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.

Precise manipulation of micro-and nano-objects through visual feedback is challenging because of the difficulty of observing their motion along the line-of-sight of microscopes. This paper presents an efficient learning-based auto-focus (AF) and visual posture estimation scheme for tracking the three-dimensional (3D) poses of multiple moving micro-and nanowires in fluid suspensions under bright-field microscopes. The proposed AF and 3D pose estimation methods integrate convolutional neural networks (CNNs) to precisely identify the focal distances and inclination angles of multiple moving wires through a single region-of-interest (ROI) image for each wire. Furthermore, we demonstrate the versatility of the proposed AF method by adapting it for wires of other materials through transfer learning (TF), using a limited dataset. Extensive experimental results validate the high accuracy and efficiency of AF and 3D pose estimation compared to traditional methods. This work lays the foundation for the automated control of micro-and nano-objects in 3D microfluidic environments.Note to Practitioners—Autonomous manipulation of multiple micro-and nanoscale objects is of major interest for various research applications. However, precise manipulation of micro-and nanowires through visual feedback is challenging because of the difficulty in focusing microscopes on their 3D pose or orientation to the x, y, and z axes as they move in the depth direction. Traditionally, achieving AF involves using one or more image metrics to evaluate image quality by performing a series of mechanical movements to locate the peak sharpness using optimization methods. In contrast, this paper proposes a learning-based approach that uses CNNs to estimate the positions of the focal planes and identify the 3D positions and orientations of multiple moving micro-and nanowires from a single ROI image. Extensive experiments demonstrate that the learning-based methods exhibit higher stability, extended duration, and faster converging speed of tracking and 3D pose estimation compared to traditional methods. This work facilitates researchers’ ability to observe and manipulate micro-and nano-objects under microscopes in 3D microfluidic environments.

We present a simultaneous sensor-based inspection and footprint coverage (SIFC) planning and control design with applications to autonomous robotic crack mapping and filling. The main challenge of the SIFC problem lies in the coupling of complete sensing (for mapping) and robotic footprint (for filling) coverage tasks. Initially, we assume known target information (e.g., cracks) and employ classic cell decomposition methods to achieve complete sensing coverage of the workspace and complete robotic footprint coverage using the least-cost route. Subsequently, we generalize the algorithm to handle unknown target information, allowing the robot to scan and incrementally construct the target map online while conducting robotic footprint coverage. The online polynomial-time SIFC planning algorithm minimizes the total robot traveling distance, guarantees complete sensing coverage of the entire workspace, and achieves near-optimal robotic footprint coverage, as demonstrated through empirical experiments. For the demonstrated application, we design coordinated nozzle motion control with the planned robot trajectory to efficiently fill all cracks within the robot's footprint. Experimental results illustrate the algorithm's design, performance, and comparisons. The SIFC algorithm offers a high-efficiency motion planning solution for various robotic applications requiring simultaneous sensing and actuation coverage.

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.

