Sharoni Feldman

Distributed Decision and Control for Cooperative UAVs Using Ad Hoc Communication

This study develops a novel distributed algorithm for task assignment (TA), coordination, and communication of multiple unmanned aerial vehicles (UAVs) engaging multiple targets and conceives an ad hoc routing algorithm for synchronization of target lists utilizing a distributed computing topology. Assuming limited communication bandwidth and range, coordination of UAV motion is achieved by implementing a simple behavioral flocking algorithm utilizing a tree topology for distributed flight coordination. Distributed TA is implemented by a relaxation process, wherein each node computes a temporary TA based on the union of the TAs of its neighbors in the tree. The computation of the temporary TAs at each node is based on weighted matching in the UAV-target distances graph. A randomized sampling mechanism is used to propagate TAs among different parts of the tree. Thus, changes in the location of the UAVs and targets do not pass through the root of the tree. Simulation experiments show that the combination of the flocking and the TA algorithms yields the best performance.

IFAS: Interactive flexible ad hoc simulator

Ad Hoc networks are characterized by fast dynamic changes in the topology of the network. Introduction of new routing algorithms requires a deep and trustworthy evaluation process. In this paper we describe an Interactive Flexible Ad Hoc Simulator (IFAS) that presents a modern and novel approach to the family of Ad Hoc simulators. This simulator supports unique viewing, debugging, tuning and interactive capabilities that shorten significantly the design and debug process of new algorithms. The development of the IFAS is still going on; however we see this new simulator as an innovative tool that will stand in line together with other traditional simulators.

Coordination and Communication of Cooperative Parafoils for Humanitarian Aid

In the case of a wide-scale disaster, an accurate airdrop of emergency supplies is crucial. We present a novel, top-down approach for designing and executing airdrop missions using guided parafoils. We develop a guidance algorithm and a cooperative task management method for the autonomous handling of faults and exceptional events by the parafoil group. The autonomous operation is based on inter-parafoil ad-hoc communication. A parafoil or a number of parafoils can dynamically react to events that prevent one or more parafoils from successfully completing their mission. Two recovery methods are presented: Swap, which enables parafoils to dynamically exchange their targets, and Replace, which gives precedence to prioritized targets over low-priority targets. The parafoil guidance method, combined with the task management algorithm, significantly increases the probability of successful airdrop. The small overhead of the communication layer and the low complexity of the swap and replace recovery algorithms enable these procedures to run in a distributed environment under real-time limitations.

Dynamic Multipath Allocation in Ad Hoc Networks

Ad hoc networks are characterized by fast dynamic changes in the topology of the network. A known technique to improve QoS is to use multipath routing where packets (voice/video/...) from a source to a destination travel in two or more maximal disjoint paths. We observe that the need to find a set of maximal disjoint paths can be relaxed by finding a set of paths S wherein only bottlenecked links are bypassed. In the proposed model we assume that there is only one edge along a path in S is a bottleneck and show that by selecting random paths in S the probability that bottlenecked edges get bypassed is high. We implemented this idea in the MRA system which is a highly accurate visual ad hoc simulator currently supporting two routing protocols AODV and MRA. We have extended the MRA protocol to use multipath routing by maintaining a set of random routing trees from which random paths can be easily selected. Random paths are allocated/released by threshold rules monitoring the session quality. The experiments show that: (1) session QoS is significantly improve, (2) the fact that many sessions use multiple paths in parallel does not depredate overall performances, (3) the overhead in maintaining multipath in the MRA algorithm is negligible.

Hierarchical Decision and Control of Cooperative UAVs using Ad-Hoc Communication *

This works develops a novel hierarchical algorithm for task assignment (TA), coordination and communication of multiple UAVs engaging multiple targets and conceives an ad-hoc routing algorithm for synchronization of target lists utilizing a distributed computing topology. Assuming limited communication bandwidth and range, coordination of UAV motion is achieved by implementing a simple behavioral flocking algorithm utilizing a tree topology for target list routing. The TA algorithm is based on a graph-theoretic approach, in which a node locates all the detectable targets, identifies them and computes its distance to each target. The node then produces an attack plan that minimizes the sum of distances of the UAVs in the subtree of a given node to the targets. Simulation experiments show that the combination of flocking and TA algorithms gives the best performance. Clear-cut advantages of the TA algorithm are shown to exist in cases where the hit probability tends to one. An improvement of efficiency, representing the ratio between killed targets and the number of missile launches, is obtained for larger numbers of UAVs only if the engagement times are long enough, utilizing the improved coverage achieved by more UAVs.

Scalability Issues in Ad-Hoc Networks: Metrical Routing Versus Table-Driven Routing

When studying scalability in ad-hoc networks, most works present experimental results for a limited number of nodes (100–200). Various “explicit” clustering techniques have been proposed to improve scalability, obtaining successful sessions for 400–800 nodes. However, explicit clustering may damage the performance, e.g., cause session breaks due to fast movements of cluster heads. An alternative to explicit clustering is the use of algorithms that are “naturally clustered”, i.e., arrange the nodes in dynamic hierarchical structures. In this work, we study the effect of explicit clustering by comparing an advanced version of the Ad Hoc Distance Vector Algorithm (AODV) with the Metrical Routing Algorithm (MRA) that possesses the natural clustering property. We cover fundamental aspects of scalability and experimentally prove the superiority of implicit clustering over explicit clustering. In particular, we consider heterogeneous theaters with several types of transmitters including personal, car-mounted, helicopters and a Geostationary (GEO) satellite. Natural clustering is more effective in heterogeneous theaters as the more powerful transmitters can serve as cluster heads. A formal bound based on general probabilistic assumptions shows that all existing ad-hoc algorithms cannot scale infinitely, thus rendering scalability as an experimental issue.

Hierarchical Task Assignment and Communication Algorithms for Unmanned Aerial Vehicle Flocks

This work develops distributed hierarchical task assignments and communication algorithms for flocks of unmanned aerial vehicles dispersed in an unknown theater while engaging multiple moving targets. The dynamical changes require distributed algorithms, without relying on a central agent, which may constitute a single point-of-failure and is exposed to communication breaks. Our methodology overcomes the typical deficiencies of a centralized solution by organizing the agents in spanning trees. Whenever possible, the spanning trees merge into a single tree that clusters the maximum number of agents. Using relaxation methods and inputs from its parent and children, every agent attempts to optimize its own solution for task assignment while communicating using an ad-hoc protocol. Simulation experiments show clear-cut advantages of using the proposed task assignment and communication algorithms.