Experience with rover navigation for lunar-like terrains

Simmons, Reid; Krotkov, Eric; Chrisman, Lonnie; Cozman, Fábio Gagliardi; Goodwin, Richard; Hebert, Martial; Katragadda, Lalitesh K.; Koenig, Sven; Krishnaswamy, Gita; Shinoda, Yoshikazu; Whittaker, William; Klarer, Paul R.

doi:10.1109/iros.1995.525833

Cited by 61 publications

(28 citation statements)

References 11 publications

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“…39) [157], [158], [12], [81], [99]. Sometimes in these sorts of applications, the robot is supposed to just wander around, exploring the vicinity of the robot without a clearcut goal [91], [136]. However, in other applications, the task to be performed requires that the robot follow a specific path to a goal position.…”

Section: Unstructured Outdoor Navigationmentioning

confidence: 99%

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Vision for mobile robot navigation: a survey

DeSouza

Kak

2002

IEEE Trans. Pattern Anal. Machine Intell.

1,193

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: Unstructured Outdoor Navigationmentioning

confidence: 99%

“…The positioning system uses traditional sensors such as encoders, inclinometers, a compass, etc. Also using the RATLER vehicle, the work reported in [136] implements a system that uses stereo vision to build a map that represents the terrain up to seven meters in front of the rover.…”

Section: Unstructured Outdoor Navigationmentioning

confidence: 99%

Vision for mobile robot navigation: a survey

DeSouza

Kak

2002

IEEE Trans. Pattern Anal. Machine Intell.

1,193

504

View full text Add to dashboard Cite

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“…Trajectory selection was based on a convolution on a cost or goodness map. This approach was an extension of Morphin, an arc-planner variant where terrain shape was considered in the trajectory selection process [11]. Another closely related algorithm is the one presented in [3], where the an arc-based search space is evaluated based on considering risk and interest.…”

Section: Related Workmentioning

confidence: 99%

Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths

Howard

Green

Kelly

2010

Springer Tracts in Advanced Robotics

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As mobile robots venture into more complex environments, more arbitrary feasible state-space trajectories and paths are required to move safely and efficiently. The capacity to effectively navigate such paths in the face of disturbances and changes in mobility can mean the difference between mission failure and success. This paper describes a technique for model predictive control of a mobile robot that utilizes the structure of a regional motion plan to effectively search the local continuum for an improved solution. The contribution, the receding horizon model-predictive control algorithm, specifically addresses the problem of path following and obstacle avoidance through cusps, turn-in-place, and multi-point turn maneuvers in environments where terrain shape and vehicle mobility effects are non-negligible. The technique is formulated as an optimal controller that utilizes a model-predictive trajectory generator to determine parameterized control inputs that navigate general mobile robots safely through the environment. Experimental results are presented for a a six-wheeled skid-steered field robot in natural terrain.

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“…Elevation maps and vision techniques can be used to assess terrain for navigation. In (Langer et al, 1994;Simmons et al, 1995;Gennery, 1999), the authors employed similar approaches using elevation maps, which are derived from stereo vision or laser rangefinders, to assign a traversability measure within a grid map cell based on height, slope, height variability, and/or terrain knowability. A search algorithm generated an optimal path from these measures.…”

Section: Related Workmentioning

confidence: 99%

Terrain Classification Using Weakly-Structured Vehicle/Terrain Interaction

2005

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Abstract. We present a new terrain classification technique both for effective, autonomous locomotion over rough, unknown terrains and for the qualitative analysis of terrains for exploration and mapping. Our approach requires a single camera with little processing of visual information. Specifically, we derived a gait bounce measure from visual servoing errors that results from vehicle-terrain interactions during normal locomotion. Characteristics of the terrain, such as roughness and compliance, manifest themselves in the spatial patterns of this signal and can be extracted using pattern classification techniques. This vision-based approach is particularly beneficial for resource-constrained robots with limited sensor capability. In this paper, we present the gait bounce derivation. We demonstrate the viability of terrain classification for legged vehicles using gait bounce with a rigorous study of more than 700 trials, obtaining an 83% accuracy on a set of laboratory terrains. We describe how terrain classification may be used for gait adaptation, particularly in relation to an efficiency metric. We also demonstrate that our technique may be generally applicable to other locomotion mechanisms such as wheels and treads.

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Experience with rover navigation for lunar-like terrains

Cited by 61 publications

References 11 publications

Vision for mobile robot navigation: a survey

Vision for mobile robot navigation: a survey

Receding Horizon Model-Predictive Control for Mobile Robot Navigation of Intricate Paths

Terrain Classification Using Weakly-Structured Vehicle/Terrain Interaction

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