An Optimized Trajectory Planner and Motion Controller Framework for Autonomous Driving in Unstructured Environments

Abstract

This paper proposes an optimized trajectory planner and motion planner framework, which aim to deal with obstacle avoidance along a reference road for autonomous driving in unstructured environments. The trajectory planning problem is decomposed into lateral and longitudinal planning sub-tasks along the reference road. First, a vehicle kinematic model with road coordinates is established to describe the lateral movement of the vehicle. Then, nonlinear optimization based on a vehicle kinematic model in the space domain is employed to smooth the reference road. Second, a multilayered search algorithm is applied in the lateral-space domain to deal with obstacles and find a suitable path boundary. Then, the optimized path planner calculates the optimal path by considering the distance to the reference road and the curvature constraints. Furthermore, the optimized speed planner takes into account the speed boundary in the space domain and the constraints on vehicle acceleration. The optimal speed profile is obtained by using a numerical optimization method. Furthermore, a motion controller based on a kinematic error model is proposed to follow the desired trajectory. Finally, the experimental results show the effectiveness of the proposed trajectory planner and motion controller framework in handling typical scenarios and avoiding obstacles safely and smoothly on the reference road and in unstructured environments.

Computationally Efficient Nonlinear Model Predictive Control Using the L1 Cost-Function

“…MPC algorithms depend heavily on precise measurements of process variables that are provided by sensors. In some versions of automatic control systems, e.g., those described in [ 12 , 14 , 15 ], it is stressed that all necessary variables must be measured because otherwise, a significant loss in control performance is unavoidable. If the measurements are not available, the typical approach is to perform on-line estimation using Kalman or Extended Kalman filters [ 16 ].…”

Rollover Prevention Control for Autonomous Electric Road Sweeper

“…To calculate the longitudinal and lateral tire forces in Equations ( 1), (3), and (7), the Dugoff tire model is adopted in this paper [16]. From the Dugoff model, the longitudinal and lateral tire forces are calculated as Equation (10). In Equation (10), the quantities S i and f (S i ) are calculated as Equation (11).…”

“…From the Dugoff model, the longitudinal and lateral tire forces are calculated as Equation (10). In Equation (10), the quantities S i and f (S i ) are calculated as Equation (11). In Equations (10) and (11), C x and C y are the longitudinal and lateral cornering stiffness of the tire, respectively.…”

“…To cope with this problem, the diesel engine in road sweepers was replaced with an electric motor and a battery. The second is autonomous driving, which we developed in order to maximize utilization in dedicated areas such as airports or public parks without a human driver [8][9][10]. For example, most passenger vehicles are utilized within 20% of their total lifecycle.…”

“…In view of control, path tracking and speed controls are the most important aspects [8]. Path tracking control means that the driver model, which generates the steering angle command, needs to follow a given path [9,10,[21][22][23]. The speed control ensures that the vehicle follows a given speed profile by means of engine throttle or motor torque commands [8,9].…”