The Experts below are selected from a list of 42204 Experts worldwide ranked by ideXlab platform
Xiang Chen - One of the best experts on this subject based on the ideXlab platform.
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decision making for the Autonomous Navigation of maritime Autonomous surface ships based on scene division and deep reinforcement learning
Sensors, 2019Co-Authors: Xinyu Zhang, Chengbo Wang, Yuanchang Liu, Xiang ChenAbstract:This research focuses on the adaptive Navigation of maritime Autonomous surface ships (MASSs) in an uncertain environment. To achieve intelligent obstacle avoidance of MASSs in a port, an Autonomous Navigation decision-making model based on hierarchical deep reinforcement learning is proposed. The model is mainly composed of two layers: the scene division layer and an Autonomous Navigation decision-making layer. The scene division layer mainly quantifies the sub-scenarios according to the International Regulations for Preventing Collisions at Sea (COLREG). This research divides the Navigational situation of a ship into entities and attributes based on the ontology model and Protege language. In the decision-making layer, we designed a deep Q-learning algorithm utilizing the environmental model, ship motion space, reward function, and search strategy to learn the environmental state in a quantized sub-scenario to train the Navigation strategy. Finally, two sets of verification experiments of the deep reinforcement learning (DRL) and improved DRL algorithms were designed with Rizhao port as a study case. Moreover, the experimental data were analyzed in terms of the convergence trend, iterative path, and collision avoidance effect. The results indicate that the improved DRL algorithm could effectively improve the Navigation safety and collision avoidance.
Ben M. Chen - One of the best experts on this subject based on the ideXlab platform.
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Autonomous Navigation of UAV in Foliage Environment
Journal of Intelligent and Robotic Systems: Theory and Applications, 2016Co-Authors: Jin Q. Cui, Shupeng Lai, Xiangxu Dong, Ben M. ChenAbstract:This paper presents a Navigation system that enables small-scale unmanned aerial vehicles to navigate Autonomously using a 2D laser range finder in foliage environment without GPS. The Navigation framework consists of real-time dual layer control, Navigation state estimation and online path planning. In particular, the inner loop of a quadrotor is stabilized using a commercial autopilot while the outer loop control is implemented using robust perfect tracking. The Navigation state estimation consists of real-time onboard motion estimation and trajectory smoothing using the GraphSLAM technique. The onboard real-time motion estimation is achieved by a Kalman filter, fusing the planar velocity measurement from match-ing the consecutive scans of a laser range finder and the acceleration measurement of an inertial measure-ment unit. The trajectory histories from the real-time Autonomous Navigation together with the observed features are fed into a sliding-window based pose-graph optimization framework. The online path plan-ning module finds an obstacle-free trajectory based the local measurement of the laser range finder. The performance of the proposed Navigation system is demonstrated successfully on the Autonomous naviga-tion of a small-scale UAV in foliage environment.
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Monocular vision-based Autonomous Navigation system on a toy quadcopter in unknown environments
2015 International Conference on Unmanned Aircraft Systems (ICUAS), 2015Co-Authors: Rui Huang, Ping Tan, Ben M. ChenAbstract:In this paper, we present an monocular vision-based Autonomous Navigation system for a commercial quadcoptor. The quadcoptor communicates with a ground-based laptop via wireless connection. The video stream of the front camera on the drone and the Navigation data measured on-board are sent to the ground station and then processed by a vision-based SLAM system. In order to handle motion blur and frame lost in the received video, our SLAM system consists of a improved robust feature tracking scheme and a relocalisation module which achieves fast recovery from tracking failure. An Extended Kalman filter (EKF) is designed for sensor fusion. Thanks to the proposed EKF, accurate 3D positions and velocities can be estimated as well as the scaling factor of the monocular SLAM. Using a motion capture system with millimeter-level precision, we also identify the system models of the quadcoptor and design the PID controller accordingly. We demonstrate that the quadcoptor can navigate along pre-defined paths in an unknown indoor environment with our system using its front camera and onboard sensors only after some simple manual initialization procedures.
Xinyu Zhang - One of the best experts on this subject based on the ideXlab platform.
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decision making for the Autonomous Navigation of maritime Autonomous surface ships based on scene division and deep reinforcement learning
Sensors, 2019Co-Authors: Xinyu Zhang, Chengbo Wang, Yuanchang Liu, Xiang ChenAbstract:This research focuses on the adaptive Navigation of maritime Autonomous surface ships (MASSs) in an uncertain environment. To achieve intelligent obstacle avoidance of MASSs in a port, an Autonomous Navigation decision-making model based on hierarchical deep reinforcement learning is proposed. The model is mainly composed of two layers: the scene division layer and an Autonomous Navigation decision-making layer. The scene division layer mainly quantifies the sub-scenarios according to the International Regulations for Preventing Collisions at Sea (COLREG). This research divides the Navigational situation of a ship into entities and attributes based on the ontology model and Protege language. In the decision-making layer, we designed a deep Q-learning algorithm utilizing the environmental model, ship motion space, reward function, and search strategy to learn the environmental state in a quantized sub-scenario to train the Navigation strategy. Finally, two sets of verification experiments of the deep reinforcement learning (DRL) and improved DRL algorithms were designed with Rizhao port as a study case. Moreover, the experimental data were analyzed in terms of the convergence trend, iterative path, and collision avoidance effect. The results indicate that the improved DRL algorithm could effectively improve the Navigation safety and collision avoidance.
