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An intelligent, heuristic path planner for multiple agent unmanned air systems

An intelligent, heuristic path planner for multiple agent unmanned air systems
An intelligent, heuristic path planner for multiple agent unmanned air systems
In situ observations of atmospheric variables are currently obtained with radiosondes, collecting data along uncontrolled trajectories. As an alternative, we propose an unmanned air system comprising a swarm of unmanned aerial vehicles, released from high altitude weather balloons. Their trajectories are optimised for efficient sampling, with an objective function measuring the space-filling properties of the entire swarm. The dynamics of the aircraft swarm are captured in a number of primitive manoeuvres simulated with JSBSim. A combination of these paths forms a set of moves available to a greedy heuristic algorithm, which determines which of the flyable paths is optimal according to the objective function. At each heuristic optimisation step a decision is made upon the next single move and, once the move is complete, the heuristic repeated, resulting in a stitching together of optimal moves from the flyable set. The proposed path planner comprises of a centralised algorithm, which executes offline, thus, each aircraft executes the heuristic synchronously and the result is a cloud of waypoints to be flown by the swarm. A case study based on orographic flows over South Georgia is used to test the performance of the algorithm in windy environments. The results indicate good performance of the algorithm, even in high, unsteady wind fields.
Crispin, Chris
f7494971-2bcd-4ecc-b8a1-6436ff181df9
Sobester, Andras
096857b0-cad6-45ae-9ae6-e66b8cc5d81b
Crispin, Chris
f7494971-2bcd-4ecc-b8a1-6436ff181df9
Sobester, Andras
096857b0-cad6-45ae-9ae6-e66b8cc5d81b

Crispin, Chris and Sobester, Andras (2015) An intelligent, heuristic path planner for multiple agent unmanned air systems. AIAA SciTech 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Kissimmee, United States. 05 - 09 Jan 2015. 13 pp . (doi:10.2514/6.2015-0361).

Record type: Conference or Workshop Item (Paper)

Abstract

In situ observations of atmospheric variables are currently obtained with radiosondes, collecting data along uncontrolled trajectories. As an alternative, we propose an unmanned air system comprising a swarm of unmanned aerial vehicles, released from high altitude weather balloons. Their trajectories are optimised for efficient sampling, with an objective function measuring the space-filling properties of the entire swarm. The dynamics of the aircraft swarm are captured in a number of primitive manoeuvres simulated with JSBSim. A combination of these paths forms a set of moves available to a greedy heuristic algorithm, which determines which of the flyable paths is optimal according to the objective function. At each heuristic optimisation step a decision is made upon the next single move and, once the move is complete, the heuristic repeated, resulting in a stitching together of optimal moves from the flyable set. The proposed path planner comprises of a centralised algorithm, which executes offline, thus, each aircraft executes the heuristic synchronously and the result is a cloud of waypoints to be flown by the swarm. A case study based on orographic flows over South Georgia is used to test the performance of the algorithm in windy environments. The results indicate good performance of the algorithm, even in high, unsteady wind fields.

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Published date: January 2015
Venue - Dates: AIAA SciTech 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Kissimmee, United States, 2015-01-05 - 2015-01-09
Organisations: Computational Engineering & Design Group

Identifiers

Local EPrints ID: 374045
URI: http://eprints.soton.ac.uk/id/eprint/374045
PURE UUID: 9ba7e5f5-7eba-4c7d-a2cb-d3e1b0a47c24
ORCID for Andras Sobester: ORCID iD orcid.org/0000-0002-8997-4375

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Date deposited: 09 Feb 2015 14:58
Last modified: 15 Mar 2024 03:13

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Contributors

Author: Chris Crispin
Author: Andras Sobester ORCID iD

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