Search

Aggressive vehicle maneuvers such as those performed by professional racing drivers achieve high agility motion at the edge of handling limits. These aggressive maneuvers can be used to design human-inspired active safety features for next-generation “accident-free” vehicles. We present a motion planning and control design for autonomous aggressive vehicle maneuvers. The motion planner takes advantages of the sparse stable trees and the enhanced rapidly exploring random tree (RRT*) algorithms. The use of the sparsity property helps to reduce the computational cost of the RRT* method by removing non-useful nodes in each iteration and therefore to rapidly converge to the optimal solution. The proposed motion control design allows the vehicle to operate outside the stability region to accomplish a safe, agile maneuver. A safety region is computed to augment the stability region and the motion control is built on a modified nonlinear model predictive control method. We implement the proposed planner and controller and demonstrate the autonomous aggressive maneuvers on a 1/7-scale racing vehicle platform. Comparison with human expert driver and other existing methods is also presented to demonstrate the performance and robustness. Note to Practitioners—Motion planning and control of human driver-inspired aggressive vehicle maneuvers is a challenging task because of high-agility, unstable fast vehicle motions. This paper is motivated by addressing this challenge in autonomous driving technologies. Instead of restricting vehicle motions within a stability region that is taken by existing methods, we augment the conservative stability region to a safety region with guaranteed performance. To improve the computational efficiency of sampling-based motion planners, we take advantage of sparsity and also integration of a nonlinear predictive control method to compute feasible vehicle motion in searching space. The stability of the vehicle motion controller and sub-optimality of the motion planner are analyzed and guaranteed. Using a scaled vehicle testbed, we validate and compare the proposed motion planning and control design with other existing methods and human expert driver. The experimental results demonstrate the superior performance than the other methods and comparable with human expert driving skills.

Global external fields are commonly used to manipulate micro- and nanoparticles in fluid suspension. However, the wireless external actuation has global and coupled influences in the workspace, which limit the robust, independent, and simultaneous control of multiple micro- and nanoparticles. Parametric uncertainty in the particles’ motion model and disturbance make it difficult to steer multiple particles precisely. In this paper, an adaptive tube model predictive control (MPC) scheme is proposed to simultaneously control multiple micro- and nanoparticles in fluid suspensions. The control strategy addresses coupled input actuation from the global electric fields. The unknown mobilities of the particles are continually estimated online, which enables the construction of dynamic tubes of individual particles. The recursive feasibility and the input-to-state stability (ISS) of the scheme are proven. The manipulability of the electrophoresis-based microfluidic manipulation system is quantified and analyzed. The manipulability is affected by the dimensions of the actuating electrodes, the number of particles to be simultaneously and independently controlled, and the configurations of those particles in the microfluidic device. The scalability of the lattice-shaped distributed array of electrodes is discussed. Simulation and experimental results validate the effectiveness of the controller to manipulate multiple particles precisely, independently, and simultaneously. Note to Practitioners—To enable the enormous potential of manufacturing functional micro- and nanodevices, it is crucial to automate the steering and manipulation of multiple micro- and nanoparticles. Wireless actuation is a promising way to position those objects in fluid suspensions. However, the global and coupled influences from the wireless external actuation, the parametric uncertainty in the particles’ motion model, and the external disturbances from the fluid flow limit the robust, independent, and simultaneous control of multiple micro- and nanoparticles. In this paper, we present an adaptive tube model predictive control (MPC) scheme to simultaneously control multiple micro- and nanoparticles in fluid suspensions using electric fields. The scheme estimates the unknown mobilities of the particles, constructs dynamic tubes online, and addresses the coupled actuation from the global electric field. The manipulability of the electrophoresis-based microfluidic manipulation system is introduced, quantified, and analyzed. Those analyses give insights into designing the most effective electrode array and the efficient trajectories to achieve agile particle motions. Experimental results validate the manipulability analyses and the performance of the proposed control strategy.

•We propose a new pretext task between the original view and the augmented view.•We measure the node-level accurate difference between the two views.•We combine various strategies to generate multiple augmented views.