Computational load reduction of the agent guidance problem using Mixed Integer Programming

This paper employs a solution to the agent-guidance problem in an environment with obstacles, whose avoidance techniques have been extensively used in the last years. There is still a gap between the solution times required to obtain a trajectory and those demanded by real world applications. These...

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Bibliographic Details
Published in:PloS one 2020-06, Vol.15 (6), p.e0233441-e0233441
Main Authors: Battagello, Vinícius Antonio, Soma, Nei Yoshihiro, Afonso, Rubens Junqueira Magalhees
Format: Article
Language:English
Subjects:
Computer and Information Sciences
Computer applications
Computer programming
Drivers
Engineering and Technology
Engineering research
Integer programming
Linear programming
Lower bounds
Methods
Mixed integer
Obstacle avoidance
Pattern recognition
Physical Sciences
Research and Analysis Methods
Time
Traffic accidents
Variables
Velocity
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Summary:This paper employs a solution to the agent-guidance problem in an environment with obstacles, whose avoidance techniques have been extensively used in the last years. There is still a gap between the solution times required to obtain a trajectory and those demanded by real world applications. These usually face a tradeoff between the limited on-board processing performance and the high volume of computing operations demanded by those real-time applications. In this paper we propose a deferred decision-based technique that produces clusters used for obstacle avoidance as the agent moves in the environment, like a driver that, at night, enlightens the road ahead as her/his car moves along a highway. By considering the spatial and temporal relevance of each obstacle throughout the planning process and pruning areas that belong to the constrained domain, one may relieve the inherent computational burden of avoidance. This strategy reduces the number of operations required and increases it on demand, since a computationally heavier problem is tackled only if the simpler ones are not feasible. It consists in an improvement based solely on problem modeling, which, by example, may offer processing times in the same order of magnitude than the lower-bound given by the relaxed form of the problem.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0233441