Diana F. Spears

Distributed, Physics-Based Control of Swarms of Vehicles

We introduce a framework, called “physicomimetics,” that provides distributed control of large collections of mobile physical agents in sensor networks. The agents sense and react to virtual forces, which are motivated by natural physics laws. Thus, physicomimetics is founded upon solid scientific principles. Furthermore, this framework provides an effective basis for self-organization, fault-tolerance, and self-repair. Three primary factors distinguish our framework from others that are related: an emphasis on minimality (e.g., cost effectiveness of large numbers of agents implies a need for expendable platforms with few sensors), ease of implementation, and run-time efficiency. Examples are shown of how this framework has been applied to construct various regular geometric lattice configurations (distributed sensing grids), as well as dynamic behavior for perimeter defense and surveillance. Analyses are provided that facilitate system understanding and predictability, including both qualitative and quantitative analyses of potential energy and a system phase transition. Physicomimetics has been implemented both in simulation and on a team of seven mobile robots. Specifics of the robotic embodiment are presented in the paper.

Where are you

The ability of robots to quickly and accurately localize their neighbors is extremely important in swarm robotics. Prior approaches generally rely either on global information provided by GPS, beacons, and landmarks, or complex local information provided by vision systems. In this paper we provide a new technique, based on trilateration. This system is fully distributed, inexpensive, scalable, and robust. In addition, the system provides a unified framework that merges localization with information exchange between robots. The usefulness of this framework is illustrated on a number of applications.

Distributed robotics approach to chemical plume tracing

This paper presents an application of a physics-based framework for distributed control of autonomous vehicles. The autonomous swarm uses local information to self-organize into dynamic sensing and computation grids during localization of the source of a toxic plume. Using physics of fluid flow, we develop a new plume-tracing algorithm, and then use computational fluid dynamics simulations to show that the new approach outperforms the leading biomimetic competitors for this task.

An overview of physicomimetics

This paper provides an overview of our framework, called physicomimetics, for the distributed control of swarms of robots. We focus on robotic behaviors that are similar to those shown by solids, liquids, and gases. Solid formations are useful for distributed sensing tasks, while liquids are for obstacle avoidance tasks. Gases are handy for coverage tasks, such as surveillance and sweeping. Theoretical analyses are provided that allow us to reliably control these behaviors. Finally, our implementation on seven robots is summarized.

Swarms for chemical plume tracing

This paper presents a physics-based framework for managing distributed sensor networks of autonomous vehicles, e.g., robots, which self-organize into structured lattice arrangements using only local information. The vehicles remain in formation during obstacle avoidance and search for a chemical emitter that is actively ejecting a toxic chemical into the air. We discuss a new plume tracing algorithm, based on the principles of fluid physics, that outperforms the leading biomimetic competitors for this task.

Agent-based chemical plume tracing using fluid dynamics

This paper presents a rigorous evaluation of a novel, distributed chemical plume tracing algorithm. The algorithm is a combination of the best aspects of the two most popular predecessors for this task. Furthermore, it is based on solid, formal principles from the field of fluid mechanics. The algorithm is applied by a network of mobile sensing agents (e.g., robots or micro-air vehicles) that sense the ambient fluid velocity and chemical concentration, and calculate derivatives. The algorithm drives the robotic network to the source of the toxic plume, where measures can be taken to disable the source emitter. This work is part of a much larger effort in research and development of a physics-based approach to developing networks of mobile sensing agents for monitoring, tracking, reporting and responding to hazardous conditions.

Physicomimetics for Mobile Robot Formations

In prior work we established how physicomimetics can be used to self-organize hexagonal and square lattice formations of mobile robots. In this paper we extend the framework to moving formations, by providing additional theoretical analysis and showing how this theory facilitates the implementation of seven robots in a hexagonal formation moving towards a goal.

Two formal gas models for multi-agent sweeping and obstacle avoidance

The task addressed here is a dynamic search through a bound-ed region, while avoiding multiple large obstacles, such as buildings. In the case of limited sensors and communication, maintaining spatial coverage – especially after passing the obstacles – is a challenging problem. Here, we investigate two physics-based approaches to solving this task with multiple simulated mobile robots, one based on artificial forces and the other based on the kinetic theory of gases. The desired behavior is achieved with both methods, and a comparison is made between them. Because both approaches are physics-based, formal assurances about the multi-robot behavior are straightforward, and are included in the paper.

Robotic simulation of gases for a surveillance task

The task addressed here requires a swarm of mobile robots to monitor a long corridor, i.e., by sweeping through it while avoiding large obstacles such as buildings. In the case of limited sensors and communication, maintaining spatial coverage - especially after passing the obstacles - is a challenging problem. Note that the main objective of this task is coverage. There are two primary methods for agents to achieve coverage: by uniformly increasing the inter-agent distances, and by moving the swarm as a whole. This paper presents a physics-based solution to the task that is based on a kinetic theory approach; our solution achieves both forms of coverage. Furthermore, the paper describes how we transition from our original algorithm to an algorithm utilizing mostly local sensor information, the latter being more realistic for modeling robots. To determine how well our kinetic theory approach performs against a popular alternative controller, experimental comparisons are presented.

Foundations of swarm robotic chemical plume tracing from a fluid dynamics perspective

Purpose – In light of the current international concerns with security and terrorism, interest is increasing on the topic of using robot swarms to locate the source of chemical hazards. The purpose of this paper is to place this task, called chemical plume tracing (CPT), in the context of fluid dynamics.Design/methodology/approach – This paper provides a foundation for CPT based on the physics of fluid dynamics. The theoretical approach is founded upon source localization using the divergence theorem of vector calculus, and the fundamental underlying notion of the divergence of the chemical mass flux. A CPT algorithm called fluxotaxis is presented that follows the gradient of this mass flux to locate a chemical source emitter.Findings – Theoretical results are presented confirming that fluxotaxis will guide a robot swarm toward chemical sources, and away from misleading chemical sinks. Complementary empirical results demonstrate that in simulation, a swarm of fluxotaxis‐guided mobile robots rapidly conver...