstances become the major challenges. In this paper, we present a modeling method of CNN that is dedicated to single object detection on the UAV without any transfer learning model. Not limited to the features learned by the transfer learning model, the single object can be selected arbitrarily and specifically, even can be distinguished from those other objects in the same category. Our modeling method introduces the inducing neural network that follows the traditional CNN and plays the role of guiding the training in a fast and efficient way with respect to the training convergence and the model capacity. Using the dataset released by DAC 2018, which contains 98 classes and 96,408 images taken by UAVs, we present how our modeling method develops the inducing neural network that integrates multi-task learning drawn from the state-of-the-art works to achieve about 50% of IoU (Intersection over Union of the ground-truth bounding boxes and predicted bounding boxes) and 20 FPS running on NVIDIA Jetson TX2. Experimental results demonstrated fast inference of an image in size of 720x1280 and the UAV navigated itself to track the target (car) using the inference result.
{"title":"Real-Time Single Object Detection on The UAV","authors":"Hsiang-Huang Wu, Zejian Zhou, Ming Feng, Yuzhong Yan, Hao Xu, Lijun Qian","doi":"10.1109/ICUAS.2019.8797866","DOIUrl":"https://doi.org/10.1109/ICUAS.2019.8797866","url":null,"abstract":"The demand for mission critical tasks, especially for tracking on the UAVs, has been increasing due to their superior mobility. Out of necessity, the ability of processing large images emerges for object detection or tracking with UAVs. As such, the requirements of low latency and lack of Internet access under some circumstances become the major challenges. In this paper, we present a modeling method of CNN that is dedicated to single object detection on the UAV without any transfer learning model. Not limited to the features learned by the transfer learning model, the single object can be selected arbitrarily and specifically, even can be distinguished from those other objects in the same category. Our modeling method introduces the inducing neural network that follows the traditional CNN and plays the role of guiding the training in a fast and efficient way with respect to the training convergence and the model capacity. Using the dataset released by DAC 2018, which contains 98 classes and 96,408 images taken by UAVs, we present how our modeling method develops the inducing neural network that integrates multi-task learning drawn from the state-of-the-art works to achieve about 50% of IoU (Intersection over Union of the ground-truth bounding boxes and predicted bounding boxes) and 20 FPS running on NVIDIA Jetson TX2. Experimental results demonstrated fast inference of an image in size of 720x1280 and the UAV navigated itself to track the target (car) using the inference result.","PeriodicalId":426616,"journal":{"name":"2019 International Conference on Unmanned Aircraft Systems (ICUAS)","volume":"428 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115999547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-01DOI: 10.1109/ICUAS.2019.8797746
M. Sollie, T. Bryne, T. Johansen
Increasing use of UAVs in high-precision applications, such as georeferencing and photogrammetry, increases the requirements on the accuracy of the estimated position, velocity and attitude of the vehicle. Commercial systems that utilize magnetometers in the heading estimates are cheap, but are affected by disturbances from both the vehicle itself, nearby metal structures and variations in the Earth’s magnetic field. On the other side, commercial dual-antenna satellite navigation systems can provide the required accuracy, but are expensive. This paper explores the use of a low-cost setup using two independent GNSS receivers, aiding an inertial navigation system by using pseudorange, Doppler frequency and carrier phase measurements from two longitudinally separated receivers on a fixed-wing UAV. The sensor integration was based on a multiplicative extended Kalman filter (MEKF). The main contribution of this paper is the derivation of measurement models for the raw GNSS measurements based on the MEKF error state, taking into account antenna lever arms and explicitly including the difference in measurement time between the receivers in the measurement model for double differenced carrier phase. The proposed method is verified using data collected from a UAV flight.
{"title":"Pose Estimation of UAVs Based on INS Aided by Two Independent Low-Cost GNSS Receivers","authors":"M. Sollie, T. Bryne, T. Johansen","doi":"10.1109/ICUAS.2019.8797746","DOIUrl":"https://doi.org/10.1109/ICUAS.2019.8797746","url":null,"abstract":"Increasing use of UAVs in high-precision applications, such as georeferencing and photogrammetry, increases the requirements on the accuracy of the estimated position, velocity and attitude of the vehicle. Commercial systems that utilize magnetometers in the heading estimates are cheap, but are affected by disturbances from both the vehicle itself, nearby metal structures and variations in the Earth’s magnetic field. On the other side, commercial dual-antenna satellite navigation systems can provide the required accuracy, but are expensive. This paper explores the use of a low-cost setup using two independent GNSS receivers, aiding an inertial navigation system by using pseudorange, Doppler frequency and carrier phase measurements from two longitudinally separated receivers on a fixed-wing UAV. The sensor integration was based on a multiplicative extended Kalman filter (MEKF). The main contribution of this paper is the derivation of measurement models for the raw GNSS measurements based on the MEKF error state, taking into account antenna lever arms and explicitly including the difference in measurement time between the receivers in the measurement model for double differenced carrier phase. The proposed method is verified using data collected from a UAV flight.","PeriodicalId":426616,"journal":{"name":"2019 International Conference on Unmanned Aircraft Systems (ICUAS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124780819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-01DOI: 10.1109/ICUAS.2019.8797779
Woo-Cheol Lee, Han-Lim Choi
This paper presents a fault detection and identification (FDI) method that can simultaneously deal with motor and sensor faults in a quadcopter. The method integrates Neural Adaptive Observers (NAOs) that predicts the errors in the dynamic model due to fault into an Interactive Multiple Model (IMM) framework. Two NAOs are constructed to deal with two different categories of faults – sensor faults and actuator faults, which are represented as two different models in the IMM filter. The stability of the proposed FDI scheme is theoretically analyzed, and validity of the method is demonstrated on a virtual physics engine environment.
