The Experts below are selected from a list of 110121 Experts worldwide ranked by ideXlab platform
Ruixing Long - One of the best experts on this subject based on the ideXlab platform.
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a motion Planning Algorithm for the rolling body problem
IEEE Transactions on Robotics, 2010Co-Authors: F. Alouges, Yacine Chitour, Ruixing LongAbstract:In this paper, we consider the control system defined by the rolling of a strictly convex surface S of IR3 on a plane without slipping or spinning. The purpose of this paper is to present the numerical implementation of a constructive Planning Algorithm for Σ, which is based on a continuation method. The performances of that Algorithm, both in robustness and convergence speed, are illustrated through several examples.
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A motion Planning Algorithm for the rolling-body problem
2009Co-Authors: F. Alouges, Yacine Chitour, Ruixing LongAbstract:In this paper, we consider the control system Σ defined by the rolling of a stricly convex surface S on a plane without slipping or spinning. It is well known that Σ is completely controllable. The purpose of this paper is to present the numerical implementation of a constructive Planning Algorithm for Σ, which is based on a continuation method. The performances of that Algorithm, both in robustness and convergence speed, are illustrated through two examples rolling of a flattened ball and an egg on the plane. ©2009 IEEE.
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a motion Planning Algorithm for the rolling body problem
Conference on Decision and Control, 2009Co-Authors: F. Alouges, Yacine Chitour, Ruixing LongAbstract:In this paper, we consider the control system Σ defined by the rolling of a stricly convex surface S on a plane without slipping or spinning. It is well known that Σ is completely controllable. The purpose of this paper is to present the numerical implementation of a constructive Planning Algorithm for Σ, which is based on a continuation method. The performances of that Algorithm, both in robustness and convergence speed, are illustrated through two examples: rolling of a flattened ball and an egg on the plane.
F. Alouges - One of the best experts on this subject based on the ideXlab platform.
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a motion Planning Algorithm for the rolling body problem
IEEE Transactions on Robotics, 2010Co-Authors: F. Alouges, Yacine Chitour, Ruixing LongAbstract:In this paper, we consider the control system defined by the rolling of a strictly convex surface S of IR3 on a plane without slipping or spinning. The purpose of this paper is to present the numerical implementation of a constructive Planning Algorithm for Σ, which is based on a continuation method. The performances of that Algorithm, both in robustness and convergence speed, are illustrated through several examples.
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A motion Planning Algorithm for the rolling-body problem
2009Co-Authors: F. Alouges, Yacine Chitour, Ruixing LongAbstract:In this paper, we consider the control system Σ defined by the rolling of a stricly convex surface S on a plane without slipping or spinning. It is well known that Σ is completely controllable. The purpose of this paper is to present the numerical implementation of a constructive Planning Algorithm for Σ, which is based on a continuation method. The performances of that Algorithm, both in robustness and convergence speed, are illustrated through two examples rolling of a flattened ball and an egg on the plane. ©2009 IEEE.
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a motion Planning Algorithm for the rolling body problem
Conference on Decision and Control, 2009Co-Authors: F. Alouges, Yacine Chitour, Ruixing LongAbstract:In this paper, we consider the control system Σ defined by the rolling of a stricly convex surface S on a plane without slipping or spinning. It is well known that Σ is completely controllable. The purpose of this paper is to present the numerical implementation of a constructive Planning Algorithm for Σ, which is based on a continuation method. The performances of that Algorithm, both in robustness and convergence speed, are illustrated through two examples: rolling of a flattened ball and an egg on the plane.
Asok Ray - One of the best experts on this subject based on the ideXlab platform.
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ν a robot path Planning Algorithm based on renormalised measure of probabilistic regular languages
International Journal of Control, 2009Co-Authors: Ishanu Chattopadhyay, Goutham Mallapragada, Asok RayAbstract:This article introduces a novel path Planning Algorithm, called ν ☆, that reduces the problem of robot path Planning to optimisation of a probabilistic finite state automaton. The ν ☆-Algorithm makes use of renormalised measure ν of regular languages to plan the optimal path for a specified goal. Although the underlying navigation model is probabilistic, the ν ☆-Algorithm yields path plans that can be executed in a deterministic setting with automated optimal trade-off between path length and robustness under dynamic uncertainties. The ν ☆-Algorithm has been experimentally validated on Segway Robotic Mobility Platforms in a laboratory environment.
