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We summarize recent progress in the development of top-down control over bottom-up colloidal processes using a taxonomy based on how active control is used to guide equilibrium, near-equilibrium, and far-from-equilibrium assembly.

Providing persistent intelligence, reconnaissance, and surveillance of targets is a challenging, but important task when time-critical information is required. In this letter, we provide a decentralized routing algorithm for coordinating multiple autonomous vehicles as they visit a discrete set of pre-defined targets with weighted revisit priorities. The algorithm utilizes a block coordinate ascent algorithm combined with a Monte Carlo tree search to tractably decide each vehicle's route. The result is a non-myopic algorithm for multiple vehicles that is decentralized, computationally tractable, and allows for target prioritization. Guarantees are provided that all targets will have finite revisit times and that the block coordinate ascent algorithm will converge to a block optimal solution. Numerical simulations illustrate the utility of this method by showing that the results are comparable to those of a centralized exhaustive search and that they degrade gracefully with limited communication and scale under increasing numbers of targets and vehicles.

Biological systems rely on chemical gradients to direct motion through both chemotaxis and signaling, but synthetic approaches for doing the same are still relatively naïve. Consequently, we present a novel method for using chemical gradients to manipulate the position and velocity of colloidal particles in a microfluidic device. Specifically, we show that a set of spatially localized chemical reactions that are sufficiently controllable can be used to steer colloidal particles via diffusiophoresis along an arbitrary trajectory. To accomplish this, we develop a control method for steering colloidal particles with chemical gradients using nonlinear model predictive control with a model based on the unsteady Green’s function solution of the diffusion equation. We illustrate the effectiveness of our approach using Brownian dynamics simulations that steer single particles along paths, such as circle, square, and figure-eight. We subsequently compare our results with published techniques for steering colloids using electric fields, and we provide an analysis of the physical parameter space where our approach is useful. Based on these findings, we conclude that it is theoretically possible to explicitly steer particles via chemical gradients in a microfluidics paradigm.

Small rotorcraft unmanned air vehicles (sUAVs) are valuable tools in solving geospatial inspection challenges. One area where this is being widely explored is disaster reconnaissance [1]. Using sUAVs to collect images provides engineers and government officials critical information about the conditions before and after a disaster [2]. This is accomplished by creating high- fidelity 3D models from the sUAV’s imagery. However, using an sUAV to perform inspections is a challenging task due to constraints on the vehicle’s flight time, computational power, and data storage capabilities [3]. The approach presented in this article illustrates a method for utilizing multiple sUAVs to inspect a disaster region and merge the separate data into a single high-resolution 3D model.

Unmanned aerial systems (UAS) are effective for surveillance and monitoring, but struggle with persistent, longterm tracking due to limited flight time. Persistent tracking can be accomplished using multiple vehicles if one vehicle can effectively hand off the tracking information to another replacement vehicle. In this paper we propose a solution to the moving-target handoff problem in the absence of GPS. The proposed solution uses a nonlinear complimentary filter for self-pose estimation using only an IMU, a particle filter for relative pose estimation between UAS using a relative range measurement, visual target tracking using a gimballed camera when the target is close to the handoff UAS, and track correlation logic using Procrustes analysis to perform the final target handoff between vehicles. We present extensive simulation results that demonstrates the effectiveness of our approach and perform Monte-Carlo simulations that indicate a 97% successful handoff rate using the proposed methods.