{"title":"Interactive Multiple Neural Adaptive Observer based Sensor and Actuator Fault Detection and Isolation for Quadcopter","authors":"Woo-Cheol Lee, Han-Lim Choi","doi":"10.1109/ICUAS.2019.8797779","DOIUrl":"https://doi.org/10.1109/ICUAS.2019.8797779","url":null,"abstract":"This paper presents a fault detection and identification (FDI) method that can simultaneously deal with motor and sensor faults in a quadcopter. The method integrates Neural Adaptive Observers (NAOs) that predicts the errors in the dynamic model due to fault into an Interactive Multiple Model (IMM) framework. Two NAOs are constructed to deal with two different categories of faults – sensor faults and actuator faults, which are represented as two different models in the IMM filter. The stability of the proposed FDI scheme is theoretically analyzed, and validity of the method is demonstrated on a virtual physics engine environment.","PeriodicalId":426616,"journal":{"name":"2019 International Conference on Unmanned Aircraft Systems (ICUAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129882334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-01DOI: 10.1109/ICUAS.2019.8798071
M. Terzi, P. Kolios, C. Panayiotou, T. Theocharides
In this paper a unified framework is presented for coordinated multi-drone tasking in emergency response missions. As elaborated in this work, response missions can be broken into a number of distinct tasks that can be allocated among the available drone agents to expedite the response operations. The proposed framework enables the development and execution of algorithms that jointly schedule and route drone agents across the field to complete their tasks and successfully address the mission goals considering the agent limitations. The key design challenges of implementing the proposed framework are discussed. Finally, initial simulation and experimental results are presented providing evidence of the real life applicability and reliability of the proposed framework.
{"title":"A Unified Framework for Reliable Multi-Drone Tasking in Emergency Response Missions","authors":"M. Terzi, P. Kolios, C. Panayiotou, T. Theocharides","doi":"10.1109/ICUAS.2019.8798071","DOIUrl":"https://doi.org/10.1109/ICUAS.2019.8798071","url":null,"abstract":"In this paper a unified framework is presented for coordinated multi-drone tasking in emergency response missions. As elaborated in this work, response missions can be broken into a number of distinct tasks that can be allocated among the available drone agents to expedite the response operations. The proposed framework enables the development and execution of algorithms that jointly schedule and route drone agents across the field to complete their tasks and successfully address the mission goals considering the agent limitations. The key design challenges of implementing the proposed framework are discussed. Finally, initial simulation and experimental results are presented providing evidence of the real life applicability and reliability of the proposed framework.","PeriodicalId":426616,"journal":{"name":"2019 International Conference on Unmanned Aircraft Systems (ICUAS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128387360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-01DOI: 10.1109/ICUAS.2019.8798072
Edward Schwalb, J. Schwalb
Reliability is one key challenge facing safe widespread integration of UASs within uncontrolled airspaces. We apply system engineering methods to airspace management to explore architectures and operational concepts which can compensate for partial failures and bridge the reliability gap. Our exploration leads to an operational approach which leverages multiple UASs to achieve redundancies. We employ simulation to demonstrate the benefits of operating single-file platoons over fixed routes, in the context of the NASA UAS Traffic Management (UTM). We show that it is possible to simultaneously reduce impact of localization errors, achieve better resilience under degraded communications, gracefully remove from airspace UAS compromised by cyber attacks, improve conflict management and increase airspace capacity. Our multi-agent airspace simulation improves realism using Firmware In the Loop (FITL), Intent Control Loop (ICL) and of execution contingencies as concurrent high priority activities.UTM, Systems Engineering, Contingencies, Platoons, Denied GPS, Degraded Communications, Cyber Attacks, Simulation, Firmware Modeling, Intent Modeling
{"title":"Improving Redundancy and Safety of UTM by Leveraging Multiple Uass","authors":"Edward Schwalb, J. Schwalb","doi":"10.1109/ICUAS.2019.8798072","DOIUrl":"https://doi.org/10.1109/ICUAS.2019.8798072","url":null,"abstract":"Reliability is one key challenge facing safe widespread integration of UASs within uncontrolled airspaces. We apply system engineering methods to airspace management to explore architectures and operational concepts which can compensate for partial failures and bridge the reliability gap. Our exploration leads to an operational approach which leverages multiple UASs to achieve redundancies. We employ simulation to demonstrate the benefits of operating single-file platoons over fixed routes, in the context of the NASA UAS Traffic Management (UTM). We show that it is possible to simultaneously reduce impact of localization errors, achieve better resilience under degraded communications, gracefully remove from airspace UAS compromised by cyber attacks, improve conflict management and increase airspace capacity. Our multi-agent airspace simulation improves realism using Firmware In the Loop (FITL), Intent Control Loop (ICL) and of execution contingencies as concurrent high priority activities.UTM, Systems Engineering, Contingencies, Platoons, Denied GPS, Degraded Communications, Cyber Attacks, Simulation, Firmware Modeling, Intent Modeling","PeriodicalId":426616,"journal":{"name":"2019 International Conference on Unmanned Aircraft Systems (ICUAS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128669001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-01DOI: 10.1109/ICUAS.2019.8797942