Nicholas Roy - One of the best experts on this subject based on the ideXlab platform.
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robust object based slam for high speed Autonomous Navigation
International Conference on Robotics and Automation, 2019Co-Authors: Katherine E Liu, Kris Frey, Jonathan P How, Nicholas RoyAbstract:We present Robust Object-based SLAM for High-speed Autonomous Navigation (ROSHAN), a novel approach to object-level mapping suitable for Autonomous Navigation. In ROSHAN, we represent objects as ellipsoids and infer their parameters using three sources of information – bounding box detections, image texture, and semantic knowledge – to overcome the observability problem in ellipsoid-based SLAM under common forward-translating vehicle motions. Each bounding box provides four planar constraints on an object surface and we add a fifth planar constraint using the texture on the objects along with a semantic prior on the shape of ellipsoids. We demonstrate ROSHAN in simulation where we outperform the baseline, reducing the median shape error by 83% and the median position error by 72% in a forward-moving camera sequence. We demonstrate similar qualitative result on data collected on a fast-moving Autonomous quadrotor.
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range robust Autonomous Navigation in gps denied environments
Journal of Field Robotics, 2011Co-Authors: Abraham Bachrach, Samuel Prentice, Nicholas RoyAbstract:This paper addresses the problem of Autonomous Navigation of a micro air vehicle (MAV) in GPS-denied environments. We present experimental validation and analysis for our system that enables a quadrotor helicopter, equipped with a laser range finder sensor, to Autonomously explore and map unstructured and unknown environments. The key challenge for enabling GPS-denied flight of a MAV is that the system must be able to estimate its position and velocity by sensing unknown environmental structure with sufficient accuracy and low enough latency to stably control the vehicle. Our solution overcomes this challenge in the face of MAV payload limitations imposed on sensing, computational, and communication resources. We first analyze the requirements to achieve fully Autonomous quadrotor helicopter flight in GPS-denied areas, highlighting the differences between ground and air robots that make it difficult to use algorithms developed for ground robots. We report on experiments that validate our solutions to key challenges, namely a multilevel sensing and control hierarchy that incorporates a high-speed laser scan-matching algorithm, data fusion filter, high-level simultaneous localization and mapping, and a goal-directed exploration module. These experiments illustrate the quadrotor helicopter's ability to accurately and Autonomously navigate in a number of large-scale unknown environments, both indoors and in the urban canyon. The system was further validated in the field by our winning entry in the 2009 International Aerial Robotics Competition, which required the quadrotor to Autonomously enter a hazardous unknown environment through a window, explore the indoor structure without GPS, and search for a visual target. © 2011 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.
Murray, Richard M. - One of the best experts on this subject based on the ideXlab platform.
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Learning Pose Estimation for UAV Autonomous Navigation andLanding Using Visual-Inertial Sensor Data
2020Co-Authors: Baldini Francesca, Anandkumar Animashree, Murray, Richard M.Abstract:In this work, we propose a new learning approach for Autonomous Navigation and landing of an Unmanned-Aerial-Vehicle (UAV). We develop a multimodal fusion of deep neural architectures for visual-inertial odometry. We train the model in an end-to-end fashion to estimate the current vehicle pose from streams of visual and inertial measurements. We first evaluate the accuracy of our estimation by comparing the prediction of the model to traditional algorithms on the publicly available EuRoC MAV dataset. The results illustrate a $25 \%$ improvement in estimation accuracy over the baseline. Finally, we integrate the architecture in the closed-loop flight control system of Airsim - a plugin simulator for Unreal Engine - and we provide simulation results for Autonomous Navigation and landing
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Learning Pose Estimation for UAV Autonomous Navigation and Landing Using Visual-Inertial Sensor Data
'Institute of Electrical and Electronics Engineers (IEEE)', 2020Co-Authors: Baldini Francesca, Anandkumar Animashree, Murray, Richard M.Abstract:In this work, we propose a robust network-in-the-loop control system for Autonomous Navigation and landing of an Unmanned-Aerial-Vehicle (UAV). To estimate the UAV’s absolute pose, we develop a deep neural network (DNN) architecture for visual-inertial odometry, which provides a robust alternative to traditional methods. We first evaluate the accuracy of the estimation by comparing the prediction of our model to traditional visual-inertial approaches on the publicly available EuRoC MAV dataset. The results indicate a clear improvement in the accuracy of the pose estimation up to 25% over the baseline. Finally, we integrate the data-driven estimator in the closed-loop flight control system of Airsim, a simulator available as a plugin for Unreal Engine, and we provide simulation results for Autonomous Navigation and landing
