Scale-free features in collective robot foraging

In many complex systems observed in nature, properties such as scalability, adaptivity, or rapid information exchange are often accompanied by the presence of features that are scale-free, i.e., that have no characteristic scale. Following this observation, we investigate the existence of scale-free features in artificial collective systems using simulated robot swarms. We implement a large-scale swarm performing the complex task of collective foraging, and demonstrate that several space and time features of the simulated swarm-such as number of communication links or time spent in resting state-spontaneously approach the scale-free property with moderate to strong statistical plausibility. Furthermore, we report strong correlations between the latter observation and swarm performance in terms of the number of retrieved items

Sophisticated collective foraging with minimalist agents: a swarm robotics test

How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments conducted with more capable real ants that sense pheromone concentration and follow its gradient. One key feature of our controllers is a control parameter which balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarmsize-dependent strategy. We test swarms implementing our controllers against our optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour

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

Space-Time Continuous Models of Swarm Robotic Systems: Supporting Global-to-Local Programming

A generic model in as far as possible mathematical closed-form was developed that predicts the behavior of large self-organizing robot groups (robot swarms) based on their control algorithm. In addition, an extensive subsumption of the relatively young and distinctive interdisciplinary research field of swarm robotics is emphasized. The connection to many related fields is highlighted and the concepts and methods borrowed from these fields are described shortly

Improving Robot Team's performance by Passing Objects between Robots

Department of Computer Science and EngineeringA transport robot system is a robotic system in which robots move objects from one place to another place. Most existing transport robot systems perform three tasks: loading an item, moving to another location, and unloading the item. Traditional mobile robots, which carry objects one at a time, is not suitable for repeatedly transporting objects over a long distance. Therefore, in the factory or warehouse environment, they still use conveyor belts to transport a large number of objects. However, the existing conveyor belts are physically fixed in their environments, and it is difficult to reconfigure the layout of a conveyor network. In this thesis, I presente three new robotic systems that have the ability to pass objects at a distance between mobile robots. These three robotic systems are mobile conveyor belts, dynamic robot chains, and mobile workstations. First, conveyor belts are commonly used to transport many objects rapidly and effectively. I present a novel conveyor system called a mobile conveyor line that can autonomously organize itself to transport objects to a given location. In this thesis, I analyze the reachability of multiple mobile conveyor belts and present an algorithm to verify the reachability of a specified destination, as well as a way to gen- erate a configuration for connecting conveyor belts to reach the destination. The key results include a complete set of equations describing the reachable set of a mobile conveyor belt on a flat surface, which leads to an effective probabilistic strategy for autonomous configuration. The results of the experiment demonstrated the overlap effect, which states that reachable sets frequently overlap. This system can be suitable for locations where it is difficult to install a conveyor line, such as disaster zones. Second, I present to use mobile conveyor belts in foraging tasks in environments with obstacles. Foraging robots can form a dynamic robot chain network that can quickly send resources received from other foraging robots to a collecting zone called a depot area. A robot chain is essentially a sequence of mobile robots with the ability to quickly pass resources at a long distance. A dynamic robot chain network is a network of robot chains that allow the branches of the robot chains to connect multiple resource clusters. By allowing branching, the traffic near the end of the robot chain network can be dis- tributed to several branches, and congestion can be avoided. The dynamic robot chain network leverages mobility to relocate, reduce collection time for other robots, and quickly send resources received from other foraging robots to the depot area. The key result is the formation of robot chains capable of over- coming the two major limitations of existing dynamic depot foraging systems: the long travel distance for delivery and congestion near the central collection zone. In the experiments, given the same num- ber of robots, a dynamic robot chain network outperformed existing dynamic depots in multiple-place foraging problems. Third, I consider the idea of mobile workstations, which integrate mobile platforms with production machinery to improve efficiency by overlapping production time and delivery time. I describe a task planning algorithm for multiple mobile workstations and offer a model of mobile workstations and their jobs. This planning problem for mobile workstations includes the features of both traveling salesman problems (TSP) and job shop scheduling problems (JSP). For planning, I presente two algorithms: a) a complete search algorithm that offers a minimum makespan plan and b) a local search in the space of task graphs to offer suboptimal plans quickly. According to the experiments, the second algorithm can generate near-optimal temporal plans when the number of jobs is small. In addition, the second algorithm can generate noticeably shorter plans than a version of the job shop scheduling algorithm and SGPlan 5 when the number of jobs is large. This research shows that transport robot systems could work together with other robots or machines in various environments to overcome the limitations of existing systems for the environments. A mobile conveyor line can pass quickly objects at a long distance and can apply to many different environments by overcoming the existing problem of conveyor belts. By using mobile conveyor belts, the robots have the ability to pass objects at a distance between mobile robots to improve the performance of foraging tasks by overcoming the long travel distance for delivery and congestion near the central collection zone. In addition, a mobile workstation can handle the tasks that transport the production of goods to users. By paralleling the production time and the movement, a mobile workstation can substantially shorten the time it takes to deliver products to customers.ope

Nonlinear state and parameter estimation of spatially distributed systems

In this thesis two probabilistic model-based estimators are introduced that allow the reconstruction and identification of space-time continuous physical systems. The Sliced Gaussian Mixture Filter (SGMF) exploits linear substructures in mixed linear/nonlinear systems, and thus is well-suited for identifying various model parameters. The Covariance Bounds Filter (CBF) allows the efficient estimation of widely distributed systems in a decentralized fashion

An Analytical and Spatial Model of Foraging in a Swarm of Robots

Abstract. The foraging scenario is important in robotics, because it has many different applications and demands several fundamental skills from a group of robots, such as collective exploration, shortest path finding, and efficient task allocation. Particularly for large groups of robots emergent behaviors are desired that are decentralized and based on local information only. But the design of such behaviors proved to be difficult because of the absence of a theoretical basis. In this paper, we present a macroscopic model based on partial differential equations for the foraging scenario with virtual pheromones as the medium for communication. From the model, the robot density, the food flow and a quantity describing qualitatively the stability of the behavior can be extracted. The mathematical model is validated in a simulation with a large number of robots. The predictions of the model correspond well to the simulation. Key words: macroscopic model, foraging, swarm robotics, mathematical analysis