Trajectory Generation From Paths for Autonomous Ground Vehicles

Path-to-trajectory conversion problem for car-like autonomous ground vehicles has been studied in various ways. It is challenging to generate a trajectory which is dynamically feasible for the vehicle and comfortable for the passengers. An important practical concern of low computational costs makes the problem even more difficult. In this work, a path-to-trajectory converter is developed using a novel receding-horizon type suboptimal algorithm. By transforming the dynamic constraints to a longitudinal velocity limit function in the velocity-acceleration phase plane, a time-sub-optimal trajectory satisfying the dynamic constraints and the initial boundary condition is generated by computing the maximum constant acceleration in the down-range horizon. The portion of the trajectory approaching the end of the path is generated in reverse time. As illustrated by some simulation results and validation on a full-scale Kia Soul EV, the proposed path-to-trajectory conversion algorithm is able to account for dynamic constraints of the vehicle and guarantees passenger comfort.

Optimal Data-Driven Modeling-Free Differential-Inversion-Based Iterative Control: A Wafer Stage Example

In this paper, an optimal data-driven modeling-free differential-inversion-based iterative control (OMFDIIC) method is proposed for both high performance and robustness in the presence of random disturbances. Achieving high accuracy and fast convergence is challenging as the system dynamics behaviors vary due to the external uncertainties and the system bandwidth is limited. The aim of the proposed method is to compensate for the dynamics effect without modeling process and achieve both high accuracy and robust convergence, by extending the existed modeling-free differential-inversion-based iterative control (MFDIIC) method through a frequency- and iteration-dependent gain. The convergence of the OMFDIIC method is analyzed with random noise/disturbances considered. The developed method is applied to a wafer stage, and shows a significant improvement in the performance.

Two-Stage Robust Tracking Controller for Linear Systems With Known Uncertainty Using Filtered Basis Functions

There is growing interest in the use of the filtered basis functions (FBF) approach to track linear systems, especially nonminimum phase (NMP) plants, because of the distinct advantages it presents as compared to other popular methods in the literature. The FBF approach expresses the control input to the plant as a linear combination of basis functions. The basis functions are forward filtered through the plant dynamics and the coefficients of the linear combination are selected such that the tracking error is minimized. This paper proposes a two-stage robust filtered basis functions approach for tracking control of linear systems in the presence of known uncertainty. In the first stage, the nominal model for filtering the basis functions is selected such that a Frobenius norm metric which considers the known uncertainty is minimized. In the second stage, an optimal set of basis functions is selected such that the effect of uncertainty is minimized for the nominal model selected in the first stage. Experiments on a 3D printer, demonstrate up to 7 times improvement in tracking performance using the proposed method as compared to the standard FBF approach.

Formation Control of Non-Holonomic Mobile Robots Moving on Slippery Surfaces

In this work, we analyze the decentralized formation control problem for a class of multi-robotic systems evolving on slippery surfaces. Grounded on experimental data of robots moving on a gravel surface inducing slippery, we show that a deterministic model cannot capture the uncertainties resulting from the kinematics of the robots while, instead, a model incorporating stochastic noise is capable of emulating such perturbations on wheel driving speed and turn rate. To account for these uncertainties, we consider a second order non-holonomic unicycle model to capture the full dynamics of individual vehicles where both actuation force and torque are subject to stochastic disturbances. Upon reducing the input-output dynamics of individual robot to a stochastic double integrator, we investigate the effects of these perturbations on the control input using concepts from stochastic stability theory and through numerical simulations. We demonstrated the applicability of the proposed scheme for formation control notably by providing sufficient conditions for exponential mean square convergence and we numerically determined the range of noise intensities for which team of robots can achieve formation stabilization. The promising findings from this work are expected to aid the design of robust control schemes for formation control of non-holonomic robots on off-road or un-paved surfaces.

Effect of Short-Term Weather Predictions on Model Predictive Trajectory Tracking Performance of Unmanned Surface Vessels

For mobile robots, such as Autonomous Surface Vessels (ASVs), limiting error from a target trajectory is necessary for effective and safe operation. This can be difficult when subjected to environmental disturbances like wind, waves, and currents. This work compares the tracking performance of an ASV using a Model Predictive Controller that includes a model of these disturbances. Two disturbance models are compared. One prediction model assumes the current disturbance measurements are constant over the entire prediction horizon. The other uses a statistical model of the disturbances over the prediction horizon. The Model Predictive Controller performance is also compared to a PI-controlled system under the same disturbance conditions. Including a disturbance model in the prediction of the dynamics decreases the trajectory tracking error over the entire disturbance spectrum, especially for longer horizon lengths.

