article

Coevolving and cooperating path planner for multiple unmanned air vehicles

Authors:

Changwen Zheng,

Chengping Zhou,

Lei LiAuthors Info & Claims

Engineering Applications of Artificial Intelligence, Volume 17, Issue 8

Pages 887 - 896

https://doi.org/10.1016/j.engappai.2004.08.016

Published: 01 December 2004 Publication History

Abstract

In this paper, the coordinated path planning problem for multiple unmanned air vehicles is studied with the proposal of a novel coevolving and cooperating path planner. In the new planner, potential paths of each vehicle form their own sub-population, and evolve only in their own sub-population, while the interaction among all sub-problems is reflected by the definition of fitness function. Meanwhile, the individual candidates are evaluated with respect to the workspace so that the computation of the configuration space is avoided. By using a problem-specific representation of candidate solutions and genetic operators, our algorithm can take into account different kinds of mission constraints and generate solutions in real time.

References

[1]

Asseo, S.J., 1988. Terrain following/terrain avoidance path optimization using the method of steepest descent. In: IEEE Proceedings of National Aerospace and Electronics Conference, Dayton, OH, pp. 1128-1136.

[2]

Coordinated target assignment and intercept for unmanned air vehicles. IEEE Transactions on Robotics and Automation. v18 i6. 911-922.

[3]

Bortoff, S., 2000. Path planning for UAVs. In: Proceedings of American Control Conference, Chicago, pp. 364-368.

[4]

Chandler, P., Rasmussen, S., Pachter, M., 2000. UAV cooperative path planning. In: Proceedings of Guidance, Navigation and Control Conference, AIAA, Denver, CO.

[5]

Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA.

[6]

Goldman, J., 1994. Path planning problems and solutions. In: IEEE Proceedings of National Aerospace and Electronics Conference, Dayton, OH, pp. 105-108.

[7]

Planning multiple paths with evolutionary speciation. IEEE Transactions on Evolutionary Computation. v5 i3. 169-192.

[8]

McLain, T.W., Beard, R.W., 2000. Cooperative rendezvous of multiple unmanned air vehicles. In: Proceedings of Guidance, Navigation and Control Conference, AIAA, Denver, CO.

[9]

McLain, T.W., Chandler, P., Pachter, M., 2000. A decomposition strategy for optimal coordination of unmanned air vehicles. In: Proceedings of American Control Conference, Chicago, pp. 369-373.

[10]

McLain, T.W., Chandler, P., Rasmussen, S., Pachter, M., 2001. Cooperative control of UAV rendezvous. In: Proceedings of American Control Conference, pp. 2309-2314.

[11]

Genetic Algorithms+Data Structures=Evolution Programs. third ed. Springer, Berlin, Germany.

[12]

Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation. v4 i1. 1-32.

[13]

Forming neural networks through efficient and adaptive coevolution. Evolutionary Computation. v5 i4. 373-399.

[14]

Potter, M.A., De Jong, K.A., 1994. A cooperative coevolutionary approach to function optimization, In: Proceedings of Third Conference on Parallel Problem Solving from Nature, Berlin, Germany, pp. 249-257.

[15]

Coevolutionary genetic algorithms for nonconvex nonlinear programming problems: revised genocop III. . Cybernet. Systems. v29 i8. 885-899.

[16]

Robust algorithm for real-time route planning. IEEE Transactions on Aerospace and Electronic System. v36 i3. 869-878.

Cited By

Mathias HFoley SCoello Coello C(2020)A parallel two-stage genetic algorithm for route planningProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3398116(1739-1746)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3377929.3398116
Mathias HFoley SFurlani T(2019)Improving a Genetic Algorithm for Route Planning Using Parallelism with Speculative ExecutionPractice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)10.1145/3332186.3333251(1-5)Online publication date: 28-Jul-2019
https://dl.acm.org/doi/10.1145/3332186.3333251
Ragusa VMathias HKazakova VWu ABosman P(2017)Enhanced genetic path planning for autonomous flightProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071293(1208-1215)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071293
Show More Cited By

Coevolving and cooperating path planner for multiple unmanned air vehicles
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

Recommendations

Evolutionary Route Planner for Unmanned Air Vehicles

Based on evolutionary computation, a novel real-time route planner for unmanned air vehicles is presented. In the evolutionary route planner, the individual candidates are evaluated with respect to the workspace so that the computation of the ...
Cooperative path planning optimization for multiple UAVs with communication constraints
Abstract
Path planning is a complicated optimization problem that is crucial for the safe flight of unmanned aerial vehicles (UAVs). Especially in the scenarios involving multiple UAVs, this problem is highly challenging due to the constraints of complex ...
Highlights
- We put forward a cooperative path planning model for multiple UAVs.
- The communication constraints are taken into account in the presented path planning model.
- We propose an improved particle swarm optimization algorithm denoted as ...
Multi-objective Path Planning of Multiple Unmanned Air Vehicles Using the CCMO Algorithm
Advances in Swarm Intelligence
Abstract
The key to efficiently executing tasks with multiple unmanned air vehicles(UAVs) is effective path planning. The goal of path planning is to plan a path which is shorter, smoother, and has minimal fluctuations while avoiding collisions. In order ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Engineering Applications of Artificial Intelligence

Engineering Applications of Artificial Intelligence Volume 17, Issue 8

December, 2004

88 pages

ISSN:0952-1976

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2004.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 December 2004

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Mathias HFoley SCoello Coello C(2020)A parallel two-stage genetic algorithm for route planningProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3398116(1739-1746)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3377929.3398116
Mathias HFoley SFurlani T(2019)Improving a Genetic Algorithm for Route Planning Using Parallelism with Speculative ExecutionPractice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)10.1145/3332186.3333251(1-5)Online publication date: 28-Jul-2019
https://dl.acm.org/doi/10.1145/3332186.3333251
Ragusa VMathias HKazakova VWu ABosman P(2017)Enhanced genetic path planning for autonomous flightProceedings of the Genetic and Evolutionary Computation Conference10.1145/3071178.3071293(1208-1215)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1145/3071178.3071293
Belkhouche F(2017)Reactive optimal UAV motion planning in a dynamic worldRobotics and Autonomous Systems10.1016/j.robot.2017.07.00696:C(114-123)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1016/j.robot.2017.07.006
Zambom ACollazos JDias R(2016)Constrained optimization with stochastic feasibility regions applied to vehicle path planningJournal of Global Optimization10.1007/s10898-015-0353-964:4(803-823)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1007/s10898-015-0353-9
David Mathias HRagusa V(undefined)An empirical study of crossover and mass extinction in a genetic algorithm for pathfinding in a continuous environment2016 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2016.7744312(4111-4118)
https://dl.acm.org/doi/10.1109/CEC.2016.7744312

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents