Abstract
This paper deals with the obstacle avoidance problem for an autonomous vehicle using NMPC (Nonlinear Model Predictive Control) while following an a priori given path. The repulsive potential of the operating space is constructed from the bounded convex regions describing the static obstacles for collision-free navigation. The contribution lies in using the Hausdorff distances among the obstacles and the agent in order to activate/inactivate the repulsive potential field. This potential field component is introduced in a NMPC framework to penalize collision. This proposal shows good results in simulations and comparisons with our previous work.
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Notes
- 1.
I.e., the computational complexity increases exponentially with the number of binary variables used in the problem formulation.
- 2.
This vessel was identified and developed in the Marine Cybernetics Laboratory at Norwegian University of Science and Technology.
- 3.
I.e., our previous work which uses Chebyshev center instead of Hausdorff distance.
- 4.
For simplicity, the Coriolis matrix is neglected.
- 5.
Safe distance, Ds is equal to the maximum length of a vessel’s hull.
- 6.
The prediction horizon is chosen enough large to guarantee obstacle avoidance but not too large because of the computational burden of the solver.
- 7.
It’s worth noting that the ship is very close the fixed obstacle 1 but does not collide due to the repulsive field.
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Tran, NQH., Prodan, I., Phan, NDM. (2021). Collision-Free Path Following of an Autonomous Vehicle Using NMPC. In: Sattler, KU., Nguyen, D.C., Vu, N.P., Long, B.T., Puta, H. (eds) Advances in Engineering Research and Application. ICERA 2020. Lecture Notes in Networks and Systems, vol 178. Springer, Cham. https://doi.org/10.1007/978-3-030-64719-3_27
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