To guarantee safe autonomous navigation of multiple small unmanned aircraft systems (sUAS) operating within a limited airspace, collision avoidance algorithms must be robust to uncertainties in aircraft states. The velocity obstacle (VO) concept is a popular avoidance algorithm which uses a collision cone to effectively determine if two objects will collide in the near future. The VO method is a reactive algorithm that allows for avoidance of dynamic obstacles. This paper proposes a novel approach, the uncertainty velocity obstacle (UVO) method, to enhance the decentralized VO collision avoidance method by addressing uncertainties in the position and velocity of moving obstacles. A scenario is presented to illustrate the utility of this method for an sUAS encountering other cooperating vehicles. In this scenario the vehicles use global positioning system (GPS) messages to communicate position and velocity between cooperative vehicles. Each vehicle uses these states in a constant-jerk Kalman filter to estimate other vehicles' positions and velocities. Numerical simulations show that UVO enhances a vehicle's ability to avoid collisions when operating in uncertain environments.

This paper presents a controller that uses relative range and bearing measurements to steer unmanned aerial vehicles (UAVs) to circular trajectories around a constant velocity target. Estimates of the range rate and angular rate to the target are used to improve the error in the range and bearing. For moving targets, their velocity is also estimated and used in the control law. Theoretical proofs using Lyapunov theory for stationary targets and feedback linearization for moving targets are provided. Numerical simulations show vehicles converging to circular formations around both stationary and moving targets.

To enable expanded and safer access for unmanned aerial vehicles in the National Airspace System, a reliable system to detect and track them needs to be established. This paper combines two radar systems into a single network to provide tracking of UAVs across a wide area. Each radar detects the UAV’s path and those detections are combined into tracks using a recursive random sample consensus algorithm. Outdoor flight experiments show the ability of the system to track a UAV across two different radar fields of view.

In this paper, an accelerometer and gyroscope are used to sense gesture commands, which are then classified using a logistic regression model. Seven gestures were chosen and mapped to specific behaviors that a fixed wing unmanned air vehicle could accomplish. These behaviors specified various searching, following, and tracking patterns that could be used in a dynamic environment. The system was trained to recognize the seven gestures and then tested in a hardware-in-the-loop simulation. The system was able to identify all gestures with an overall accuracy of 90% and with five of the seven gestures being accurately identified at least 94% of the time. Each of the behaviors associated with the gestures was tested in simulation and the ability to dynamically switch between behaviors was proven. The results show that the system can be used as a natural interface to assist an operator in directing an unmanned air vehicle’s behaviors.

Unmanned vehicles are capable of working as teams to accomplish a wide variety of mission objectives, such as searching for and tracking targets. In this paper, vehicle search paths are dictated by a joint cost function which maximizes the reward earned from partitioned sections across the search area. In previous work, these rewards were assigned based on the elapsed time since the section had last been searched. This approach is effective in rewarding vehicles to search out areas which haven't been visited in a long time, yet it lacks the ability to weight grid cells differently based on the probability that targets will be in that section. This paper proposes a method of using accumulated knowledge of the average density of targets within an area, along with a Gaussian process regression to assign rewards. Vehicles then choose paths that are more likely to find targets rather than seeking areas which have not been searched recently. Through numerical simulations we show that this method increases the number of targets seen by cooperating UAVs and provides an accurate estimate of target density within a search area.

Unmanned vehicles are an effective platform for tracking, surveillance, and reconnaissance missions. Existing control algorithms promote collaboration of unmanned aerial vehicles and other autonomous vehicles. However, these algorithms often fail to account for the degradation of control performance caused by flowfields. This paper presents decentralized multivehicle coordination algorithms designed for operation in a spatially or temporally varying flowfield. Each vehicle is represented using a Newtonian particle traveling at constant speed relative to the flow and subject to a steering control. An algorithm is described that stabilizes a circular formation in a time-varying spatially nonuniform flowfield, assuming that the flowfield is known and does not exceed the particle speed relative to the flow. For a time-varying and spatially uniform flowfield, an algorithm is provided to stabilize a circular formation in which the temporal spacing between particles is regulated. These algorithms are extended by relaxing the assumption that the flow is known: each particle dynamically estimates the flow and uses that estimate in the control. It is shown that including a turning-rate bound does not alter the main results. The theoretical results are supported by numerical simulations that illustrate the coordinated encirclement of a maneuvering target.

