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Robotics, Volume 2, Issue 1 (March 2013), Pages 1-35

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Research

Open AccessArticle Ant Robotic Swarm for Visualizing Invisible Hazardous Substances
Robotics 2013, 2(1), 1-18; doi:10.3390/robotics2010001
Received: 8 November 2012 / Revised: 21 December 2012 / Accepted: 4 January 2013 / Published: 7 January 2013
Cited by 3 | PDF Full-text (866 KB) | HTML Full-text | XML Full-text
Abstract
Inspired by the simplicity of how nature solves its problems, this paper presents a novel approach that would enable a swarm of ant robotic agents (robots with limited sensing, communication, computational and memory resources) form a visual representation of distributed hazardous substances within
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Inspired by the simplicity of how nature solves its problems, this paper presents a novel approach that would enable a swarm of ant robotic agents (robots with limited sensing, communication, computational and memory resources) form a visual representation of distributed hazardous substances within an environment dominated by diffusion processes using a decentralized approach. Such a visual representation could be very useful in enabling a quicker evacuation of a city’s population affected by such hazardous substances. This is especially true if the ratio of emergency workers to the population number is very small. Full article
(This article belongs to the Special Issue Human Centred Robotics)
Open AccessArticle Sensor-Based Trajectory Generation for Advanced Driver Assistance System
Robotics 2013, 2(1), 19-35; doi:10.3390/robotics2010019
Received: 11 January 2013 / Revised: 19 February 2013 / Accepted: 5 March 2013 / Published: 11 March 2013
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Abstract
This paper investigates the trajectory generation problem for an advanced driver assistance system that could sense the driving state of the vehicle, so that a collision free trajectory can be generated safely. Specifically, the problem of trajectory generation is solved for the safety
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This paper investigates the trajectory generation problem for an advanced driver assistance system that could sense the driving state of the vehicle, so that a collision free trajectory can be generated safely. Specifically, the problem of trajectory generation is solved for the safety assessment of the driving state and to manipulate the vehicle in order to avoid any possible collisions. The vehicle senses the environment so as to obtain information about other vehicles and static obstacles ahead. Vehicles may share the perception of the environment via an inter-vehicle communication system. The planning algorithm is based on a visibility graph. A lateral repulsive potential is applied to adaptively maintain a trade-off between the trajectory length and vehicle clearance, which is the greatest problem associated with visibility graphs. As opposed to adaptive roadmap approaches, the algorithm exploits the structured nature of the environment for construction of the roadmap. Furthermore, the mostly organized nature of traffic systems is exploited to obtain orientation invariance, which is another limitation of both visibility graphs and adaptive roadmaps. Simulation results show that the algorithm can successfully solve the problem for a variety of commonly found scenarios. Full article
(This article belongs to the Special Issue Human Centred Robotics)

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