| Abstrak/Abstract |
In this paper, a stochastic-based path-planning op-
timization technique called Eikonal-MPPI is presented, which
combines the model predictive path integral (MPPI) and the
eikonal equation to achieve a high successful rate of risk traversal
navigation and maintain computational efficiency. In this ap-
proach, the cost function of the MPPI is preprocessed by the
eikonal equation based on the risk transform function to establish
a velocity field. After the preprocessing, the resulting Eikonal cost
map is injected into the MPPI, gains higher success in producing
optimal and safe trajectories amidst complex environmental
configurations. Compared to other MPPI-based path planners
such as the baseline MPPI, Log-MPPI, Cluster-MPPI, and BiC-
MPPI, the proposed approach reached the highest success rate of
95.5% while keeping the processing time low on BARN dataset,
and low control effort on the elevation risk map. It is predicted
that the proposed approach will be suitable for risk-sensitive
autonomous robotic applications |
