Obtaining Optimal Mobile-Robot Paths with Nonsmooth Anisotropic Cost Functions Using Qualitative-State Reasoning

Rowe, Neil C.

doi:10.1177/027836499701600307

Cited by 25 publications

(10 citation statements)

References 12 publications

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“…In each of these areas, researchers create the models specific to said application; unfortunately their analysis and results cannot be easily transferred to other problems. For example, the problem of computing an optimal path for a mobile robot considers friction and gravity forces for various regions of terrain, and then uses this direction and location dependent cost function to find a path that minimizes the total energy consumption of the robot [34,62,63,68]. Since surface contour does not change over time, this set of problems only considers path finding in a static environment.…”

Section: Literature Overviewmentioning

confidence: 99%

“…Furthermore, presence of obstacles is not commonly addressed in the published studies. For example, Rowe [62] and Rowe and Ross [63] study optimal path finding for a mobile agent (e.g., robot or vehicle) across hilly terrains, where a simple and specific physical model of friction and gravity forces is used to compute the anisotropic cost function for the agent.…”

Section: Related Workmentioning

confidence: 99%

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Optimal path finding in direction, location, and time dependent environments

Dolinskaya

2012

Naval Research Logistics

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This article examines optimal path finding problems where cost function and constraints are direction, location, and time dependent. Recent advancements in sensor and data‐processing technology facilitate the collection of detailed real‐time information about the environment surrounding a ground vehicle, an airplane, or a naval vessel. We present a navigation model that makes use of such information. We relax a number of assumptions from existing literature on path‐finding problems and create an accurate, yet tractable, model suitable for implementation for a large class of problems. We present a dynamic programming model which integrates our earlier results for direction‐dependent, time and space homogeneous environment, and consequently, improves its accuracy, efficiency, and run‐time. The proposed path finding model also addresses limited information about the surrounding environment, control‐feasibility of the considered paths, such as sharpest feasible turns a vehicle can make, and computational demands of a time‐dependent environment. To demonstrate the applicability and performance of our path‐finding algorithm, computational experiments for a short‐range ship routing in dynamic wave‐field problem are presented. © 2012 Wiley Periodicals, Inc. Naval Research Logistics, 2012

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Section: Literature Overviewmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimal path finding in direction, location, and time dependent environments

Dolinskaya

2012

Naval Research Logistics

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“…Optimal path finding problems in anisotropic media have been addressed for a few specific applications, and the solution approach and results are often customized to the application at hand. For example, [7,8] study optimal path finding for a mobile agent (e.g., robot or vehicle) across hilly terrains, where a physical model of friction and gravity forces is used to compute the anisotropic cost function for the agent.…”

Section: Related Workmentioning

confidence: 99%

Fastest-Path Planning for Direction-Dependent Speed Functions

Dolinskaya

Smith

2012

J Optim Theory Appl

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We discuss path planning in a direction-dependent environment illustrated by the fastestpath problem with anisotropic speed function. The difficulty of optimal-path finding in a direction-dependent medium comes from the fact that our travel-time function is asymmetric, and in general, violates the triangle inequality. We present an analytical form solution for the fastest-path finding problem in an obstacle-free domain without making any assumptions on the structure of the speed function. Subsequently, we merge these results with visibility graph search methods to develop an obstacle-avoiding fastest-path finding algorithm for an anisotropic speed function. Optimal routing of a vessel in a stationary random seaway is discussed throughout the paper to motivate and demonstrate application of our work.

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“…By substituting (8) and (11), we have (12) and therefore (13) Similarly, for a totally traversable terrain face, we have , as the braking range does not overlap with any of the two sideslope overturn ranges. Again, by substituting (10) and (11), we have (14) and therefore (15) …”

Section: B Computing Special Ranges For a Terrain Facementioning

confidence: 99%