Cooperative steering controls enable mobile sampling platforms to conduct synoptic, adaptive surveys of dynamic spatiotemporal processes by appropriately regulating the space-time separation of their sampling trajectories. Sensing platforms in the air and maritime domains can be pushed off course by strong and variable environmental dynamics. However, most existing cooperative-control algorithms are based on simple motion models that do not include a flowfield. Existing models that include the flowfield often include speed control to compensate for the flow. In this paper, we describe a constant-speed self-propelled particle model that explicitly incorporates a time-invariant flowfield. Each vehicle is represented by a Newtonian particle subject to a gyroscopic steering control. We describe the Lyapunov-based design of decentralized control algorithms that stabilize collective motion in a known flowfield. In the case of a spatially variable flow, we provide an algorithm to stabilize synchronized motion, in which all of the particles move in the same direction, and circular motion, in which all of the particles orbit an inertially fixed point at a constant radius. For a spatially invariant flow, we provide an algorithm to stabilize balanced motion, in which the particle position centroid is inertially fixed, and symmetric circular formations, in which the particle spacing around a circle is temporally regulated. Via the latter algorithm, we provide a method of stabilizing a circular formation in which the particles are evenly spaced in time and the formation is centered on a moving target. The theoretical results are illustrated with two numerical examples based on applications in environmental monitoring and target surveillance.

This paper examines the limits of performance for an ensemble of cooperating, mobile sensing agents executing an undersea surveillance mission. The objective of the multi-agent ensemble is to minimize uncertainty concerning the presence and location of targets as the multi-target system evolves over time. Each agent is capable of sensing, communicating with other agents, processing data to infer states of interest (fusion), and deciding on and executing motion commands. Each agent continually executes a perception-action cycle in which it fuses information to determine its best estimate of the multi-target system state and decides on its next (and possibly future) motion action(s) to optimize a criterion related to its entropic state (quantification of information gain or loss). Each agent's perception of the states of interest is derived from measurements captured by its own sensor(s) and information communicated by other agents. Each agent's decisions are based on its estimates of the multi­ target system state, its entropic state, and its predictions of peer agent actions. The multi-agent cooperative decision making can be modeled as a cyclic optimization whereby the joint decision vector is optimized by sequentially optimizing each individual agent's decision vector while holding the others fixed. Moreover, the problem is a cyclic stochastic optimization (CSO) whereby only noisy measurements of the objective function are available to each agent. Preliminary theoretical results have recently emerged regarding convergence conditions and sub-optimality for CSO. This paper examines the implications and applicability of CSO convergence and sub-optimality via simulation- based experiments in the context of a cooperating multi-agent ensemble of undersea sensing agents searching a region for new targets and maintaining track on all discovered targets. Simulation results indicate that the theoretical results provide useful guidance on predicting the empirically observed limits of performance of the multi-agent ensemble.

Profilin-1 (Pfn-1) is a ubiquitously expressed actin-binding protein that is essential for normal cell proliferation and migration. In breast cancer and several other adenocarcinomas, Pfn-1 expression is downregulated when compared to normal tissues. Previous studies from our laboratory have shown that genetically modulating Pfn-1 expression significantly impacts proliferation, migration, and invasion of breast cancer cells in vitro, and mammary tumor growth, dissemination, and metastatic colonization in vivo. Therefore, small molecules that can modulate Pfn-1 expression could have therapeutic potential in the treatment of metastatic breast cancer. The overall goal of this study was to perform a multiplexed phenotypic screen to identify compounds that inhibit cell motility through upregulation of Pfn-1. Screening of a test cassette of 1280 compounds with known biological activities on an Oris™ Pro 384 cell migration platform identified several agents that increased Pfn-1 expression greater than two-fold over vehicle controls and exerted anti-migratory effects in the absence of overt cytotoxicity in MDA-MB-231 human breast cancer cells. Concentration-response confirmation and orthogonal follow-up assays identified two bona fide inducers of Pfn-1, purvalanol and tyrphostin A9, that confirmed in single-cell motility assays and Western blot analyses. SiRNA-mediated knockdown of Pfn-1 abrogated the inhibitory effect of tyrphostin A9 on cell migration, suggesting Pfn-1 is mechanistically linked to tyrphostin A9′s anti-migratory activity. The data illustrate the utility of the high-content cell motility assay to discover novel targeted anti-migratory agents by integrating functional phenotypic analyses with target-specific readouts in a single assay platform.

