Design of an UAV swarm

This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation

Distributed sequential task allocation in foraging swarms

When designing a practical swarm robotics system, self-organized task allocation is key to make best use of resources. Current research in this area focuses on task allocation which is either distributed (tasks must be performed at different locations) or sequential (tasks are complex and must be split into simpler sub-tasks and processed in order). In practice, however, swarms will need to deal with tasks which are both distributed and sequential. In this paper, a classic foraging problem is extended to incorporate both distributed and sequential tasks. The problem is analysed theoretically, absolute limits on performance are derived, and a set of conditions for a successful algorithm are established. It is shown empirically that an algorithm which meets these conditions, by causing emergent cooperation between robots can achieve consistently high performance under a wide range of settings without the need for communication

An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms

Searching and collecting multiple resources from large unmapped environments is an important challenge. It is particularly difficult given limited time, a large search area and incomplete data about the environment. This search task is an abstraction of many real-world applications such as search and rescue, hazardous material clean-up, and space exploration. The collective foraging behavior of robot swarms is an effective approach for this task. In our work, individual robots have limited sensing and communication range (like ants), but they are organized and work together to complete foraging tasks collectively. An efficient foraging algorithm coordinates robots to search and collect as many resources as possible in the least amount of time. In the foraging algorithms we study, robots act independently with little or no central control. As the swarm size and arena size increase (e.g., thousands of robots searching over the surface of Mars or ocean), the foraging performance per robot decreases. Generally, larger robot swarms produce more inter-robot collisions, and in swarm robot foraging, larger search arenas result in larger travel distances causing the phenomenon of diminishing returns. The foraging performance per robot (measured as a number of collected resources per unit time) is sublinear with the arena size and the swarm size. Our goal is to design a scale-invariant foraging robot swarm. In other words, the foraging performance per robot should be nearly constant as the arena size and the swarm size increase. We address these problems with the Multiple-Place Foraging Algorithm (MPFA), which uses multiple collection zones distributed throughout the search area. Robots start from randomly assigned home collection zones but always return to the closest collection zones with found resources. We simulate the foraging behavior of robot swarms in the robot simulator ARGoS and employ a Genetic Algorithm (GA) to discover different optimized foraging strategies as swarm sizes and the number of resources is scaled up. In our experiments, the MPFA always produces higher foraging rates, fewer collisions, and lower travel and search time than the Central-Place Foraging Algorithm (CPFA). To make the MPFA more adaptable, we introduce dynamic depots that move to the centroid of recently collected resources, minimizing transport times when resources are clustered in heterogeneous distributions. Finally, we extend the MPFA with a bio-inspired hierarchical branching transportation network. We demonstrate a scale-invariant swarm foraging algorithm that ensures that each robot finds and delivers resources to a central collection zone at the same rate, regardless of the size of the swarm or the search area. Dispersed mobile depots aggregate locally foraged resources and transport them to a central place via a hierarchical branching transportation network. This approach is inspired by ubiquitous fractal branching networks such as animal cardiovascular networks that deliver resources to cells and determine the scale and pace of life. The transportation of resources through the cardiovascular system from the heart to dispersed cells is the inverse problem of transportation of dispersed resources to a central collection zone through the hierarchical branching transportation network in robot swarms. We demonstrate that biological scaling laws predict how quickly robots forage in simulations of up to thousands of robots searching over thousands of square meters. We then use biological scaling predictions to determine the capacity of depot robots in order to overcome scaling constraints and produce scale-invariant robot swarms. We verify the predictions using ARGoS simulations. While simulations are useful for initial evaluations of the viability of algorithms, our ultimate goal is predicting how algorithms will perform when physical robots interact in the unpredictable conditions of environments they are placed in. The CPFA and the Distributed Deterministic Spiral Algorithm (DDSA) are compared in physical robots in a large outdoor arena. The physical experiments change our conclusion about which algorithm has the best performance, emphasizing the importance of systematically comparing the performance of swarm robotic algorithms in the real world. We illustrate the feasibility of implementing the MPFA with transportation networks in physical robot swarms. Full implementation of the MPFA in an outdoor environment is the next step to demonstrate truly scalable and robust foraging robot swarms