Abstract
In this paper, the hierarchical architecture of trajectory planning and control is set up for safe driving with multiple participants without collision, where both levels utilize a time-varying model predictive control methodology. Firstly, a high-level planner formulates an optimal control problem to obtain an optimal trajectory while satisfying different constraints. In particular, due to obstacles’ occupation, several partition functions are generated as linear collision constraints through an optimization process in order to convexify the collision-free region into sub-regions. Secondly, the low-level controller receives the desired trajectory from the high-level planner, and then computes an appropriate steering angle to execute the planned maneuver. Both levels are formulated within the model predictive control(MPC) methodology. The strength of this framework is that it combines different constraints in each optimal control problem. Including a high-level planner ensures the feasibility of safe trajectory planning and the use of a low-level controller ensures tracking stability for safe driving, even under various collision constraints and model mismatch between system plant and predictive process model. Finally, several simulations verified the proposed framework, which was used to compute an optimal, safe trajectory over a set of static or moving obstacles and stabilize the vehicle around it.
Highlights
Numerous accidents have shown that steering control of vehicles is regarded as a solution to prevent collision with a static or moving obstacle
We present a control framework for safe, on-road driving with the presence of multi-obstacles, with the main component being a linear time-varying model predictive control (LTV-Model Predictive Control (MPC)) strategy for both trajectory planning in high-level and tracking control in low-level
The contributions of this paper lie on the following aspects: i) the nonconvex collision avoidance problem turns into a linear model-based, constrained optimization problem; ii) the nonlinearity and nonconvexity of the optimal control problem caused by obstacles can be solved by partitioning the nonconvex feasible region to be convex subregions at each predictive time step; iii) safe driving control is integrated in a hierarchical layer to guarantee position and yaw stabilization along the safe trajectory
Summary
Numerous accidents have shown that steering control of vehicles is regarded as a solution to prevent collision with a static or moving obstacle. In complex scenarios with multiple participants in a highly structured roadway, essential challenges of the optimization-based planning methods lie on how to construct objective function and collision constraints Their use requires the consideration of the nonlinearities in the vehicle dynamics, and the nonconvexity caused by dynamic obstacles. The contributions of this paper lie on the following aspects: i) the nonconvex collision avoidance problem turns into a linear model-based, constrained optimization problem; ii) the nonlinearity and nonconvexity of the optimal control problem caused by obstacles can be solved by partitioning the nonconvex feasible region to be convex subregions at each predictive time step; iii) safe driving control is integrated in a hierarchical layer to guarantee position and yaw stabilization along the safe trajectory. A(1), B(1) are the state and control matrices for the continuous model, these being:
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