Showing papers by "Swaroop Darbha published in 2016"

GPS Denied UAV Routing with Communication Constraints

Abstract: A novel GPS denied routing problem for UAVs is described, where the UAVs cooperatively navigate through a restricted zone deployed with noncommunicating Unattended Ground Sensors (UGS). The routing algorithm presenting in this paper ensures the UAVs maintain strict contact with at least one UGS, which allows the UGS act as beacons for relative navigation eliminating the need for dead reckoning. This problem is referred to as the Communication Constrained UAV Routing Problem (CCURP). Two architectures for cooperative navigation of two or three UAVs are considered. For the two UAV problem, a 92$\frac {9}{2}$-approximation algorithm is developed. The three UAV problem is transformed into a one-in-a-set Traveling Salesman Problem (TSP), which is solved as a regular asymmetric TSP using existing methods after applying a second transformation. Computational results corroborating the performance bounds are presented.

Optimal Human–Machine Teaming for a Sequential Inspection Operation

Abstract: A novel mixed initiative optimal control system for intelligence, surveillance and reconnaissance (ISR) operations which entails human–machine teaming has been developed. The scenario entails a camera-equipped unmanned air vehicle sequentially overflying geolocated objects of interest, which need to be classified as either a true or false target by a human operator. The vehicle is allowed a prespecified number of revisits, such that an object can be looked at, a second time, under better viewing conditions. The overarching goal is to correctly classify the objects and minimize the false alarm (FA) and missed detection (MD) rates. We design a stochastic controller that computes if and when a revisit is necessary and also the optimal revisit state, i.e., viewing altitude and aspect angle. The concept of operation is such that the critical task of detection/pattern recognition is relegated to the human operator, whereas optimal decision making is entrusted to the machine. The stochastic dynamic programming-based decision algorithm is, however, informed about the performance of the human operator via an empirical human perception model. The model is experimentally obtained in the form of state-dependent confusion matrices. The optimal closed-loop ISR system is shown to experimentally achieve a FA rate of 5% and MD rate of 12%, which are significantly lower than the open-loop operator-only performance metrics. The performance improvements that were observed are relevant to a particular operator, and thus, the study suggests that the same improvements could conceivably be achieved with other test subjects.

Algorithms for a Satellite Constellation scheduling Problem

Abstract: Remote sensing systems such as a constellation of satellites periodically observe regions on the surface of the earth to collect visual imagery and other sensory data that is both spatial and temporal. Efficiently scheduling sensing activities on a constellation of satellites is a natural problem that arises while managing these systems. Given a set of satellites, a set of sensing activities with their priorities and timing constraints, the objective of the problem is to assign activities to the satellites over a given time period such that at most one activity is assigned to a satellite at any time and the quality of information collected by the satellites is maximized. This problem is computationally challenging to solve and is NP-Hard. In this research, heuristics are first developed to find feasible solutions based on a greedy approach and by dividing the given time period into smaller blocks of time. To determine the quality of a feasible solution, an integer linear programming approach is also developed. Numerical results show that good feasible solutions can be obtained in the order of seconds on a standard computer for a constellation of up to eight satellites and thousand activities using the proposed algorithms.

Performance Guarantee of an Approximate Dynamic Programming Policy for Robotic Surveillance

Abstract: This paper is focused on the development and analysis of suboptimal decision algorithms for a collection of robots that assist a remotely located operator in perimeter surveillance. The operator is tasked with the classification of incursions across the perimeter. whenever there is an incursion into the perimeter, an unattended ground sensor (UGS) in the vicinity, signals an alert. A robot services the alert by visiting the alert location, collecting information, e.g., photo and video imagery, and transmitting it to the operator. The accuracy of operator's classification depends on the volume and freshness of information gathered and provided by the robots at locations where incursions occur. There are two competing objectives for a robot: it needs to spend adequate time at an alert location to collect evidence for aiding the operator in accurate classification but it also needs to service other alerts as soon as possible, so that the evidence collected is relevant. The decision problem is to determine the optimal amount of time a robot must spend servicing an alert. The incursions are stochastic and their statistics are assumed to be known. This problem can be posed as a Markov Decision Problem. However, even for two robots and five UGS locations, the number of states is of the order of billions rendering exact dynamic programming methods intractable. Approximate dynamic programming (ADP) via linear programming (LP) provides a way to approximate the value function and derive suboptimal strategies. The novel feature of this paper is the derivation of a tractable lower bound via LP and the construction of a suboptimal policy whose performance improves upon the lower bound. An illustrative perimeter surveillance example corroborates the results derived in this paper.

Reconfiguration of a vehicle formation with ring communication structure

Abstract: In multiple vehicle formations, there is often a need to reconfigure communication graphs to accommodate (1) for addition or removal of vehicles or (2) for change in positions of the vehicles within a given formation. This problem is commonly referred to as the topology or graph reconfiguration problem. In this paper, we will study the reconfiguration problem from the point of view of increasing the size of the formation. We first discuss the suitability of the ring structure to add vehicles into the formation followed by ways to reconfigure the communication structure when adding vehicles. We will show that the directed ring graph is well suited for adding vehicles from the point of view of scalability of the existing controller and the ease with which the existing ring structure will be able to handle the increase in the formation size. The algorithm for obtaining the ring structure is formulated as a specific instance of the Traveling Salesman Problem, where constraints may be included to model the communication sensing range; in addition, we use the nearest neighbor search to include new vehicles into the perimeter of the formation.

Reconfiguration of a vehicle formation with ring communication structure

Abstract: In multiple vehicle formations, there is often a need to reconfigure communication graphs to accommodate (1) for addition or removal of vehicles or (2) for change in positions of the vehicles within a given formation. This problem is commonly referred to as the topology or graph reconfiguration problem. In this paper, we will study the reconfiguration problem from the point of view of increasing the size of the formation. We first discuss the suitability of the ring structure to add vehicles into the formation followed by ways to reconfigure the communication structure when adding vehicles. We will show that the directed ring graph is well suited for adding vehicles from the point of view of scalability of the existing controller and the ease with which the existing ring structure will be able to handle the increase in the formation size. The algorithm for obtaining the ring structure is formulated as a specific instance of the Traveling Salesman Problem, where constraints may be included to model the communication sensing range; in addition, we use the nearest neighbor search to include new vehicles into the perimeter of the formation.