The road more traveled: a foundation for autonomous roadway operations

Rosenblum, Mark L.; Rajagopalan, Venkataramanan

doi:10.1117/12.605464

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…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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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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Mobility Performance Algorithms for Small Unmanned Ground Vehicles

Richmond¹,

Mason²,

Coutermarsh³

et al. 2009

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Future Combat Systems will include Small Unmanned Ground Vehicles (SUGV). Several have already been deployed, including the TALON ® , an 80-lb SUGV; PackBot ® at 50 lb, and Gator TM at 2500 lb. As doctrine, tactics, techniques, and procedures continue to evolve, there exists a need to represent the performance of these vehicles in Army models and simulations. Army simulations such as COMBAT XXI and OneSAF will use the Standard Mobility Application Programmers Interface (STNDMob API) for estimating vehicle performance. Currently, only the Gator can be represented by a STNDMob vehicle class, and it is modeled as a manned vehicle.This report describes the results of a study undertaken to identify and discuss mobility performance algorithms applicable to SUGV in the weight range of 10 to 5000 lb. Algorithms used by the NATO Reference Mobility Model and the STNDMob were examined. Most of the algorithms currently used in STNDMob were found to be applicable to SUGV. However, it was found that vehicle performance data and algorithms for additional material surfaces and obstacles need to be developed; specifically, models and data for wheeled vehicle skid steering, interior building floor and roof surfaces, and stair climbing are needed. Previously unreported SUGV test results for TALON, MATILDA, and PackBot are presented, and performance estimates described herein compared well using currently available algorithms in STNDMob. Recommendations for new algorithms and improvements to current mobility algorithms are presented.

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The road more traveled: a foundation for autonomous roadway operations

Cited by 2 publications

References 6 publications

Extending the Path-Planning Horizon

Extending the Path-Planning Horizon

Mobility Performance Algorithms for Small Unmanned Ground Vehicles

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