The Experts below are selected from a list of 13443 Experts worldwide ranked by ideXlab platform
Andrey V Savkin - One of the best experts on this subject based on the ideXlab platform.
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A Hybrid Approach for Autonomous Collision-Free UAV Navigation in 3D Partially Unknown Dynamic Environments
'MDPI AG', 2021Co-Authors: Taha Elmokadem, Andrey V SavkinAbstract:In the past decades, unmanned aerial vehicles (UAVs) have emerged in a wide range of applications. Owing to the advances in UAV technologies related to sensing, computing, power, etc., it has become possible to carry out missions autonomously. A key component to achieving this goal is the development of Safe Navigation methods, which is the main focus of this work. A hybrid Navigation approach is proposed to allow Safe autonomous operations in three-dimensional (3D) partially unknown and dynamic environments. This method combines a global path planning algorithm, namely RRT-Connect, with a reactive control law based on sliding mode control to provide quick reflex-like reactions to newly detected obstacles. The performance of the suggested approach is validated using simulations
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an algorithm for Safe Navigation of mobile robots by a sensor network in dynamic cluttered industrial environments
Robotics and Computer-integrated Manufacturing, 2018Co-Authors: Andrey V SavkinAbstract:Abstract Mobile robots have been widely implemented in industrial automation and smart factories. Different types of mobile robots work cooperatively in the workspace to complete some complicated tasks. Therefore, the main requirement for multi-robot systems is collision-free Navigation in dynamic environments. In this paper, we propose a sensor network based Navigation system for ground mobile robots in dynamic industrial cluttered environments. A range finder sensor network is deployed on factory floor to detect any obstacles in the field of view and perform a global Navigation for any robots simultaneously travelling in the factory. The obstacle detection and robot Navigation are integrated into the sensor network and the robot is only required for a low-level path tracker. The novelty of this paper is to propose a sensor network based Navigation system with a novel artificial potential field (APF) based Navigation algorithm. Computer simulations and experiments confirm the performance of the proposed method.
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wireless sensor network based Navigation of micro flying robots in the industrial internet of things
IEEE Transactions on Industrial Informatics, 2018Co-Authors: Andrey V SavkinAbstract:In this paper, we propose a wireless sensor network based Safe Navigation algorithm for micro flying robots in the industrial Internet of things (IIoT). A micro flying robot cannot be equipped with heavy obstacle detection sensors for local Navigation. Therefore, in our method, a wireless sensor network consisting of three-dimensional range finder is used to detect the static and dynamic obstacles in an indoor industrial environment and navigate the micro flying robots to avoid any collisions with the obstacles. Only a path tracking controller is required for the micro flying robot and there is not any complex computation on the micro flying robot. It is an economical and efficient solution for multiple micro flying robots’ Navigation and management in the IIoT. The computer simulations confirm the expected performance of the proposed algorithm in static and dynamic environment with multiple micro flying robots.
Dinesh Manocha - One of the best experts on this subject based on the ideXlab platform.
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Safe Navigation with human instructions in complex scenes
International Conference on Robotics and Automation, 2019Co-Authors: Jia Pan, Tingxiang Fan, Ruigang Yang, Dinesh ManochaAbstract:In this letter, we present a robotic Navigation algorithm with natural language interfaces that enables a robot to Safely walk through a changing environment with moving persons by following human instructions such as “go to the restaurant and keep away from people.” We first classify human instructions into three types: goal, constraints, and uninformative phrases. Next, we provide grounding in a dynamic manner for the extracted goal and constraint items along with the Navigation process to deal with target objects that are too far away for sensor observation and the appearance of moving obstacles such as humans. In particular, for a goal phrase (e.g., “go to the restaurant”), we ground it to a location in a predefined semantic map and treat it as a goal for a global motion planner, which plans a collision-free path in the workspace for the robot to follow. For a constraint phrase (e.g., “keep away from people”), we dynamically add the corresponding constraint into a local planner by adjusting the values of a local costmap according to the results returned by the object detection module. The updated costmap is then used to compute a local collision avoidance control for the Safe Navigation of the robot. By combining natural language processing, motion planning, and computer vision, our developed system can successfully follow natural language Navigation instructions to achieve Navigation tasks in both simulated and real-world scenarios. Videos are available at https://sites.google.com/view/snhi .
