Daniel S. Brown

Human-swarm interactions based on managing attractors

Leveraging the abilities of multiple affordable robots as a swarm is enticing because of the resulting robustness and emergent behaviors of a swarm. However, because swarms are composed of many different agents, it is difficult for a human to influence the swarm by managing individual agents. Instead, we propose that human influence should focus on (a) managing the higher level attractors of the swarm system and (b) managing trade-offs that appear in mission-relevant performance. We claim that managing attractors theoretically allows a human to abstract the details of individual agents and focus on managing the collective as a whole. Using a swarm model with two attractors, we demonstrate this concept by showing how limited human influence can cause the swarm to switch between attractors. We further claim that using quorum sensing allows a human to manage trade-offs between the scalability of interactions and mitigating the vulnerability of the swarm to agent failures.

Supporting human interaction with robust robot swarms

In this paper we propose a bio-inspired model for a decentralized swarm of robots, similar to the model proposed by Couzin [5], that allows for dynamic task assignment and is robust to limited communication from a human. We provide evidence that the model has two fundamental attractors: a torus attractor and a flock attractor. Through simulation and mathematical analysis we investigate the stability of these attractors and show that a control input can be used to force the system to change from one attractor to the other. Finally, we generalize another of Couzins ideas [4] and present the idea of a stakeholder agent. We show how a human operator can use stakeholders to responsively influence group behavior while maintaining group structure.

Two invariants of human-swarm interaction

The search for invariants is a fundamental aim of scientific endeavors. These invariants, such as Newtons laws of motion, allow us to model and predict the behavior of systems across many different problems. In the nascent field of Human-Swarm Interaction (HSI), a systematic identification of fundamental invariants is still lacking. Discovering and formalizing these invariants will provide a foundation for developing, and better understanding, effective methods for HSI. We propose two invariants underlying HSI for geometric-based swarms: (1) collective state is the fundamental percept associated with a bio-inspired swarm, and (2) a humans ability to influence and understand the collective state of a swarm is determined by the balance between the span and persistence. We provide evidence of these invariants by synthesizing much of our previous work in the area of HSI with several new results, including a novel user study where users manage multiple swarms simultaneously. We also discuss how these invariants can be applied to enable more efficient and successful teaming between humans and bio-inspired collectives and identify several promising directions for future research into the invariants of HSI.

Classifying swarm behavior via compressive subspace learning

Bio-inspired robot swarms encompass a rich space of dynamics and collective behaviors. Given some agent measurements of a swarm at a particular time instance, an important problem is the classification of the swarm behavior. This is challenging in practical scenarios where information from only a small number of agents may be available, resulting in limited agent samples for classification. Another challenge is recognizing emerging behavior: the prediction of swarm behavior prior to convergence of the attracting state. In this paper we address these challenges by modeling a swarms collective motion as a low-dimensional linear subspace. We illustrate that for both synthetic and real data, these behaviors manifest as low-dimensional subspaces, and that these subspaces are highly discriminative. We also show that these subspaces generalize well to predicting emerging behavior, highlighting that there exists low-dimensional structure in transient agent behavior. In order to learn distinct behavior subspaces, we extend previous work on subspace estimation and identification from missing data to that of compressive measurements, where compressive measurements arise due to agent positions scattered throughout the domain. We demonstrate improvement in performance over prior works with respect to limited agent samples over a wide range of agent models and scenarios.

Discovery and Exploration of Novel Swarm Behaviors Given Limited Robot Capabilities

Emergent collective behaviors have long interested researchers . These behaviors often result from complex interactions between many individuals following simple rules. However, knowing what collective behaviors are possible given a limited set of capabilities is difficult. Many emergent behaviors are counter-intuitive and unexpected even if the rules each agent follows are carefully constructed. While much work in swarm robotics has studied the problem of designing sets of rules and capabilities that result in a specific collective behavior, little work has examined the problem of exploring and describing the entire set of collective behaviors that can result from a limited set of capabilities. We take what we believe is the first approach to address this problem by presenting a general framework for discovering collective emergent behaviors that result from a specific capability model. Our approach uses novelty search to explore the space of possible behaviors in an objective-agnostic manner. Given this set of explored behaviors we use dimensionality reduction and clustering techniques to discover a finite set of behaviors that form a taxonomy over the behavior space. We apply our methodology to a single, binary-sensor capability model. Using our approach we are able to re-discover cyclic pursuit and aggregation, as well as discover several behaviors previously unknown to be possible with only a single binary sensor: wall following, dispersal, and a milling behavior often displayed by ants and fish.