Online manipulation of multiple micro- and nanoscale agents is of major interest for various research applications. Among the biggest limitations of wireless external actuation are its global and coupled influences in the workspace, which limit the robust manipulation of multiple agents independently and simultaneously. In this paper, we propose novel motion planning algorithms,$\tt Bi$-$\tt iSST$and$\tt Ref$-$\tt iSST$, to quickly generate time-optimal trajectories for multiple agents sharing global external fields. Both algorithms are extended by the stable sparse rapidly-exploring random tree kinodynamic motion planning algorithm. The$\tt Bi$-$\tt iSST$uses a bidirectional approach to speed up the searching process. A novel connection process is proposed to connect the two trees efficiently by applying an optimization procedure. The$\tt Ref$-$\tt iSST$uses the workspace information to quickly generate global-routing trajectories as references, then guides the search process more effectively by getting more accurate heuristics according to the reference global-routing trajectories. A transition matrix similar to that in Markov Decision Processes is used to form the reference trajectory. Compared with the state-of-the-art$\tt iSST$algorithm, the proposed algorithms quickly update feasible solutions and converge to a near-optimal, minimum-time solution to increase the efficiency of the simultaneous manipulation of multiple micro agents using global external fields. Extensive analysis and physical experiments are presented to confirm the effectiveness and the performance of the motion planning algorithms.Note to Practitioners—Autonomous manipulation of multiple micro- and nanoscale agents is of major interest for various research applications. Wireless actuation is a promising way to position those objects. The commonly used non-contact actuation techniques include magnetic actuation, electrical field actuation, optical tweezers, and actuated flows, etc. Among the biggest limitations of wireless external actuation are its global and coupled influences in the workspace, which limit the capability to robustly manipulate multiple agents independently and simultaneously. In this paper, we propose novel bidirectional informed sampling-based motion planning algorithms to quickly generate time-optimal trajectories for multiple agents sharing global external fields. Novel heuristics and informed reference trajectory are used to guide the search for manipulating multiple agents under global fields. Numerical simulations and physical experiments are presented to demonstrate the performance of the motion planning design. The proposed algorithm guarantees anytime performance and quickly converges to a near-optimal minimum time solution for multiple agents. Although we focus on the electric-field actuated multiple-micro-agent system, the proposed motion planning strategies are not limited to the actuation and can be generalized to other field-based applications, in which the actuation among a group of multiple agents are coupled or intertwined.

A simultaneous robotic footprint and sensor coverage planning scheme is proposed to efficiently detect all the unknown targets with range sensors and cover the targets with the robot's footprint in a structured environment. The proposed online Sensor-based Complete Coverage (online SCC) planning minimizes the total traveling distance of the robot, guarantees the complete sensor coverage of the whole free space, and achieves near-optimal footprint coverage of all the targets. The planning strategy is applied to a crack-filling robotic prototype to detect and fill all the unknown cracks on ground surfaces. Simulation and experimental results are presented that confirm the efficiency and effectiveness of the proposed online planning algorithm.

To fully take advantage of the enormous potential of functional nanodevices, it is crucial to automate their manufacture with efficient steering and manipulation of multiple nanowires with controlled orientations to specific spatial locations. In this paper, we present motion planning and control algorithms for simultaneously steering multiple nanowires in liquid suspension. The motion planning and control is designed for a microfluidic device that is actuated by a simple generic set of electrodes. We first present a motion control algorithm to simultaneously steer multiple nanowires along different desired trajectories under controlled electrophoretic forces. A two-stage motion planning algorithm is then presented to generate the desired trajectory for each individual nanowire. Both numerical simulation and experimental results are presented to demonstrate the performance of the motion planning and control design using electric fields to simultaneously steer and manipulate multiple nanowires.

Automated manipulation of nanowires and nanotubes would enable the scalable manufacturing of nanodevices for a variety of applications, including nanoelectronics and biological applications. In this paper, we present an electric-field-based method for motion control, planning, and manipulation of nanowires in liquid suspension with a simple, generic set of electrodes. We first present a dynamic model and a vision-based motion control of the nanowire motion in dilute suspension with a set of$N\times N$controllable electrodes. Since the motion planning of a nanowire from one position to the target location is NP-hard, two heuristic algorithms are presented to generate near-optimal motion trajectories. We compare the heuristic motion planning algorithms with other existing algorithms such as the rapidly exploring random tree (RRT) and$A^{\ast}$algorithms. The comparisons show that the proposed heuristic algorithms obtain near-optimal minimum time trajectories. Finally, we demonstrate a single, integrated process to position, orient, and deposit multiple nanowires onto the substrate. Extensive experimental and numerical results are presented to confirm the motion control and planning algorithms.

We report the development of a human whole-body pose estimation scheme with application to rider-bicycle interactions. The estimation scheme is built on the fusion of measurements of a monocular camera on the bicycle and a set of small wearable gyroscopes attached to the rider's upper- and lower-limb and the trunk. A single feature point is collocated with each wearable gyroscope and also on the segment link where the gyroscope is not attached. An extended Kalman filter is designed to fuse the vision-inertial measurements to obtain accurate whole-body poses. The estimation design also incorporates a set of constraints from human anatomy and the physical rider-bicycle interactions. We demonstrate and compare the performance of the estimation design through multiple subjects riding experiments.