This paper presents a novel optimal control problem, referred to as distributed optimal control, that is applicable to multiscale dynamical systems comprised of numerous interacting agents. The system performance is represented by an integral cost function of the macroscopic state that is optimized subject to a hyperbolic partial differential equation known as the advection equation. The microscopic control laws are derived from the optimal macroscopic description using a potential function approach. The optimality conditions of the distributed optimal control problem are first derived analytically and, then, demonstrated numerically through a multi-agent trajectory optimization problem

This paper presents a distributed optimal control approach for managing omnidirectional sensor networks deployed to cooperatively track moving targets in a region of interest. Several authors have shown that under proper assumptions, the performance of mobile sensors is a function of the sensor distribution. In particular, the probability of cooperative track detection, also known as track coverage, can be shown to be an integral function of a probability density function representing the macroscopic sensor network state. Thus, a mobile sensor network deployed to detect moving targets can be viewed as a multiscale dynamical system in which a time-varying probability density function can be identified as a restriction operator, and optimized subject to macroscopic dynamics represented by the advection equation. Simulation results show that the distributed control approach is capable of planning the motion of hundreds of cooperative sensors, such that their effectiveness is significantly increased compared to that of existing uniform, grid, random, and stochastic gradient methods.

Many complex systems, ranging from renewable resources [1] to very-large-scale robotic systems (VLRS) [2], can be described as multiscale dynamical systems comprising many interactive agents. In recent years, significant progress has been made in the formation control and stability analysis of teams of agents, such as robots, or autonomous vehicles. In these systems, the mutual goals of the agents are, for example, to maintain a desired configuration, such as a triangle or a star formation, or to perform a desired behavior, such as translating as a group (schooling) or maintaining the center of mass of the group (flocking) [2]-[7]. While this literature has successfully illustrated that the behavior of large networks of interacting agents can be conveniently described and controlled by density functions, it has yet to provide an approach for optimizing the agent density functions such that their mutual goals are optimized.

We consider the search for moving targets over large areas using a network of fixed sensor nodes. The concept of track-before-detect is defined and used to both manage the information between sensors and reduce the likelihood of false searches. We develop expressions for the probability of search success and the probability of reporting false search in this system concept. Using these as performance measures, we examine how the track-before-detect search strategy impacts design choices in these networks, by showing which parameter changes in the sensor design have the greatest impact on improving the desired performance goals.

This paper addresses the problem of target detection in dynamic environments in a semi-supervised data-driven setting with low-cost passive sensors. A key challenge here is to simultaneously achieve high probabilities of correct detection with low probabilities of false alarm under the constraints of limited computation and communication resources. In general, the changes in a dynamic environment may significantly affect the performance of target detection due to limited training scenarios and the assumptions made on signal behavior under a static environment. To this end, an algorithm of binary hypothesis testing is proposed based on clustering of features extracted from multiple sensors that may observe the target. First, the features are extracted individually from time-series signals of different sensors by using a recently reported feature extraction tool, called symbolic dynamic filtering. Then, these features are grouped as clusters in the feature space to evaluate homogeneity of the sensor responses. Finally, a decision for target detection is made based on the distance measurements between pairs of sensor clusters. The proposed procedure has been experimentally validated in a laboratory setting for mobile target detection. In the experiments, multiple homogeneous infrared sensors have been used with different orientations in the presence of changing ambient illumination intensities. The experimental results show that the proposed target detection procedure with feature-level sensor fusion is robust and that it outperforms those with decision-level and data-level sensor fusion.

This letter addresses the time-optimal risk-aware motion planning problem for curvature-constrained variable-speed vehicles in the presence of obstacles. To the best of our knowledge, this problem has not been solved. To obtain a feasible solution, we present a grid-based method, called T*, which computes the time-optimal risk-aware path by finding the optimal sequence of vehicle states from the start to the goal with the minimum total cost of time and risk. We propose a novel risk function based on the concept of collision time, which utilizes the complete information of the vehicle state, including its location with respect to obstacles, heading angle, and speed. We also propose a state pruning technique that can significantly reduce the computational complexity. The algorithm was validated in simulations of complex obstacle-rich scenarios, and the resulting paths are shown to be superior than the Dubins paths.