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l an intelligent path Planning Algorithm based on renormalized measure of probabilistic regular languages
American Control Conference, 2008Co-Authors: Ishanu Chattopadhyay, Goutham Mallapragada, Asok RayAbstract:A novel path Planning Algorithm L* is introduced that reduces the problem to optimization of a probabilistic finite state machine and applies the rigorous theory of language-measure-theoretic optimal control to compute v-optimal paths to the specified goal. It is shown that although the underlying navigation model is probabilistic, the proposed Algorithm computes plans that can be executed in a deterministic sense with automated optimal trade-off between path length and robustness under dynamic uncertainty. The Algorithm has been validated on mobile robotic platforms in a laboratory environment.
Myoung Ho Sunwoo - One of the best experts on this subject based on the ideXlab platform.
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deceleration Planning Algorithm based on classified multi layer perceptron models for smart regenerative braking of ev in diverse deceleration conditions
Sensors, 2019Co-Authors: Gyubin Sim, Kyunghan Min, Seongju Ahn, Myoung Ho SunwooAbstract:The smart regenerative braking system (SRS) is an autonomous version of one-pedal driving in electric vehicles. To implement SRS, a deceleration Planning Algorithm is necessary to generate the deceleration used in automatic regenerative control. To reduce the discomfort from the automatic regeneration, the deceleration should be similar to human driving. In this paper, a deceleration Planning Algorithm based on multi-layer perceptron (MLP) is proposed. The MLP models can mimic the human driving behavior by learning the driving data. In addition, the proposed deceleration Planning Algorithm has a classified structure to improve the Planning performance in each deceleration condition. Therefore, the individual MLP models were designed according to three different deceleration conditions: car-following, speed bump, and intersection. The proposed Algorithm was validated through driving simulations. Then, time to collision and similarity to human driving were analyzed. The results show that the minimum time to collision was 1.443 s and the velocity root-mean-square error (RMSE) with human driving was 0.302 m/s. Through the driving simulation, it was validated that the vehicle moves safely with desirable velocity when SRS is in operation, based on the proposed Algorithm. Furthermore, the classified structure has more advantages than the integrated structure in terms of Planning performance.
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Behavior and path Planning Algorithm of autonomous vehicle a1 in structured environments
IFAC Proceedings Volumes (IFAC-PapersOnline), 2013Co-Authors: Junsoo Kim, Keonyup Chu, Dongchul Kim, Kichun Jo, Myoung Ho SunwooAbstract:This paper presents a behavior and path Planning Algorithm that is responsible for safe autonomous driving in structured environments. In order to accomplish safe driving, the mission planner makes a decision of vehicle control mode by using a road map and perception information. Based on the vehicle control mode, the path planner generates the optimal path to reach the destination without collision or traffic accidents. The control strategy of acceleration and deceleration of the vehicle is determined for accurate mission completion by a longitudinal motion planner. The proposed Planning Algorithms were implemented on the autonomous vehicle A1 that won the 2012 Autonomous Vehicle Competition (AVC) organized by the Hyundai Motor Group in Korea. © 2013 IFAC.
Jiang Tie - One of the best experts on this subject based on the ideXlab platform.
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Path Planning Algorithm in Time-dependent Network Based on Forecasting Information
Computer Engineering, 2012Co-Authors: Jiang TieAbstract:This paper analyzes the two minimum time Planning Algorithms.In allusion to deficiencies in the application of the Algorithm,based on the SPFA static Planning Algorithm,combined with the advantages of the two Planning Algorithms,it proposes a minimum time path Planning improved Algorithm.Example results show that the Algorithm can provide path Planning with real-time,high efficiency and predictability.It better meets the needs of users in urban traffic.