Evolving and Controlling Perimeter, Rendezvous, and Foraging Behaviors in a Computation-Free Robot Swarm

Designing and controlling the collective behavior of a swarm often requires complex range, bearing sensors, and peer-to-peer communication strategies. Recent work studying swarm of robots that have no computational power has shown that complex behaviors such as aggregation and object clustering can be produced from extremely simple control policies and sensing capability. We extend previous work on computation-free swarm behaviors and show that it is possible to evolve simple control policies to form a perimeter around a target, rendezvous to a specific location, and perform foraging. We also demonstrate that simple manipulations of the environment can be used to control, these collective behaviors. The robustness and expressiveness of these behaviors, combined with the simple requirements for control and sensing, demonstrate the feasibility of implementing swarm behaviors at small scales or in extreme environments.

Balancing Human and Inter-Agent Influences for Shared Control of Bio-Inspired Collectives

Human interaction with bio-inspired collectives provides an interesting setting for studying shared control. A human will often have knowledge of global objectives and high-level plans, but the collective will often have more detailed lower-level knowledge about the particulars of the situation at hand. Thus it is important to understand how control can be appropriately shared between the human and the collective. We analyze human interaction with bio-inspired collectives using graph theory, and propose that there are two human-side elements that determine how well control is shared: span and persistence. We additionally propose that there is a collective-side element that determines how well control is shared: connectivity. We study two examples of shared-control between a human and a bio-inspired collective: shaping a spatial formation and causing a collective to switch between stable collective states. Our empirical results show that span, persistence, and connectivity combine to affect (1) how influence is shared between the human and the collective and (2) the resulting success of human-collective interactions.

Multiobjective Optimization for the Stochastic Physical Search Problem

We model an intelligence collection activity as multiobjective optimization on a binary stochastic physical search problem, providing formal definitions of the problem space and nondominated solution sets. We present the Iterative Domination Solver as an approximate method for generating solution sets that can be used by a human decision maker to meet the goals of a mission. We show that our approximate algorithm performs well across a range of uncertainty parameters, with orders of magnitude less execution time than existing solutions on randomly generated instances.

k-Agent Sufficiency for Multiagent Stochastic Physical Search Problems

In many multi-agent applications, such as patrol, shopping, or mining, a group of agents must use limited resources to successfully accomplish a task possibly available at several distinct sites. We investigate problems where agents must expend resources e.g. battery power to both travel between sites and to accomplish the task at a site, and where agents only have probabilistic knowledge about the availability and cost of accomplishing the task at any location. Previous research on Multiagent Stochastic Physical Search mSPS has only explored the case when sites are located along a path, and has not investigated the minimal number of agents required for an optimal solution. We extend previous work by exploring physical search problems on both paths and in 2-dimensional Euclidean space. Additionally, we allow the number of agents to be part of the optimization. Often, research into multiagent systems ignores the question of how many agents should actually be used to solve a problem. To investigate this question, we introduce the condition of k-agent sufficiency for a multiagent optimization problem, which means that an optimal solution exists that requires only k agents. We show that mSPS along a path with a single starting location is at most 2-agent sufficient, and quite often 1-agent sufficient. Using an optimal branch-and-bound algorithm, we also show that even in Euclidean space, optimal solutions are often only 2- or 3-agent sufficient on average.

Exact and Heuristic Algorithms for Risk‐Aware Stochastic Physical Search

We consider an intelligent agent seeking to obtain an item from one of several physical locations, where the cost to obtain the item at each location is stochastic. We study risk‐aware stochastic physical search (RA‐SPS), where both the cost to travel and the cost to obtain the item are taken from the same budget and where the objective is to maximize the probability of success while minimizing the required budget. This type of problem models many task‐planning scenarios, such as space exploration, shopping, or surveillance. In these types of scenarios, the actual cost of completing an objective at a location may only be revealed when an agent physically arrives at the location, and the agent may need to use a single resource to both search for and acquire the item of interest. We present exact and heuristic algorithms for solving RA‐SPS problems on complete metric graphs. We first formulate the problem as mixed integer linear programming problem. We then develop custom branch and bound algorithms that result in a dramatic reduction in computation time. Using these algorithms, we generate empirical insights into the hardness landscape of the RA‐SPS problem and compare the performance of several heuristics.