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This paper introduces the planning algorithm Sa-feRRT, which extends the rapidly-exploring random tree (RRT) algorithm by using feedback control and positively invariant sets to guarantee collision-free closed-loop path tracking. The SafeRRT algorithm steers the output of a system from a feasible initial value to a desired goal, while satisfying input constraints and non-convex output constraints...
Motion-prediction algorithms for vehicles often employ historical behavior of a vehicle, rely on the Markov property of the underlying system, and predict the future behavior of the vehicle. However, the Markov property alone may lead to conservative predictions and heavy computational burden. To overcome these drawbacks, this paper develops a method that uses the notion of similarity among vehicle...
We present an algorithm for steering the output of a linear system from a feasible initial condition to a desired target position, while satisfying input constraints and nonconvex output constraints. The system input is generated by a collection of local linear state-feedback controllers. The path-planning algorithm selects the appropriate local controller using a graph search, where the nodes of...
This paper proposes a sampling based planning technique for planning maneuvering paths for semi-autonomous vehicles, where the autonomous driving system may be taking over the driver operation. We use Rapidly-exploring Random Tree Star (RRT*) and propose a two-stage sampling strategy and a particular cost function to adjust RRT* to semi-autonomous driving, where, besides the standard goals for autonomous...
We consider an approach for solving strictly convex quadratic programs (QPs) with general linear inequalities by the alternating direction method of multipliers (ADMM). In particular, we focus on the application of ADMM to the QPs of constrained Model Predictive Control (MPC). After introducing our ADMM iteration, we provide a proof of convergence closely related to the theory of maximal monotone...
This paper is concerned with L2-gain optimal control approach for coordinating the active front steering and differential braking to improve vehicle yaw stability and cornering control. The vehicle dynamics with respect to the tire slip angles is formulated and disturbances are added on the front and rear cornering forces characteristics modelling, for instance, variability on road friction. The mathematical...
This paper proposes a new framework to minimize the fuel consumed in a conventional vehicle over a given driving route by finding the optimal velocity profile. The optimization problem is solved in a remote cloud computing environment and assumes the vehicle route to be known a priori. A spatial domain dynamic programming optimization algorithm is used in this study to find the optimal velocity profile...
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