Fuzzy Logic Controller for Force Feedback Control of Quadcopter via Tether

Tethered drone systems can be used to perform long-endurance tasks such as area surveillance and relay stations for wireless communication. However, all the existing systems use tethers only for data and power transmission from a stationary point on the ground. This work presents a control strategy that enables a quadcopter to follow a moving tether anchor. A force feedback controller is implemented using Fuzzy Logic. Using force-based strategy provides effective compliance between the tether’s anchor and the drone. The drone can thus be controlled by mere physical movement/manipulation of tether. This enhances the safety of current tethered drone systems and simplifies the flying of drones. Fuzzy Logic provides an intuitive edge to the control of such systems and allows handling noise in force sensors. Extensive simulation results are presented in this paper showing the effectiveness of the proposed control scheme.

Effect of Electromechanical Coupling on Locally Resonant Metastructures for Simultaneous Energy Harvesting and Vibration Attenuation Applications

The study of simultaneous energy harvesting and vibration attenuation has recently been the focus in many acoustic meta-materials investigations. The studies have reported the possibility of harvesting electric power using electromechanical coupling; however, the effect of the electromechanical resonator on the obtained bandgap’s boundaries has not been explored yet. In this paper, we investigate metamaterial coupled to electromechanical resonators to demonstrate the effect of electromechanical coupling on the wave propagation analytically and experimentally. The electromechanical resonator is shunted to an external load resistor to harvest energy. We derive the analytical dispersion curve of the system and show the band structure for different load resistors and electromechanical coupling coefficients. To verify the analytical dispersion relations, we also simulate the system numerically. Furthermore, experiment is carried out to validate the analytical observations. The obtained observations can guide designers in selecting electromechanical resonator parameters for effective energy harvesting from meta-materials.

HapticWall: An Encountered-Type Two-Dimensional Vertical System for Virtual Reality

We present HapticWall, an encountered-type, motor actuated vertical two-dimensional system that enables both small and large scale physical interactions in virtual reality. HapticWall consists of a motor-actuated vertical two-dimensional gantry system that powers the physical proxy for the virtual counterpart. The physical proxy, combined with the HapticWall system, can be used to provide both small and large scale haptic feedbacks for virtual reality in the vertical space. Haptic Wall is capable of providing wall-like haptic feedback and interactions in the vertical space. We created two virtual reality applications to demonstrate the application of the HapticWall system. Preliminary user feedback was collected to evaluate the performance and the limitations of the HapticWall system. The results of our study are presented in this paper. The outcome of this research will provide better understanding of multi-scale haptic interfaces in the vertical space for virtual reality and guide the future development of the HapticWall system.

Autonomous Light Assessment Drone for Dark Skies Studies

The improper use of artificial light causing skyglow is detrimental to many types of wildlife and can potentially cause irregular human sleeping patterns. Studies have been performed to analyze light pollution on a global scale. However, light pollution data on a local scale is not of ten available and the effects at local scale have rarely been studied. Herein, a new custom-designed autonomous light assessment drone (ALAD) is described for evaluating light pollution at local scale. The ALAD is designed and equipped with a sky quality meter (SQM) to measure skyglow and a low-cost illuminance sensor to measure light from artificial sources. Outdoor field tests are performed at a remote site in central Utah and the measured results are validated against data from lightpollution-map.info. The SQM measurements are in agreement with the estimates from the light pollution map, and the initial results demonstrate feasibility of the ALAD for local-scale skyglow assessment.

Optimal Control of a Multirotor Unmanned Aerial Vehicle Based on a Multiphysical Model

Electric multirotor aircraft with vertical-take-off-and-landing capabilities are emerging as a revolutionary transportation mode. This paper studies optimal control of a multirotor unmanned aerial vehicle based on a system-level multiphysical model. The model considers aerodynamics of the rotor-propeller assembly, electro-mechanical dynamics of the motor and motor controller, and rigid-body dynamics of the vehicle, as control based on a system-level model incorporating all these dynamics and their coupling is missing in literature. A forward flight operation is considered for time-optimal and energy-optimal control, as well as battery voltages of 25 V and 21 V. Energy-optimal control is shown to reduce the energy required for the operation by 38.5% at 25 V, while reducing the battery voltage increases the minimum operation time by 19.8%. The energy-optimal cruise velocity is also examined, demonstrating that the optimal velocity predicted without considering rotor aerodynamics uses 35.2% more energy per meter travelled than is required at the true optimal velocity.