<title>Distributed robotic mapping of extreme environments</title>

Abstract

In the extreme environments posed by war fighting, fire fighting, and nuclear accident response, the cost of direct human exposure is levied in terms of injury and death. Robotic alternatives must address effective operations while removing humans from danger. This is profoundly challenging, as extreme environments inflict cumulative performance damage on exposed robotic agents. Sensing and perception are among the most vulnerable components. We present a distributed robotic system that enables autonomous reconnaissance and mapping in urban structures using teams of robots. Robot teams scout remote sites, maintain operational tempos, and successfully execute tasks, principally the construction of 3-D Maps, despite multiple agent failures. Using an economic model of agent interaction based on a free market architecture, a virtual platform (a robot colony) is synthesized where task execution does not directly depend on individual agents within the colony. MOTIVATIONMilitary Operations in Urban Terrain (MOUT) pose fierce constraints such as limited visibility, complex and expansive fortifications, limited intelligence, and the presence of native populations and other non-combatants that prohibit deployment of large forces [1,2]. Further, the use of asymmetric threats, e.g. biological and chemical agents, against both land forces and indigenous populations in urban settings is an increasing likelihood [3]. These conditions place land forces and noncombatants in a highly non-deterministic, dangerous, confrontational, and volatile environment. An effort to identify and improve the ability of ground forces to project sufficient force and safeguard non-combatants is underway. This program, called the MOUT Advanced Concept Technology Development (ACTD), focuses on improving operational effectiveness in urban areas [4,5].The development of robotics technology will enable minimally invasive and precise MOUT operations that reduce risk to both ground forces and non-combatants by removing soldiers from dangerous and sometimes confrontational tasks [6]. Potential tasks for robotic systems include mine sweeping, reconnaissance, security and monitoring presence, and communications infrastructure [7]. DARPA has undertaken the task of enabling distributed robotics technology to execute urban reconnaissance missions. As a part of this effort, DARPA's Software for Distributed Robotics (SDR) program is looking at revolutionary approaches to the development of multi-agent robotic systems within this task domain. Under the auspices of the SDR Program, our team is producing software technology designed to construct interior maps of urban structures using groups of homogenous mobile robots. This concept software will be demonstrated on surrogate, simplistic mobile robotic platforms and eventually ported to mission capable mobile robotic systems.Our approach to the problem focuses initially on the production of reliable, accurate, and fault-tolerant software by exploiting the redundancy inherent in a group of homogenous robots, ...

Opportunistic optimization for market-based multirobot control

“…in typically non-competitive environments) as opposed to competitive multirobot domains and competitive inter-agent interactions in domains such as E-commerce. Simulation results using this approach were produced by Dias and Stentz [3], and proven robot results were presented by Thayer et al [11], and Zlot et al [13]. A brief introduction to this approach is presented here.…”

“…Stentz and Dias [10] proposed a more capable market-based approach for multirobot coordination which aims to opportunistically introduce pockets of centralized optimal planning into a distributed system, thereby exploiting the desirable properties of both distributed and centralized approaches. Thayer et al [11], Gerkey and Mataric [4], and Zlot et al [13] have since produced market-based multirobot coordination results. Economic approaches are not without their disadvantages.…”

COLLABORATIVE EXPLORATION IN GRID DOMAINS - Constructive Conjecture of a Polynomial Time Complexity

“…(Hettiarachchi and Spears, 2005;Wagner and Bruckstein, 2001;Steels, 1990) or (Arkin, 1990;Mataric, 1992;Haynes and Sen, 1986) for biology inspired designs (behavior based control models, flocking and dispersing models and predator-prey approaches, respectively), (Gerkey and Mataric, 2002;Rabideau et al, 1999;Thayer et al, 2000) for economics applications and (Chevallier and Payandeh, 2000) for a physics inspired approach).…”

To Add with Caution —Decreasing a Swarm Robotics’ Efficiency by Imprudently Enhancing the Robots’ Capabilities

“…[1][2][3] or [4][5][6] for biology inspired designs (behavior based control models, flocking and dispersing models and predator-prey approaches, respectively), [7][8][9][10] for economics applications and [11] for a physics inspired approach).…”