In this paper, we present "IVO: Inverse Velocity Obstacles" an ego-centric framework that improves the real time implementation. The proposed method stems from the concept of velocity obstacle and can be applied for both single agent and multi-agent system. It focuses on computing collision free maneuvers without any knowledge or assumption on the pose and the velocity of the robot. This is primarily achieved by reformulating the velocity obstacle to adapt to an ego-centric framework. This is a significant step towards improving real time implementations of collision avoidance in dynamic environments as there is no dependency on state estimation techniques to infer the robot pose and velocity. We evaluate IVO for both single agent and multi-agent in different scenarios and show it's efficacy over the existing formulations. We also show the real time scalability of the proposed methodology.
In this section, we present an overview of the approaches on collision avoidance and navigation in dynamic environment. Quite a few approaches @cite_4 , @cite_2 , @cite_14 , @cite_8 assume that the obstacles are static and plan for the control to avoid the collision. In case of moving obstacles these replan based on the updated positions of the obstacles. But these fail to generate the safe trajectories around the fast moving obstacles. In @cite_0 , @cite_16 , @cite_20 , @cite_10 the future position of obstacles are computed by extrapolating with the current velocity to handle high velocities. But these approaches cannot handle the reactive nature of the other agents. Many works like @cite_1 , @cite_5 , @cite_3 , @cite_19 have focused on crowd simulation in which each agent considers the other agents as obstacles and navigates independently.
@cite_14 @cite_4 @cite_8 @cite_1 @cite_3 @cite_0 @cite_19 @cite_2 @cite_5 @cite_16 @cite_10 @cite_20
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In this paper, we propose a new concept - the "Reciprocal Velocity Obstacle"- for real-time multi-agent navigation. We consider the case in which each agent navigates independently without explicit communication with other agents. Our formulation is an extension of the Velocity Obstacle concept [3], which was introduced for navigation among (passively) moving obstacles. Our approach takes into account the reactive behavior of the other agents by implicitly assuming that the other agents make a similar collision-avoidance reasoning. We show that this method guarantees safe and oscillation- free motions for each of the agents. We apply our concept to navigation of hundreds of agents in densely populated environments containing both static and moving obstacles, and we show that real-time and scalable performance is achieved in such challenging scenarios. Abstract The periodic Lorentz gas is the dynamical system corresponding to the free motion of a point particle in a periodic system of fixed spherical obstacles of radius r centered at the integer points of the Euclidian plane, assuming all collisions of the particle with the obstacles to be elastic. In this Note, we study this motion on time intervals of order 1 r as r → 0 + . To cite this article: E. Caglioti, F. Golse, C. R. Acad. Sci. Paris, Ser. I 346 (2008). A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Purpose – The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations.Design methodology approach – ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets – a citation network based on an early paper on webometrics, and a self‐citation network based on the 19 most cited papers in the Journal of Documentation – using citation data taken from the Web of Knowledge database.Findings – ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited ...