Target localization is an important task for a broad range of ubiquitous computing applications. A great deal of previous work has focused on applying advanced machine learning methods to address image-based target detection and localization problems. Some work has also explored the localizing capabilities of radar signals that describe target distances and velocities. This paper proposes a novel machine learning architecture, SoliNet, to localize target positions from radar images alone. Radar data were acquired by Google’s Soli sensors and processed to generate range-Doppler images describing the motion of a radar target. Corresponding camera images of the target were used as validation data. Over 60 experiments including six unique target trajectories were performed using a robotic actuator rig. The SoliNet machine learning architecture was then trained to accomplish localization using the known location of the target over time as dictated by the programmed robot trajectory. SoliNet is an end-to-end trained combination of deep convolutional and recurrent neural networks. Experimental results showed an overall accuracy of 98.6% and equivalent effectiveness to camera-based target localization, demonstrating the promising capabilities of the proposed technique.

Tablet computers hold great promise as educational tools. Many universities, high schools and elementary schools are working to create curricula that exploit these devices. An important part of this work is the development of textbooks that move away from the static display of words and figures to include interactive components such as videos and interactive simulations. We present such an interactive textbook, developed for an undergraduate course in mechanical engineering. We describe the course for which it was developed and the interactive components of the book. Student evaluations of the textbook are presented and discussed. Finally, we offer suggestions for additional steps that can improve interactive textbooks for use on tablet computers.

Cranes are commonly used to swing wrecking balls that demolish unwanted buildings and other structures. The crane operator moves the machine back and forth to increase the wrecking ball swing and then attempts to direct the swinging ball at the desired point of impact. By increasing the oscillation, the wrecking ball has more damaging force at impact. The rudimentary control interface, the sluggish dynamic response of the crane, and the limited field of view make this a challenging problem for the human operator. Although shaping human operator commands has traditionally been used to reduce oscillation, a related design process can be used to develop oscillation-amplifying commands. This paper presents such a command-shaping method that aids the operator in maximizing the swing of a crane payload. The effectiveness of the command shapers is verified through numerous simulations and experiments.

This paper examines the manual control of systems with oscillatory dynamics. Tracking performance is improved by using input shaping to suppress command-induced oscillation. An operator study tested tracking behavior using controlled elements with both low-frequency (1.25 rad/s) and high-frequency (5 rad/s) oscillatory modes. After each experimental trial, measures of tracking performance and subjective task difficulty were recorded, and frequency-domain control characteristics were computed. Results showed that the high-frequency oscillatory mode did not greatly decrease the tracking performance from the nonoscillatory case; thus, input shaping did not produce a significant improvement in the tracking performance. However, input shaping did cause a decrease in the average subjective task difficulty and made the system closely resemble McRuer's “crossover model.” For the low-frequency case, the addition of input shaping significantly improved the tracking performance and reduced the tracking difficulty. These results demonstrate that input shaping can greatly improve the continuous tracking ability of human-machine systems that have oscillatory modes.

This paper proposes a method for optimizing a control system that includes a human in the loop. The method uses human-operator testing to measure the system's performance for a given value of a control parameter. Root-mean-squared (RMS) tracking error is used to quantify performance. The optimum control parameter value is found using a one-dimensional search algorithm. As an example application, the golden-section search method is used to find the optimal form of a specified-negative-amplitude (SNA) input shaper. Results from an operator experiment show that the method was able to identify an optimal range of parameter values using a relatively small number of iterations.

The payload oscillation inherent to all cranes makes it challenging for human operators to manipulate payloads quickly, accurately, and safely. Manipulation difficulty is also increased by nonintuitive crane-control interfaces. This paper describes a new interface that allows operators to drive a crane by moving a hand-held device (wand or glove) freely in space. A crane-mounted camera tracks the movement of the hand-held device, the position of which is used to drive the crane. Two control architectures were investigated. The first uses a simple feedback controller, and the second uses feedback and an input shaper. Two operator studies demonstrate that hand-motion crane control is faster and safer than using a standard push-button pendent control.

