Planning Strategic Paths Through Variable Terrain Data

Mitchell, Joseph S. B.; Keirsey, D.

doi:10.1117/12.943182

Cited by 26 publications

(17 citation statements)

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“…These studies have considered both deterministic and stochastic situations and have derived methods based on, for example, potential fields [4], graph search methods [5] and evolutionary algorithms [6], [7], [8].…”

Section: Related Workmentioning

confidence: 99%

Minimum-Energy Flight Paths for UAVs Using Mesoscale Wind Forecasts and Approximate Dynamic Programming

Nachmani¹

2007

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to ABSTRACTFuel or battery consumption of unmanned aerial vehicles (UAVs) can be improved by utilizing or avoiding air currents. This thesis adopts a network modeling approach to formulate the problem of finding minimum energy flight paths. The relevant airspace is divided into small regions using a grid of nodes, inter-connected by arcs. A function, representing energy cost, is defined on every arc in terms of the solution of a constrained nonlinear program for the optimal local airspeed to fly in a given wind field. Then, shortest-path models are implemented on the network to find the optimal paths from an origin to a destination. Five models are studied and they correspond to cases of pre-planning of flight routes and dynamic updating of routes during the course of the flight. These models use three-dimensional grids of forecasted wind currents, produced by the Naval Research Laboratory's Coupled Ocean-Atmosphere Mesoscale Prediction System (COAMPS) with horizontal resolution of 1 km. One of the shortest-path models, a stochastic-dynamic model, assumes real-time measurement capabilities of the wind velocity in the vicinity of the UAV, through its GPS-INS system, and provides updated waypoints to follow after every measurement. For each model, the energy costs of the shortest-path solutions for 1000 randomized missions over a Nevada test site are simulated and compared to the energy costs of straight-line paths. For a 100 kg UAV, the dynamic model produces an average reduction of 15.1% in the energy consumption along 40 km long round trips, and an average reduction of 30.1% under windy conditions with average wind speeds larger than 15 m/s. A stochastic-dynamic model for maximum duration, solved using a heuristic algorithm, achieves an average increase of 32.2% in the flight duration for a 100 kg UAV. NUMBER OF PAGES 8514. SUBJECT TERMS Optimization, Unmanned Aerial Vehicles, Stochastic-Dynamic Shortest Path, Weather Forecasts, Aerodynamics, Correlation Structure. PRICE CODE SECURITY CLASSIFICATION OF REPORT Unclassified SECURITY CLASSIFICATION OF THIS PAGE Unclassified SECURITY CLASSIFICATION OF ABSTRACT Unclassified LIMITATION OF ABSTRACT MINIMUM-ENERGY FLIGHT PATHS FOR UAVS USING MESOSCALE WIND FORECASTS AND APPROXIMATE DYNAMIC PROGRAMMINGGil Nachmani Captain, Israel Defense Forces B.Sc., Hebrew University of Jerusalem, 2001 Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN OPERATIONS RESEARCHfrom the NAVAL POSTGRADUATE SCHOOL December 2007Author: Gil Nachmani

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Section: Related Workmentioning

confidence: 99%

Minimum-Energy Flight Paths for UAVs Using Mesoscale Wind Forecasts and Approximate Dynamic Programming

Nachmani¹

2007

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“…Common algorithms for autonomous path planning are methods such as potential fields [3], graph search methods [4,5], and Evolutionary Algorithmic (EA) techniques [6,7]. Of these the EA-based methods seem well suited for dynamic environments as they can deal with uncertainty in location, however they tend to be somewhat inefficient.…”

Section: Navigation Systemmentioning

confidence: 99%

Navigation in REVERIE's virtual environments

Rivera

Fuijk

Izquierdo

2015

2015 IEEE Virtual Reality (VR)

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This work presents a novel navigation system for social collaborative virtual environments populated with multiple characters. The navigation system ensures collision free movement of avatars and agents. It supports direct user manipulation, automated path planning, positioning to get seated, and follow-me behaviour for groups. In follow-me mode, the socially aware system manages the mise en place of individuals within a group. A use case centred around on an educational virtual trip to the European Parliament created for the REVERIE FP7 project, also serves as an example to bring forward aspects of such navigational requirements.

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“…(Rowe and Ross 1990) discussed another way to find optimal paths for the problem we are considering, apportioning the terrain into a uniform grid of elevation values and doing a "wavefront propagation" of costs in an ever-expanding area about a start point. But there are many disadvantages to this approach (Mitchell and Keirsey 1984;Rowe and Richbourg 1990). Section 8 will show that such an approach gives ugly solution paths hard to follow in the real world, and requires significantly more computational effort and storage than our methods in this paper.…”

Section: Introductionmentioning

confidence: 99%

Near-Minimum-Energy Paths on a Vertical-Axis Cone With Anisotropic Friction and Gravity Effects

Rowe

Kanayama

1994

The International Journal of Robotics Research

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We determine near-optimal paths with respect to work against friction and gravity on the surface of a vertical-axis ideal cone, assuming a moving agent of negligible size, friction proportional to the normal force, and power-maximum and stability limitations on the agent. This can provide good paths across hilly terrain for mobile robots. Our previous work required difficult-to-obtain polyhedral terrain models; cone surface patches permit easier and better models. We prove that our near-optimal paths on a vertical-axis cone surface are not much more complex than on polyhedra: There are qualitatively 22 kinds of path behavior (as opposed to four on a polyhedral face), and the area of the surface optimally reachable from a fixed start point by a given qualitative behavior has mathematically simple boundaries (only line segments, circle arcs, and arcs of logarithmic spirals in azimuth projection). We examine the possible cases based on agent characteristics and cone steepness, and provide "behavior maps" for quickly finding near-optimal paths between any two points on the cone surface. Our models incorporate discontinuous effects with respect to traversal heading. Comparisons with a program using uniform-grid path planning show our methods run considerably faster in less space, and our paths are much simpler to describe and easier to follow in the real world.

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Planning Strategic Paths Through Variable Terrain Data

Cited by 26 publications

References 0 publications

Minimum-Energy Flight Paths for UAVs Using Mesoscale Wind Forecasts and Approximate Dynamic Programming

Minimum-Energy Flight Paths for UAVs Using Mesoscale Wind Forecasts and Approximate Dynamic Programming

Navigation in REVERIE's virtual environments

Near-Minimum-Energy Paths on a Vertical-Axis Cone With Anisotropic Friction and Gravity Effects

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