Dimitri V. Zarzhitsky

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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.

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.

Artificial physics for mobile robot formations

In prior work we established how artificial physics can be used to self-organize swarms of mobile robots into hexagonal formations. In this paper we extend the framework to moving formations, by providing additional theoretical analysis that facilitates the implementation of seven robots in a hexagonal format ion moving towards a goal.

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...

Swarm Approach to Chemical Source Localization

We present a physics-based approach to the localization of chemical sources with autonomous swarms. Robotic vehicles with short-range sensors use local pair-wise interactions to self-assemble into structured lattice formations, serving as distributed sensor and computational meshes. The robots use fluid flow information to navigate toward the chemical emitter. We develop a new search algorithm from first principles of fluid mechanics that outperforms the leading biomimetic competitors for the chemical source localization task. A validation of the scalability of our solution via simulation of plume and vehicle dynamics is given

A Fluid Dynamics Approach to Multi-Robot Chemical Plume Tracing

This paper presents a novel chemical plume tracing algorithm executed by a distributed network of mobile sensing robots that measure the ambient fluid velocity and chemical concentration. The algorithm drives the robotic network to the source of the toxic plume, where measures can be taken to remove or extinguish the source emitter.

Experimental studies of swarm robotic chemical plume tracing using computational fluid dynamics simulations

Purpose – The purpose of this paper is to describe a multi‐robot solution to the problem of chemical source localization, in which a team of inexpensive, simple vehicles with short‐range, low‐power sensing, communication, and processing capabilities trace a chemical plume to its source emitterDesign/methodology/approach – The source localization problem is analyzed using computational fluid dynamics simulations of airborne chemical plumes. The analysis is divided into two parts consisting of two large experiments each: the first part focuses on the issues of collaborative control, and the second part demonstrates how task performance is affected by the number of collaborating robots. Each experiment tests a key aspect of the problem, e.g. effects of obstacles, and defines performance metrics that help capture important characteristics of each solution.Findings – The new empirical simulations confirmed previous theoretical predictions: a physics‐based approach is more effective than the biologically inspir...