This paper explores methods to include aspects of controller taskload into conflict-resolution programs through a parametric approach. We are motivated by the desire to create conflict-resolution decision-support tools that operate within a human-in-the-loop control architecture by actively accounting for, and moderating, controller taskload. Specifically, we introduce two conflict-resolution programs with the objective of managing controller conflict-resolution taskload, i.e., the number of maneuvers used to separate air traffic. Managing conflict-resolution taskload is accomplished by penalizing aircraft maneuvers through theirL1norm in the cost function or constraining the number of maneuvers directly. Analysis of the programs reveals that both approaches are successful at managing controller conflict-resolution taskload and minimizing fuel burn. Directly constraining conflict-resolution taskload is more successful at minimizing the variation in the number of aircraft maneuvers issued and returning the aircraft to their desired exit point. Penalizing maneuvers throughL1norm costs is more successful at reducing controller conflict-resolution taskload at lower traffic volumes. Ultimately, results demonstrate that the inclusion of such parametric models can successfully regulate controller conflict-resolution taskload.

Operating cranes is challenging because the payload significantly lags behind the control input and can undergo large amplitude oscillations. While significant work has been directed at reducing the payload swing, little effort has been placed on reducing the time lag. There is a good reason for neglecting the time lag; it cannot be eliminated. It is a result of the physical limitations of the crane; motor torque limits coupled with the very large inertia of cranes and their payloads cause sluggish behavior. Experienced crane operators become accustomed to the time lag and develop the skill to start decelerating the crane well before the desired stopping location. This paper presents a control method that aids the human operator by graphically displaying a prediction of where the crane will stop. This predictive element is combined with an input-shaping controller that both reduces the payload swing and simplifies the implementation of the predictive element. Results from a study of crane operators show that the proposed control system significantly improves tower crane performance, in terms of both task completion time and positioning accuracy.

In this paper, we consider the air-traffic conflict-resolution problem and develop an optimization model to identify the required heading and speed changes of aircraft to avoid conflict such that fuel costs are minimized. Nonconvex fuel functions in the optimization problem are modeled through tight linear approximations, which enable the formulation of the problem as a mixed-integer linear program. The significance of the developed model is that fuel-optimal conflict-resolution maneuvers can be identified in near real time, even for conflicts involving a large number of aircraft. Computational tests based on realistic air-traffic scenarios demonstrate that conflicts involving up to 15 aircraft can be solved in less than 10 s with an optimality gap of around 0.02%.

The payload oscillation inherent to all cranes makes it challenging for human operators to manipulate pay-loads quickly, accurately, and safely. Under certain conditions, the problem is compounded when the payload creates a double-pendulum effect. This paper presents an input-shaping control method for suppressing double-pendulum payload oscillations. Local and teleoperation experiments performed on a portable tower crane are used to verify the effectiveness of the method. Data from these experiments show that operators performed manipulation tasks faster and safer when input shaping was utilized to reduce payload sway. Furthermore, the teleoperation delays did not degrade the input shaping effectiveness.

Input shaping is a method of reducing residual vibrations in computer-controlled machines. Input shaping is implemented by convolving the desired command signal with a sequence of impulses. The result of the convolution is then used to drive the system. Because input shaping alters the commanded trajectory, it has had questionable utility for trajectory following applications such as painting, cutting, and scanning. The effects of input shaping on trajectory following were investigated by simulating the response of a fourth-order system with orthogonal modes and conducting experiments on an XY positioning stage. For nearly all values of experimental parameters, input shaping improved trajectory following.

A method is described for limiting vibration in flexible systems by shaping the system inputs. Unlike most previous attempts at input shaping, this method does not require an extensive system model or lengthy numerical computation; only knowledge of the system's natural frequency and damping ratio is required. The effectiveness of this method when there are errors in the system model is explored and quantified. An algorithm is presented which, given an upper bound on acceptable residual vibration amplitude, determines a shaping strategy that is insensitive to errors in the estimated natural frequency.>