13,086 research outputs found
A path planning and path-following control framework for a general 2-trailer with a car-like tractor
Maneuvering a general 2-trailer with a car-like tractor in backward motion is
a task that requires significant skill to master and is unarguably one of the
most complicated tasks a truck driver has to perform. This paper presents a
path planning and path-following control solution that can be used to
automatically plan and execute difficult parking and obstacle avoidance
maneuvers by combining backward and forward motion. A lattice-based path
planning framework is developed in order to generate kinematically feasible and
collision-free paths and a path-following controller is designed to stabilize
the lateral and angular path-following error states during path execution. To
estimate the vehicle state needed for control, a nonlinear observer is
developed which only utilizes information from sensors that are mounted on the
car-like tractor, making the system independent of additional trailer sensors.
The proposed path planning and path-following control framework is implemented
on a full-scale test vehicle and results from simulations and real-world
experiments are presented.Comment: Preprin
Nonlinear Model Predictive Control for Multi-Micro Aerial Vehicle Robust Collision Avoidance
Multiple multirotor Micro Aerial Vehicles sharing the same airspace require a
reliable and robust collision avoidance technique. In this paper we address the
problem of multi-MAV reactive collision avoidance. A model-based controller is
employed to achieve simultaneously reference trajectory tracking and collision
avoidance. Moreover, we also account for the uncertainty of the state estimator
and the other agents position and velocity uncertainties to achieve a higher
degree of robustness. The proposed approach is decentralized, does not require
collision-free reference trajectory and accounts for the full MAV dynamics. We
validated our approach in simulation and experimentally.Comment: Video available on: https://www.youtube.com/watch?v=Ot76i9p2ZZo&t=40
A Sequential Two-Step Algorithm for Fast Generation of Vehicle Racing Trajectories
The problem of maneuvering a vehicle through a race course in minimum time
requires computation of both longitudinal (brake and throttle) and lateral
(steering wheel) control inputs. Unfortunately, solving the resulting nonlinear
optimal control problem is typically computationally expensive and infeasible
for real-time trajectory planning. This paper presents an iterative algorithm
that divides the path generation task into two sequential subproblems that are
significantly easier to solve. Given an initial path through the race track,
the algorithm runs a forward-backward integration scheme to determine the
minimum-time longitudinal speed profile, subject to tire friction constraints.
With this fixed speed profile, the algorithm updates the vehicle's path by
solving a convex optimization problem that minimizes the resulting path
curvature while staying within track boundaries and obeying affine,
time-varying vehicle dynamics constraints. This two-step process is repeated
iteratively until the predicted lap time no longer improves. While providing no
guarantees of convergence or a globally optimal solution, the approach performs
very well when validated on the Thunderhill Raceway course in Willows, CA. The
predicted lap time converges after four to five iterations, with each iteration
over the full 4.5 km race course requiring only thirty seconds of computation
time on a laptop computer. The resulting trajectory is experimentally driven at
the race circuit with an autonomous Audi TTS test vehicle, and the resulting
lap time and racing line is comparable to both a nonlinear gradient descent
solution and a trajectory recorded from a professional racecar driver. The
experimental results indicate that the proposed method is a viable option for
online trajectory planning in the near future
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing
Within the context of autonomous driving a model-based reinforcement learning
algorithm is proposed for the design of neural network-parameterized
controllers. Classical model-based control methods, which include sampling- and
lattice-based algorithms and model predictive control, suffer from the
trade-off between model complexity and computational burden required for the
online solution of expensive optimization or search problems at every short
sampling time. To circumvent this trade-off, a 2-step procedure is motivated:
first learning of a controller during offline training based on an arbitrarily
complicated mathematical system model, before online fast feedforward
evaluation of the trained controller. The contribution of this paper is the
proposition of a simple gradient-free and model-based algorithm for deep
reinforcement learning using task separation with hill climbing (TSHC). In
particular, (i) simultaneous training on separate deterministic tasks with the
purpose of encoding many motion primitives in a neural network, and (ii) the
employment of maximally sparse rewards in combination with virtual velocity
constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl
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