Resolution-adaptive risk-aware trajectory planning for surface vehicles operating in congested civilian traffic View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2016-10

AUTHORS

Brual C. Shah, Petr Švec, Ivan R. Bertaska, Armando J. Sinisterra, Wilhelm Klinger, Karl von Ellenrieder, Manhar Dhanak, Satyandra K. Gupta

ABSTRACT

The growing variety and complexity of marine research and application oriented tasks requires unmanned surface vehicles (USVs) to operate fully autonomously over long time horizons even in environments with significant civilian traffic. In order to address this challenge, we have developed a lattice-based 5D trajectory planner for USVs. The planner estimates collision risk and reasons about the availability of contingency maneuvers to counteract unpredictable behaviors of civilian vessels. The planner also incorporates avoidance behaviors of the vessels into the search for a dynamically feasible trajectory to minimize collision risk. In order to be computationally efficient, it dynamically scales the control action primitives of a trajectory based on the distribution and concentration of civilian vessels while preserving the dynamical feasibility of the primitives. We present a novel congestion metric to compare the complexity of different scenarios when evaluating the performance of the planner. Our results demonstrate that the basic version of the risk and contingency-aware planner (RCAP) significantly decreases the number of collisions compared to a baseline, velocity obstacles based planner, especially in complex scenarios with a high number of civilian vessels. The adaptive version of the planner (A-RCAP) improves the computational performance of RCAP by 500 %. This leads to a high replanning rate, which allows shorter traversal distances and smaller arrival times, while ensuring comparable incidence of collisions. More... »

PAGES

1139-1163

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10514-015-9529-x

DOI

http://dx.doi.org/10.1007/s10514-015-9529-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1014611619


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