Michail G. Michailidis, Mohammed Agha, M. Rutherford, K. Valavanis
The paper presents a software in the loop (SIL) sensor study in simulation environments for traditional Kalman, linear and nonlinear complementary filters, which are derived, tested and implemented on a fixed wing UAV for attitude estimation (pitch, roll and heading angle). An overview of the SIL setup environment between MATLAB/Simulink and the X-Plane flight simulator is given. Kalman filter design in Simulink utilizes a state-space model of the UAV dynamics, while complementary filter combines accelerometer output for low frequency attitude estimation with integrated gyro output for high frequency estimation. Simulation results are provided and discussed under both Gaussian and uniform noise, highlighting the convergence of the designed estimators. It is also shown that the estimator following the nonlinear complementary framework yields a better match to the dynamic evolution of the actual attitude angles of the vehicle over time.
{"title":"A Software in the Loop (SIL) Kalman and Complementary Filter Implementation on X-Plane for UAVs","authors":"Michail G. Michailidis, Mohammed Agha, M. Rutherford, K. Valavanis","doi":"10.1109/ICUAS.2019.8797942","DOIUrl":"https://doi.org/10.1109/ICUAS.2019.8797942","url":null,"abstract":"The paper presents a software in the loop (SIL) sensor study in simulation environments for traditional Kalman, linear and nonlinear complementary filters, which are derived, tested and implemented on a fixed wing UAV for attitude estimation (pitch, roll and heading angle). An overview of the SIL setup environment between MATLAB/Simulink and the X-Plane flight simulator is given. Kalman filter design in Simulink utilizes a state-space model of the UAV dynamics, while complementary filter combines accelerometer output for low frequency attitude estimation with integrated gyro output for high frequency estimation. Simulation results are provided and discussed under both Gaussian and uniform noise, highlighting the convergence of the designed estimators. It is also shown that the estimator following the nonlinear complementary framework yields a better match to the dynamic evolution of the actual attitude angles of the vehicle over time.","PeriodicalId":426616,"journal":{"name":"2019 International Conference on Unmanned Aircraft Systems (ICUAS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129789178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-06-01DOI: 10.1109/ICUAS.2019.8797888
Amith Manoharan, Rajnikant Sharma, P. Sujit
This paper proposes a nonlinear model predictive control (NMPC) scheme to tackle the problem of localization and path planning of a group of unmanned aerial vehicles (UAVs) in global positioning system (GPS) denied environments. It is assumed that the UAVs can cooperate by sharing information among themselves. It is also assumed that the area under consideration contains some landmarks with known locations. The NMPC computes the optimal control inputs for the vehicles such that the vehicles cooperate to transit from a source location to a destination while choosing a path that will cover enough landmarks for localization. An Extended Kalman Filter (EKF) is used to estimate the vehicle positions using only relative bearing measurements. The efficacy of the proposed method was evaluated through numerical simulations, and the results are discussed.
{"title":"Nonlinear Model Predictive Control to Aid Cooperative Localization","authors":"Amith Manoharan, Rajnikant Sharma, P. Sujit","doi":"10.1109/ICUAS.2019.8797888","DOIUrl":"https://doi.org/10.1109/ICUAS.2019.8797888","url":null,"abstract":"This paper proposes a nonlinear model predictive control (NMPC) scheme to tackle the problem of localization and path planning of a group of unmanned aerial vehicles (UAVs) in global positioning system (GPS) denied environments. It is assumed that the UAVs can cooperate by sharing information among themselves. It is also assumed that the area under consideration contains some landmarks with known locations. The NMPC computes the optimal control inputs for the vehicles such that the vehicles cooperate to transit from a source location to a destination while choosing a path that will cover enough landmarks for localization. An Extended Kalman Filter (EKF) is used to estimate the vehicle positions using only relative bearing measurements. The efficacy of the proposed method was evaluated through numerical simulations, and the results are discussed.","PeriodicalId":426616,"journal":{"name":"2019 International Conference on Unmanned Aircraft Systems (ICUAS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130147571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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