Cooperating autonomous vehicles perform better than uncooperating vehicles for applications such as surveillance, environmental sampling and target tracking. For multiple vehicles to cooperate effectively, the navigation control laws should account for disturbances caused by ocean currents or atmospheric winds. This paper provides dynamic decentralized control algorithms for motion coordination in an unknown, time-invariant flowfield. The algorithms simultaneously estimate the flowfield and use that estimate in an observer-based feedback control that stabilizes a moving formation. Each vehicle uses noisy measurements of its own position to generate independent flowfield estimates. For a uniform flowfield, we provide a theoretically justified approach for each vehicle to estimate the flow independently. For a nonuniform flowfield, we propose a distributed algorithm using an information filter to reconstruct the flowfield and a consensus filter to share information between vehicles. In either case, the vehicles use the flowfield estimate to steer to a circular formation.

Conventional target recognition approaches for SAR include template matching and feature-based classification. However, unlike visual imagery, Synthetic Aperture Radar (SAR) presents a unique challenge in that many attributes, such as scattering centers, are extremely pose dependent and wink in and out with even minor viewing geometry changes. This work implements a highly efficient biologically-inspired 3D template-based approach, the Map Seeking Circuit (MSC) algorithm, for target recognition in SAR. Instead of exhaustively searching a high dimensional state space, the MSC algorithm efficiently searches a superposition hypersurface to estimate target location and 3D pose. Results are shown from applying the algorithm to real SAR datasets.

Unmanned vehicles provide an effective approach for tracking, surveillance, and reconnaissance missions. Much work has been done to provide control algorithms to promote collaboration of UAVs and other autonomous vehicles. However, the majority of this work does not account for the adverse effects caused by flowfields. In this paper we present decentralized control algorithms designed to operate in a spatially or temporally varying flowfield. We use a Newtonian-particle model to represent each vehicle. Each particle travels at a constant speed and uses a steering control to compensate for the flow. We propose an algorithm that stabilizes a circular formation in a time-varying, spatially nonuniform flowfield. The center of the formation can be arbitrary or prescribed. In the case of a time-varying and spatially uniform flowfield, we propose an algorithm to stabilize a circular formation in which the temporal spacing between particles is regulated. The theoretical results are supported by numerical simulations that illustrate a circular formation tracking a maneuvering target as it turns or accelerates.

This paper investigates the problem of non-myopic multiple platform trajectory control in a multiple target search and track setting. It presents a centralized receding discrete time horizon controller (RHC) with variable-step look-ahead for motion planning of a heterogeneous ensemble of airborne sensor platforms. The controller operates in a closed feedback loop with a Multiple Hypothesis Tracker (MHT) that fuses the disparate sensor data to produce target declarations and state estimates. The RHC action space for each air vehicle is represented via maneuver automaton with simple motion primitives. The reward function is based on expected Fisher information gain and priority scaling of target tracks and ground regions. A customized Particle Swarm Optimizer (PSO) is developed to handle the resulting non-Markovian, time-varying, multi-modal, and discontinuous reward function. The algorithms were evaluated by simulating ground surveillance scenarios using representative sensors with varying fields of view and typical target densities and motion profiles. Simulation results show improved aggregate target detection, track accuracy, and track maintenance for closed-loop operation as compared with typical open-loop surveillance plans.