Collision avoidance effects on the mobility of a UAV swarm using chaotic ant colony with model predictive control.

The recent development of compact and economic small Unmanned Aerial Vehicles (UAVs) permits the development of new UAV swarm applications. In order to enhance the area coverage of such UAV swarms, a novel mobility model has been presented in previous work, combining an Ant Colony algorithm with chaotic dynamics (CACOC). This work is extending CACOC by a Collision Avoidance (CA) mechanism and testing its efficiency in terms of area coverage by the UAV swarm. For this purpose, CACOC is used to compute UAV target waypoints which are tracked by model predictively controlled UAVs. The UAVs are represented by realistic motion models within the virtual robot experimentation platform (V-Rep). This environment is used to evaluate the performance of the proposed CACOC with CA algorithm in an area exploration scenario with 3 UAVs. Finally, its performance is analyzed using metrics

On the use of chaotic dynamics for mobile network design and analysis: towards a trace data generator

With the constant increase of the number of autonomous vehicles and connected objects, tools to understand and reproduce their mobility models are required. We focus on chaotic dynamics and review their applications in the design of mobility models. We also provide a review of the nonlinear tools used to characterize mobility models, as it can be found in the literature. Finally, we propose a method to generate traces for a given scenario involving moving people, using tools from the nonlinear analysis domain usually dedicated to topological analysis of chaotic attractors.Comment: 22 pages, 7 figures, to be published in Journal of Difference Equations and Application

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Predictive protocol of flocks with small-world connection pattern

By introducing a predictive mechanism with small-world connections, we propose a new motion protocol for self-driven flocks. The small-world connections are implemented by randomly adding long-range interactions from the leader to a few distant agents, namely pseudo-leaders. The leader can directly affect the pseudo-leaders, thereby influencing all the other agents through them efficiently. Moreover, these pseudo-leaders are able to predict the leader's motion several steps ahead and use this information in decision making towards coherent flocking with more stable formation. It is shown that drastic improvement can be achieved in terms of both the consensus performance and the communication cost. From the industrial engineering point of view, the current protocol allows for a significant improvement in the cohesion and rigidity of the formation at a fairly low cost of adding a few long-range links embedded with predictive capabilities. Significantly, this work uncovers an important feature of flocks that predictive capability and long-range links can compensate for the insufficiency of each other. These conclusions are valid for both the attractive/repulsive swarm model and the Vicsek model.Comment: 10 pages, 12 figure

Control mechanisms for mobile devices

In this paper we consider control mechanisms for mobile devices in a stochastic environment. In particular, we consider a device in n-dimensional space subject to Brownian perturbations where a control mechanism moves the device towards its target location at a speed which is a function of its displacement. For this scenario, we construct stochastic differential equations for the mobility process and solve for the steady state probability density function of displacement. From this we are able to give general solutions to key metrics such as displacement outage (the long term probability of exceeding a given distance from the target), connectivity probability (derived from the SNR distribution in a Rayleigh channel with pathloss), the mean time at which the device first exceeds a given distance from the target, and the mean kinetic energy required by the control mechanism. We evaluate these metrics for important special cases of the control mechanism and also study the optimization problem of minimizing kinetic energy over the parameters of the control function

The chaotic milling behaviors of interacting swarms after collision

We consider the problem of characterizing the dynamics of interacting swarms after they collide and form a stationary center of mass. Modeling efforts have shown that the collision of near head-on interacting swarms can produce a variety of post-collision dynamics including coherent milling, coherent flocking, and scattering behaviors. In particular, recent analysis of the transient dynamics of two colliding swarms has revealed the existence of a critical transition whereby the collision results in a combined milling state about a stationary center of mass. In the present work we show that the collision dynamics of two swarms that form a milling state transitions from periodic to chaotic motion as a function of the repulsive force strength and its length scale. We used two existing methods as well as one new technique: Karhunen-Loeve decomposition to show the effective modal dimension chaos lives in, the 0-1 test to identify chaos, and then Constrained Correlation Embedding to show how each swarm is embedded in the other when both swarms combine to form a single milling state after collision. We expect our analysis to impact new swarm experiments which examine the interaction of multiple swarms

Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are rapidly becoming a critical military asset. In the future, advances in miniaturization are going to drive the development of insect size UAVs. New approaches to controlling these swarms are required. The goal of this research is to develop a controller to direct a swarm of UAVs in accomplishing a given mission. While previous efforts have largely been limited to a two-dimensional model, a three-dimensional model has been developed for this project. Models of UAV capabilities including sensors, actuators and communications are presented. Genetic programming uses the principles of Darwinian evolution to generate computer programs to solve problems. A genetic programming approach is used to evolve control programs for UAV swarms. Evolved controllers are compared with a hand-crafted solution using quantitative and qualitative methods. Visualization and statistical methods are used to analyze solutions. Results indicate that genetic programming is capable of producing effective solutions to multi-objective control problems

An OpenEaagles Framework Extension for Hardware-in-the-Loop Swarm Simulation

Unmanned Aerial Vehicle (UAV) swarm applications, algorithms, and control strategies have experienced steady growth and development over the past 15 years. Yet, to this day, most swarm development efforts have gone untested and thus unimplemented. Cost of aircraft systems, government imposed airspace restrictions, and the lack of adequate modeling and simulation tools are some of the major inhibitors to successful swarm implementation. This thesis examines how the OpenEaagles simulation framework can be extended to bridge this gap. This research aims to utilize Hardware-in-the-Loop (HIL) simulation to provide developers a functional capability to develop and test the behaviors of scalable and modular swarms of autonomous UAVs in simulation with high confidence that these behaviors will prop- agate to real/live ight tests. Demonstrations show the framework enhances and simplifies swarm development through encapsulation, possesses high modularity, pro- vides realistic aircraft modeling, and is capable of simultaneously accommodating four hardware-piloted swarming UAVs during HIL simulation or 64 swarming UAVs during pure simulation