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Safe Navigation with human instructions in complex scenes
arXiv: Robotics, 2018Co-Authors: Jia Pan, Tingxiang Fan, Ruigang Yang, Dinesh ManochaAbstract:In this paper, we present a robotic Navigation algorithm with natural language interfaces, which enables a robot to Safely walk through a changing environment with moving persons by following human instructions such as "go to the restaurant and keep away from people". We first classify human instructions into three types: the goal, the constraints, and uninformative phrases. Next, we provide grounding for the extracted goal and constraint items in a dynamic manner along with the Navigation process, to deal with the target objects that are too far away for sensor observation and the appearance of moving obstacles like humans. In particular, for a goal phrase (e.g., "go to the restaurant"), we ground it to a location in a predefined semantic map and treat it as a goal for a global motion planner, which plans a collision-free path in the workspace for the robot to follow. For a constraint phrase (e.g., "keep away from people"), we dynamically add the corresponding constraint into a local planner by adjusting the values of a local costmap according to the results returned by the object detection module. The updated costmap is then used to compute a local collision avoidance control for the Safe Navigation of the robot. By combining natural language processing, motion planning, and computer vision, our developed system is demonstrated to be able to successfully follow natural language Navigation instructions to achieve Navigation tasks in both simulated and real-world scenarios. Videos are available at this https URL
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efficient and Safe vehicle Navigation based on driver behavior classification
Computer Vision and Pattern Recognition, 2018Co-Authors: Ernest Cheung, Aniket Bera, Dinesh ManochaAbstract:We present an autonomous driving planning algorithm that takes into account neighboring drivers' behaviors and achieves Safer and more efficient Navigation. Our approach leverages the advantages of a data-driven mapping that is used to characterize the behavior of other drivers on the road. Our formulation also takes into account pedestrians and cyclists and uses psychology-based models to perform Safe Navigation. We demonstrate our benefits over previous methods: Safer behavior in avoiding dangerous neighboring drivers, pedestrians and cyclists, and efficient Navigation around careful drivers.
Eric Lucet - One of the best experts on this subject based on the ideXlab platform.
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online velocity fluctuation of off road wheeled mobile robots a reinforcement learning approach
International Conference on Robotics and Automation, 2021Co-Authors: Francois Gauthierclerc, Ashley Hill, Jean Laneurit, Roland Lenain, Eric LucetAbstract:During the off-road path following of a wheeled mobile robot in presence of poor grip conditions, the longitudinal velocity should be limited in order to maintain Safe Navigation with limited tracking errors, while at the same time being high enough to minimize travel time. Thus, this paper presents a new approach of online speed fluctuation, capable of limiting the lateral error below a given threshold, while maximizing the longitudinal velocity. This is accomplished using a neural network trained with a reinforcement learning method. This speed modulation is done side-by-side with an existing model-based predictive steering control, using a state estimator and dynamic observers. Simulated and experimental results show a decrease in tracking error, while maintaining a consistent travel time when compared to a classical constant speed method and to a kinematic speed fluctuation method.
Alejandro Ribeiro - One of the best experts on this subject based on the ideXlab platform.
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stochastic artificial potentials for online Safe Navigation
IEEE Transactions on Automatic Control, 2020Co-Authors: Santiago Paternain, Alejandro RibeiroAbstract:Consider a convex set of which we remove an arbitrary number of disjoints convex sets—the obstacles—and a convex function whose minimum is the agent's goal. We consider a local and stochastic approximation of the gradient of a Rimon–Koditschek Navigation function where the attractive potential is the convex function that the agent is minimizing. In particular, we show that if the estimate available to the agent is unbiased, convergence to the desired location while avoiding the obstacles is guaranteed with probability one under the same geometrical conditions as in the deterministic case. Qualitatively these conditions are that the ratio between the maximum and minimum eigenvalue of the Hessian of the objective function is not too large and that the obstacles are not too flat or too close to the desired destination. Moreover, we show that for biased estimates convergence to a point arbitrarily close to the goal is achieved with probability one. The assumptions on the bias for the result to hold are motivated by the study of the estimate of the gradient of a Rimon–Koditschek Navigation function for sensor models that fit circles around the obstacles. Numerical examples explore the practical value of these theoretical results.
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stochastic artificial potentials for online Safe Navigation
arXiv: Optimization and Control, 2016Co-Authors: Santiago Paternain, Alejandro RibeiroAbstract:Consider a convex set of which we remove an arbitrarily number of disjoints convex sets -- the obstacles -- and a convex function whose minimum is the agent's goal. We consider a local and stochastic approximation of the gradient of a Rimon-Koditschek Navigation function where the attractive potential is the convex function that the agent is minimizing. In particular we show that if the estimate available to the agent is unbiased convergence to the desired destination while obstacle avoidance is guaranteed with probability one under the same geometrical conditions than in the deterministic case. Qualitatively these conditions are that the ratio of the maximum over the minimum eigenvalue of the Hessian of the objective function is not too large and that the obstacles are not too flat or too close to the desired destination. Moreover, we show that for biased estimates a similar result holds under some assumptions on the bias. These assumptions are motivated by the study of the estimate of the gradient of a Rimon-Koditschek Navigation function for sensor models that fit circles or ellipses around the obstacles. Numerical examples explore the practical value of these theoretical results.
Nikolay Atanasov - One of the best experts on this subject based on the ideXlab platform.
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learning barrier functions with memory for robust Safe Navigation
International Conference on Robotics and Automation, 2021Co-Authors: Kehan Long, Cheng Qian, Jorge E Cortes, Nikolay AtanasovAbstract:Control barrier functions are widely used to enforce Safety properties in robot motion planning and control. However, the problem of constructing barrier functions online and synthesizing Safe controllers that can deal with the associated uncertainty has received little attention. This letter investigates Safe Navigation in unknown environments, using on-board range sensing to construct control barrier functions online. To represent different objects in the environment, we use the distance measurements to train neural network approximations of the signed distance functions incrementally with replay memory. This allows us to formulate a novel robust control barrier Safety constraint which takes into account the error in the estimated distance fields and its gradient. Our formulation leads to a second-order cone program, enabling Safe and stable control synthesis in a prior unknown environments.
