Path planners in which a hunter is required to chase after a moving target is an important problem for robotic systems such as Unmanned Aerial Vehicles (UAVs) and Unmanned Underwater Vehicles (UUVs). This paper describes an incremental moving target path planning algorithm which leverages previous planning data to update the path in the case where the target moves. The algorithm in this paper addresses the need for a quick path-planner that can be used in an environment where the target is moving. The algorithm does this by sacrificing optimality in order to reduce the computational complexity of the problem. Examples show that the algorithm reduces the complexity of re-planning by approximately 12 times while only increasing path length taken by 1.5%. Analytical estimates of the best and worst case complexity of the algorithm were developed, and these estimates are validated with experimental data.

The ability to monitor various activities related to the human head and neck is of critical importance to medical professionals. Radio-based wireless body-area networks (WBANs) provide a potential method of tracking human motion patterns without compromising the comfort or privacy of users. This paper investigates the feasibility of using the electromagnetic creeping wave propagation around the human neck to monitor neck-based motions. The dynamic channel around the human neck contains information regarding the neck and head movements, therefore it can be considered for activity classification. The changes in the transmission coefficient (S21) between two on-body antennas are measured to classify four different head- and mouth-related motions including chewing, drinking, deep breathing, and speaking. To capture the time-varying signatures of the measured S21, the joint-time frequency analysis has been applied. The spectrograms are classified by a Deep Convolutional Neural Network.

Random numbers are a valuable commodity in gaming and gambling, simulation, conventional and quantum cryptography, and in non-conventional computing schemes such as stochastic computing. We propose to generate a random bit using a position measurement of a single mobile charge on a coupled pair of quantum dots. True randomness of the measurement outcome is - depending on implementation - provided by statistical mechanics, or by quantum mechanics via Born's rule. A random bit string may be generated using a sequence of repeated measurements on the same double quantum dot (DQD) system. Any bias toward a "0" measurement or a "1" measurement may be removed or tuned as desired simply by adjusting the bias between the dots. Device tunability provides versatility, enabling this quantum random number generator (QRNG) to support applications in which no bias is desired, or where a tunable bias is desired. We discuss a metal-dot implementation as well as a molecular implementation of this QRNG. Basic quantum mechanical principles are used to study power dissipation and timing considerations for the generation of random bit strings. The DQD offers a small form factor and, in a metallic implementation, is usable in the case where cryogenic operations are desirable (as in the case of quantum computing). For room-temperature applications, a molecular DQD may be used.

This paper presents path planning using reconfigurable analog very large scale integrated (AVLSI) circuits. Existing research has shown that custom AVLSI circuits known as application specific integrated circuits (ASICs) can theoretically be used for path planning. There are two main drawbacks to using custom ASICs: 1) circuit designs are fixed to some extent (not changeable) and 2) long design cycle/fabrication time (order of months). Reconfigurable analog circuits called field-programmable analog arrays (FPAAs) have been used to implement a variety of AVLSI circuits in a short time (order of minutes). This paper presents an algorithm for mapping a robot's environment onto an FPAA, and then presents hardware results using an FPAA to implement the path-planning algorithm. Experimental results and analysis are presented for 24 environment scenarios. Digital search methods like breadth-first search have solutions which scale on the order of O(4^d) whereas this paper will show our analog solution is on the order of O(d) where d is the depth of the solution.

Industrial robotics, military, surveying, and delivery applications have laid a foundation for research into autonomous machines, including unmanned aerial vehicles, autonomous ground vehicles, and autonomous undersea vehicles. This paper supports this research by developing a new guidance method which combines potential fields, typically used for obstacle avoidance, and parallel navigation, a popular missile guidance method. The new unified algorithm allows a mobile robot interceptor to guide to, and rendezvous with, a moving target while avoiding obstacles in its path. A Matlab simulator is used to analyze performance for different interceptor-target geometries and obstacle densities. Simulations and analysis show that the combined algorithm increases performance by reducing the time-to-contact in most of the cases. Specifically, it was found to provide a 14.4%–21.3% improvement in approximately 96%–98% of the simulation cases.