A portable, autonomous, urban reconnaissance robot

Matthies, Larry; Xiong, Yalin; Hogg, Robert; Zhu, David; Rankin, Arturo L.; Kennedy, B. M.; Hebert, Martial; MacLachlan, Robert A.; Won, Chi; Frost, T.M.E.; Sukhatme, Gaurav S.; McHenry, Michael; Goldberg, Steve

doi:10.1016/s0921-8890(02)00241-5

Cited by 111 publications

(49 citation statements)

References 7 publications

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“…We consider here a ladar collecting range measurements as it rotates around a vertical axis. In particular, the examples in this paper are based on data from a customized Acuity AccuRange 4000 (Bergh et al, 2000;Matthies et al, 2000). This ladar operates at a wavelength of 780 nm, in the NIR domain, rotates at a rate of 5 Hz and can estimate range up to distances of 15 m (50 ft).…”

Section: Ladar-based Classificationmentioning

confidence: 99%

Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation

et al. 2005

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Abstract. Autonomous navigation in cross-country environments presents many new challenges with respect to more traditional, urban environments. The lack of highly structured components in the scene complicates the design of even basic functionalities such as obstacle detection. In addition to the geometric description of the scene, terrain typing is also an important component of the perceptual system. Recognizing the different classes of terrain and obstacles enables the path planner to choose the most efficient route toward the desired goal.This paper presents new sensor processing algorithms that are suitable for cross-country autonomous navigation. We consider two sensor systems that complement each other in an ideal sensor suite: a color stereo camera, and a single axis ladar. We propose an obstacle detection technique, based on stereo range measurements, that does not rely on typical structural assumption on the scene (such as the presence of a visible ground plane); a color-based classification system to label the detected obstacles according to a set of terrain classes; and an algorithm for the analysis of ladar data that allows one to discriminate between grass and obstacles (such as tree trunks or rocks), even when such obstacles are partially hidden in the grass. These algorithms have been developed and implemented by the Jet Propulsion Laboratory (JPL) as part of its involvement in a number of projects sponsored by the US Department of Defense, and have enabled safe autonomous navigation in high-vegetated, off-road terrain.

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Section: Ladar-based Classificationmentioning

confidence: 99%

Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation

et al. 2005

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“…This type of research is mainly focussed on mechanical structures of articulated wheels and tracks and their ability to pass obstacles. Although most of these rovers are remotely controlled, research aims also at developing sensors for autonomous operation (Vandapel et al, 1999) or to help the remote operator (Matthies et al, 2002).…”

Section: Rough Terrain Navigationmentioning

confidence: 99%

Swarm-Bot: A New Distributed Robotic Concept

Pettinaro²,

et al. 2004

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Abstract. The swarm intelligence paradigm has proven to have very interesting properties such as robustness, flexibility and ability to solve complex problems exploiting parallelism and self-organization. Several robotics implementations of this paradigm confirm that these properties can be exploited for the control of a population of physically independent mobile robots.The work presented here introduces a new robotic concept called swarm-bot in which the collective interaction exploited by the swarm intelligence mechanism goes beyond the control layer and is extended to the physical level. This implies the addition of new mechanical functionalities on the single robot, together with new electronics and software to manage it. These new functionalities, even if not directly related to mobility and navigation, allow to address complex mobile robotics problems, such as extreme all-terrain exploration.The work shows also how this new concept is investigated using a simulation tool (swarmbot3d) specifically developed for quickly designing and evaluating new control algorithms. Experimental work shows how the simulated detailed representation of one s-bot has been calibrated to match the behaviour of the real robot.

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“…[24] This robot is equipped with an enormous array of sensors, actuators, and processing power. It is designed to navigate through very tricky environments.…”

Section: Energy Usage Calculations and Comparisonmentioning

confidence: 99%

Parasitic Mobility for Pervasive Sensor Networks

Laibowitz¹,

Paradiso²

2005

Lecture Notes in Computer Science

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Abstract. Distributed sensor networks offer many new capabilities for contextually monitoring environments. By making such systems mobile, we increase the application-space for the distributed network mainly by providing dynamic context-dependent deployment, continual relocatability, automatic node recovery, and a larger area of coverage. In existing models, the addition of actuation to the nodes has exacerbated three of the main problems with distributed systems: power usage, node size, and node complexity. In this paper we propose a solution to these problems in the form of parasitically actuated nodes that harvest their mobility and local navigational intelligence by selectively engaging and disengaging from mobile hosts in their environment. We analyze the performance of parasitically mobile distributed networks through software simulations and design, implement, and demonstrate hardware prototypes.

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A portable, autonomous, urban reconnaissance robot

Cited by 111 publications

References 7 publications

Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation

Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation

Swarm-Bot: A New Distributed Robotic Concept

Parasitic Mobility for Pervasive Sensor Networks

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