Abstract
This paper exploits the computing power of widely available multi-core machines to accelerate the trajectory planning by parallelisation of the search algorithm. In particular we investigate the approach that schedules the workload on the cores using the hashing function based on the geographical partitioning of the search space. We use this approach to parallelize the AA* algorithm. In our solution, each partition of the geographical space is represented as an agent. The concept is evaluated on the simulation of real-time trajectory planning of aircraft respecting the environment and real aircraft performance models. We show that the approach decreases the planning time significantly on common multi-core machines preserving the quality of the trajectory provided by AA* algorithm.
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Kopřiva, Š., Šišlák, D., Pěchouček, M. (2012). Towards Parallel Real-Time Trajectory Planning. In: Demazeau, Y., Müller, J., Rodríguez, J., Pérez, J. (eds) Advances on Practical Applications of Agents and Multi-Agent Systems. Advances in Intelligent and Soft Computing, vol 155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28786-2_11
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DOI: https://doi.org/10.1007/978-3-642-28786-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28785-5
Online ISBN: 978-3-642-28786-2
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