The CMU system for mobile robot navigation

Goto, Yoshimasa; Stentz, Anthony

doi:10.1109/robot.1987.1088022

Cited by 57 publications

(17 citation statements)

References 8 publications

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“…Other pioneering research contributions on computer vision for road following include those of Waxman's et al [154], [155], Wallace et al [152] and Wallace [153], Lawton et al [88], Goto and Stentz [45], Kuan et al [83] and Kuan and Sharma [84], and others.…”

Section: Outdoor Navigation In Structured Environmentsmentioning

confidence: 99%

Vision for mobile robot navigation: a survey

DeSouza

Kak

2002

IEEE Trans. Pattern Anal. Machine Intell.

1,181

504

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Abstract-This paper surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.

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Section: Outdoor Navigation In Structured Environmentsmentioning

confidence: 99%

Vision for mobile robot navigation: a survey

DeSouza

Kak

2002

IEEE Trans. Pattern Anal. Machine Intell.

1,181

504

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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.…”

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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“…Similar to the Codger 8 system architecture used to drive CMU's Navlab, 9 our system does not use its blackboard as a problem-solving mechanism but primarily as the supervisor and coordinator of several realtime activities. These activities continually post their current state and the current state of the environment to the blackboard.…”

Section: Eveloping a Mobile Robotmentioning

confidence: 99%

“…This generally leads to a system well suited for task planning and capable of managing several distributed processes but with little or no capability for reactive behavior. For that reason, most mobile-robot projects using blackboard systems 9,13,14 have modified the blackboards to manage the real-time issues peculiar to this field.…”

Section: Requirementsmentioning

confidence: 99%