6,413 research outputs found
Model predictive trajectory optimization and tracking for on-road autonomous vehicles
Motion planning for autonomous vehicles requires spatio-temporal motion plans
(i.e. state trajectories) to account for dynamic obstacles. This requires a
trajectory tracking control process which faithfully tracks planned
trajectories. In this paper, a control scheme is presented which first
optimizes a planned trajectory and then tracks the optimized trajectory using a
feedback-feedforward controller. The feedforward element is calculated in a
model predictive manner with a cost function focusing on driving performance.
Stability of the error dynamic is then guaranteed by the design of the
feedback-feedforward controller. The tracking performance of the control system
is tested in a realistic simulated scenario where the control system must track
an evasive lateral maneuver. The proposed controller performs well in
simulation and can be easily adapted to different dynamic vehicle models. The
uniqueness of the solution to the control synthesis eliminates any
nondeterminism that could arise with switching between numerical solvers for
the underlying mathematical program.Comment: 6 pages, 7 figure
A Distributed Model Predictive Control Framework for Road-Following Formation Control of Car-like Vehicles (Extended Version)
This work presents a novel framework for the formation control of multiple
autonomous ground vehicles in an on-road environment. Unique challenges of this
problem lie in 1) the design of collision avoidance strategies with obstacles
and with other vehicles in a highly structured environment, 2) dynamic
reconfiguration of the formation to handle different task specifications. In
this paper, we design a local MPC-based tracking controller for each individual
vehicle to follow a reference trajectory while satisfying various constraints
(kinematics and dynamics, collision avoidance, \textit{etc.}). The reference
trajectory of a vehicle is computed from its leader's trajectory, based on a
pre-defined formation tree. We use logic rules to organize the collision
avoidance behaviors of member vehicles. Moreover, we propose a methodology to
safely reconfigure the formation on-the-fly. The proposed framework has been
validated using high-fidelity simulations.Comment: Extended version of the conference paper submission on ICARCV'1
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