The Rocky 7 Mars rover prototype

Volpe, R.; Balaram, J.; Ohm, Timothy R.; Ivlev, R.

doi:10.1109/iros.1996.569020

Cited by 88 publications

(31 citation statements)

References 6 publications

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“…Some highlights of prior work include autonomous cross country navigation systems developed on HMMWV's 1 [1], Mars rover research prototypes [2], and indoor mapping systems developed on wheeled indoor robots [3]. Contributions of our effort include a small mobility chassis suitable for mixed outdoor and indoor terrain, design of a small, two-axis scanning laser rangefinder for indoor mapping and position estimation, packaging of a large number of sensors and significant computing power in a 20 kg robot, and algorithms for new autonomous navigation capabilities in the areas of obstacle avoidance, visual servoing, and stair climbing.…”

Section: Introductionmentioning

confidence: 99%

A portable, autonomous, urban reconnaissance robot

Matthies

Xiong

Hogg

et al. 2002

Robotics and Autonomous Systems

111

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Portable mobile robots, in the size class of 20 kg or less, could be extremely valuable as autonomous reconnaissance platforms in urban hostage situations and disaster relief. We have developed a prototype urban robot on a novel chassis with articulated tracks that enable stair climbing and scrambling over rubble. Autonomous navigation capabilities of the robot include stereo vision-based obstacle avoidance, visual servoing to user-designated goals, and autonomous vision-guided stair climbing. The system was demonstrated in an urban reconnaissance mission scenario at Fort Sam Houston in October 1999. A two-axis scanning laser rangefinder has been developed and will be integrated in the coming year for indoor mapping and position estimation. This paper describes the robot, its performance in field trials, and some of the technical challenges that remain to enable fieldable urban reconnaissance robots.

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Section: Introductionmentioning

confidence: 99%

A portable, autonomous, urban reconnaissance robot

Matthies

Xiong

Hogg

et al. 2002

Robotics and Autonomous Systems

111

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“…Most notable are the experiments with the Autonomous Land Vehicle "ALV" [21,22], the Australian Center for Field Robotics High Speed Vehicle "HSV" [3,86] and Carnegie Mellon University's "NAVLAB" [35,44] (Figure 2.1). The groups at the Jet Propulsion Laboratory (JPL) [68,121] and Laboratoire d'Analyse et d'Architecture des Systèmes (LAAS) [66,78] have focused on the extraterrestrial application domain of sensing and navigating in unstructured environments. Whereas the groups at Space and Naval Warfare Systems Center (SPAWAR) [41,90], National Institute of Standards and Technology NIST [46,97], SAIC [103], Perceptek [102,104] and also JPL [7] have been looking at navigating large Unmanned Ground Vehicles under one of the many DARPA and ARL programs [112].…”

Section: Path Planning For Ground Vehiclesmentioning

confidence: 99%

Extending the Path-Planning Horizon

Nabbe¹,

Hebert

2007

The International Journal of Robotics Research

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SINCE typical mobile robotic vehicles have mobility sensors (such as LADAR or stereo) that can only acquire data up to a few tens of meters, a navigation system has no knowledge about the world beyond this sensing horizon. As a result, path planners that rely only on this knowledge to compute paths are unable to anticipate obstacles sufficiently early and has no choice than to plan inefficient paths that trace obstacle boundaries. To alleviate this problem, We present an opportunistic navigation and view planning strategy that incorporates look-ahead sensing of possible obstacle configurations. This planning strategy is based on a "what-if" analysis of hypothetical future configurations of the environment. Candidate vantage positions are evaluated based on their ability of observing anticipated obstacles. These vantage positions identified by this forward-simulation framework are used by the planner as intermediate waypoints.The validity of the strategy is supported by results from simulations as well as field experiments with a real robotic platform. These results also show that opportunistically significant reduction in path length can be achieved by using this framework. The reconstructed epipolar geometry from the very wide baseline100 A.9The ratio of the height over the width of the window on the left is 1.5456 and the window on the right 1.8356, which gives us an indication of the reconstruction quality. This effect is due to the fact that the planner is limited by the maximum range of the mobility sensors. Since typical mobility sensors, such as Laser Radar (LADAR) or passive stereo vision, will only acquire data up to a few tens of meters, the planner has no knowledge about what to encounter beyond the sensed perimeter. As a result, the planner is unable to anticipate obstacles sufficiently early and has no choice but to plan paths close to obstacle boundaries.For autonomous navigation over long distances, this issue degrades the performance of the system by greatly increasing the length of the path traveled by the vehicle. Consequently, the power consumed is increased and, more importantly, CHAPTER 1. INTRODUCTION FIGURE 1.1. Typical example of poor performance due to lack of sensor planning and mid-range sensing (Left: Overhead view of terrain; Right: Executed path with detected obstacles shown as shaded regions). The path from S1 to G1 intersects a large hill which is discovered only when the vehicle enters a large cul-de-sac, causing the executed path to be substantially more expensive than the path that would have been followed, had the obstruction been discovered earlier. (From [114]) the risk of exposure to threats is also increased. In addition, the relative short range of the mobility sensors forces the vehicle to drive closer to terrain obstructions than is safe or necessary.One solution is to use sensors with longer range to acquire information about the terrain further ahead. However, to use these sensors one needs to take into account certain constraints about the sensing geom...

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“…Correlation is the basic method used to find corresponding pixels. Several real time systems have been developed using correlation based stereo (Konolige ,1997) ( Matthies et al, 1995) (Volpe et al, 1996) (Guisse et al, 2000 Images of cameras are in two dimensional spaces and for each point with losing the depth in images can obtain one line in real world. The relations in stereo vision demonstrate that measure of depth's points in each image is obtained as shown in (Gonzalez,1998):…”

Section: Mapping Relations In Robotsmentioning

confidence: 99%