We review the observations and the basic laws describing the essential aspects of collective motion -- being one of the most common and spectacular manifestation of coordinated behavior Our aim is to provide a balanced discussion of the various facets of this highly multidisciplinary field, including experiments, mathematical methods and models for simulations, so that readers with a variety of background could get both the basics and a broader, more detailed picture of the field The observations we report on include systems consisting of units ranging from macromolecules through metallic rods and robots to groups of animals and people Some emphasis is put on models that are simple and realistic enough to reproduce the numerous related observations and are useful for developing concepts for a better understanding of the complexity of systems consisting of many simultaneously moving entities As such, these models allow the establishing of a few fundamental principles of flocking In particular, it is demonstrated, that in spite of considerable differences, a number of deep analogies exist between equilibrium statistical physics systems and those made of self-propelled (in most cases living) units In both cases only a few well defined macroscopic/collective states occur and the transitions between these states follow a similar scenario, involving discontinuity and algebraic divergences

Collective Motion

One of the most important design problems for multi-UAV (Unmanned Air Vehicle) systems is the communication which is crucial for cooperation and collaboration between the UAVs. If all UAVs are directly connected to an infrastructure, such as a ground base or a satellite, the communication between UAVs can be realized through the in-frastructure. However, this infrastructure based communication architecture restricts the capabilities of the multi-UAV systems. Ad-hoc networking between UAVs can solve the problems arising from a fully infrastructure based UAV networks. In this paper, Flying Ad-Hoc Networks (FANETs) are surveyed which is an ad hoc network connecting the UAVs. The differences between FANETs, MANETs (Mobile Ad-hoc Networks) and VANETs (Vehicle Ad-Hoc Networks) are clarified first, and then the main FANET design challenges are introduced. Along with the existing FANET protocols, open research issues are also discussed.

Flying Ad-Hoc Networks (FANETs)

Unmanned aerial vehicles (UAVs) can be used to serve as aerial base stations to enhance both the coverage and performance of communication networks in various scenarios, such as emergency communications and network access for remote areas. Mobile UAVs can establish communication links for ground users to deliver packets. However, UAVs have limited communication ranges and energy resources. Particularly, for a large region, they cannot cover the entire area all the time or keep flying for a long time. It is thus challenging to control a group of UAVs to achieve certain communication coverage in a long run, while preserving their connectivity and minimizing their energy consumption. Toward this end, we propose to leverage emerging deep reinforcement learning (DRL) for UAV control and present a novel and highly energy-efficient DRL-based method, which we call DRL-based energy-efficient control for coverage and connectivity (DRL-EC3). The proposed method 1) maximizes a novel energy efficiency function with joint consideration for communications coverage, fairness, energy consumption and connectivity; 2) learns the environment and its dynamics; and 3) makes decisions under the guidance of two powerful deep neural networks. We conduct extensive simulations for performance evaluation. Simulation results have shown that DRL-EC3 significantly and consistently outperform two commonly used baseline methods in terms of coverage, fairness, and energy consumption.

Energy-Efficient UAV Control for Effective and Fair Communication Coverage: A Deep Reinforcement Learning Approach

One variation of a method for imaging an area of interest includes: within a user interface, receiving a selection for a set of interest points on a digital map of a physical area and receiving a selection for a resolution of a geospatial map; identifying a ground area corresponding to the set of interest points for imaging during a mission; generating a flight path over the ground area for execution by an unmanned aerial vehicle during the mission; setting an altitude for the unmanned aerial vehicle along the flight path based on the selection for the resolution of the geospatial map and an optical system arranged within the unmanned aerial vehicle; setting a geospatial accuracy requirement for the mission based on the selection for the mission type; and assembling a set of images captured by the unmanned aerial vehicle during the mission into the geospatial map.

Unmanned aerial vehicle and methods for controlling same

Structures and protocols are presented for configuring an unmanned aerial device to participate in the performance of tasks, for using data resulting from such a configuration or performance, or for facilitating other interactions with such devices.

Unmanned device interaction methods and systems

The present invention relates to a hierarchical system and method for task assignment (TA), coordination and communication of multiple Unmanned Aerial Vehicles (UAV's) engaging multiple attack 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 UAV's in the subtree of a given node to the targets.

Uav decision and control system

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.

Distributed Decision and Control for Cooperative UAVs Using Ad Hoc Communication

An ad hoc routing algorithm must be able to locate and maintain a path from a source to a destination node while overcoming sudden changes in the network topology and explore alternative paths in case of path breaks. One "classic" type of solution is to constantly maintain an approximate routing table at each node and use it to dynamically search for a routing path to the destination. Another, more efficient type of algorithms uses geographical/geometrical coordinates at each node (obtained by GPS devices) to forward packets and explore alternative paths to the destinations. Recently it was shown that the exact GPS coordinates can be replaced by "virtual" grid like coordinates computed based on the number of links/hops between the nodes. Computing these grid-like coordinates is a costly process eliminating the advantages of the geographical routing over the more classic types of algorithms. We propose a different type of virtual coordinates based on dynamic embedding of the ad-hoc connectivity graph by a minimal set of rooted trees. We give a novel ad hoc routing algorithm called MRA (metrical routing algorithm) that uses the tree like coordinates to efficiently find minimal cover of the nodes by rooted trees and based on the tree coordinates forward packets to their destinations. Similar to geographical routing our algorithm allows oblivious transfer of packets to any destination avoiding the use of routing tables. The proposed method is compared to the well known AODV (ad hoc on demand distance vector routing) obtaining significant improvements in terms of number of messages, number of concurrent sessions, duration of the sessions, nodes' densities, queue sizes and resilience to sudden movements of the nodes. We have built a sophisticated simulator that has been carefully designed for a fair comparison between the two algorithms.

/pdf/ad-hoc-routing-using-virtual-coordinates-based-on-rooted-d6xuki0p66.pdf

Ad-hoc routing using virtual coordinates based on rooted trees

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.

/pdf/coordination-and-communication-of-cooperative-parafoils-for-1e8i2pvijd.pdf

Coordination and Communication of Cooperative Parafoils for Humanitarian Aid

https://theory.epfl.ch/moranfe/Publications/Journals/Simulation%20Modeling%20Practice%20and%20Theory%202007.pdf

Sharoni Feldman

Papers

Uav decision and control system

Distributed Decision and Control for Cooperative UAVs Using Ad Hoc Communication

Ad-hoc routing using virtual coordinates based on rooted trees

Coordination and Communication of Cooperative Parafoils for Humanitarian Aid

IFAS: Interactive flexible ad hoc simulator