Proceedings of the 2002 IEEWRSJ Intl. Conference on Intelligent Robots and Systems

EPFL. Lausanne, Switzerland - October 2002

Development of an Immunology-Based Multi-Robot Coordination Algorithm for Exploration and Mapping Domains

Scott M. Thayer’ and Swya P. N. Sin&’

’ Robotics Institute, Carnegie Mellon University, Pinsburgh, Pennsylvania, USA, sthaver63ri.cmu.edu Center for Design Research, Stanford University, Stanford, Calfornia, USA, suns~c&.sianford.edu

Abstract This paper presents a new concept called the Immunol- ogy-derived Distributed Autonomous Robotics Architec- ture (IDARA) for the manipulation of “kilorobots” (large multi-robot colonies) modeled on the actions of the human immune system. The paperpresents the de- velopment ofthe IDARA algorithm for the control and coordination ofkilorobotsfor robot exploration task in four mapping scenarios. As characterized via computer simulations with robot populations of up to 1,500, IDARA-based exploration proved to be an eflcient, ro- bust, and compact method for large-scale multirobot control that combines the speed of reflexive methoak with the precisian ofdeliberative control.

1. Introduction

Remotely-operated robots have found wide applications in environments which are either too hazardous or inac- cessible to humans. These applications include, for ex- ample, exploration in outer space (e.g., the Mars Rover) and lethally-radioactive environments (e.g., Chemobyl), search and rescue operations for humans in inaccessible locations (e.g., after disasters such as collapsed build- ings due to earthquakes, etc.), and for locating unex- ploded ordnances such as mines.

The Immunology-derived Distributed Autonomous Robotics Architecture (IDARA) has been developed for application in kilorobotics - large-scale, heterogeneous multi-robot teams having populations in the thousands [I]. Motivated by recent advances in miniature robotic platforms, IDARA considers the exploration of variable, dynamically changing environments [2]. To fully serve an operator’s varying needs and fully utilize all informa- tion available, the coordination method takes into ac- count (but does not require) available a priori informa- tion and distributes resources such that the environment is fully characterized.

In nahwe, we observe several cases where large populations work cooperatively in a productive ~ N I M

to achieve complex goals far more eficiently than may be accomplished individually. Many of these groups of robots or agents consist of large populations that coor- dinate and cooperate on tasks as needed in the presence

of substantial complexity resulting from various factors including environmental uncertainty, noisy inputs, ad- versarial agents, and external threats. One prime exam- ple of this type of system in nature is the human im- mune system. The immune system is a remarkable example of a highly scalable distributed control and co- ordination system [3]. The immune system is able to control and coordinate a massively-scaled distributed- object environment in a measured, decisive, dynamic, and seamless manner to deter bacterial or viral threats. For example, the immune system coordinates over a tril- lion lymphocyte cells, which together involve about IO2’ (100 quintillion) antibody molecules. The immune system also responds dynamically to changing macro- scopic and microscopic conditions. As an example, in the time it takes to make a cup of coffee, the immune system produces 8 million new lymphocytes and re- leases nearly a billion antibodies. The immune system acts like a protective force that continually monitors the hioenvironment and, depending upon a perceived threat to the body, activates the necessary multi-agent control system and responses [4,5].

Just as the nervous system can serve as a powerful construct for building deterministic intelligent systems (e.g., neural net classifiers), the immune system serves as a model for the design of robotlsoftware architectures that respond and perform learning via a stochastic proc- esses [6]. Although its fundamental goal is patho- gednon-pathogen selection and response, the immune system model gives insights to several methods for autonomous multi-robot colony operations based upon the native exploration methods found within the human immune system [3]. By using this as a basis, kilorobot- ics is able to more fully exploit the comparative advan- tages inherent in autonomous multi-robot systems, namely: parallel execution, redundant operations, in- creased reliability, and robustness to point failures. The increased number of robots available gives the search method extm degrees of freedom in finding the object under consideration, be it a victim, unexploded ord- nance, goal-point, or terrain feature.

Here we present an overview of IDARA followed by its application to a variety of simulated exploration and mapping situations.

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2. Previous Work & Immunology Overview

Many popular multi-robot control systems available for exploration are based on centralized operations. For example, Albus and Stentz both base their results on a centralized, hierarchical approach to the control of multi-robot systems [7, E]. While easier to imple- ment, the application and scaling of this approach has been limited by the large computational and commu- nications burden associated with its (centralized) op- erations [9]. A second approach is to use a highly dis- tributed robot system that communicates via a series of peer-to-peer or implicit communications systems [lo]. Often based on the use of biologically-inspired behavior control mechanisms, they do not provide a convenient method for integrating deliberative proc- essing and are applicable to select domains [ 1 I].

Hybrid approaches have been developed to com- bine the qualities of both deliberative and behavioral methods. These approaches resolve many of the prob- lems associated with these two architectures, however, have limited scalability due to increased system com- plexity [9]. Examples include Dias and Sentz's mac- roeconomic approach and Feddema's applied statisti- cavgraph-theoretic approaches to coordinating hund- reds to thousands of cooperative robotic agents [7,12].

A alternative approach for coordinating robot colo- nies in dynamic environments having a relatively slow rate of change is to repeatedly apply a method with guaranteed coverage, such as [13], at a sufficiently high frequency. The most significant problem with this approach is that it essentially entails that the fre- quency of any environmental change is less than the bandwidth (or "refresh frequency") of the method he- ing considered. Also, these methods make extensive use of the steady-state assumption. Thus, it is possible that transient effects of a dynamic environment could present unforeseen stability difficulties to this method.

Immune System Overview A brief overview of the immune system is pre-

sented as a background on the operations and interac- tions on which the IDARA metaphor is based. The human immune system works on two levels with the general goal of pathogen control: a general response mechanism (i.e., innate immunity) that is not directed at any specific pathogen and a specific, anti-body me- diated response (i.e., acquired immunity) that encom- passes many of the pattern recognition and situational memory aspects that are a core aspect of the human immune system. Figure 1 illustrates the specificity ladder between response and effectiveness.

As elaborated in [9], the main thrust of this research is not to mimic the immune system's operation, but to use it as a model for the construction of methods that coordinate large numbers of largely-independent agents. Implemented via a probabilistic approach, this

technique uses a layered hierarchy to mediate behav- iors, update actions based on recognized patterns, and perform a variety of "fuzzy"tasks [14, 151.

A

~ a R r F o n u c o G l Fig. I: Cascading Response Model for Immune' System Re-

sponss (Rspmc h m e s more specific and advanced with time)

3. LDARA Architecture Design

The IDARA design focuses on the solution of macro- scopic guidance and coordination issues, rather than specific individual control. The IDARA software ar- chitecture is diagramed in Figure 2. While the current research has emphasized the use of these algorithms towards the development of a first-order distributed robotics methodology, the layered-response intelli- gence model and robustness inherent to IDARA can be extended to other robot problem domains.

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(a) Seasor data me used for rapidly pmccssing a general, reactive mechanism, @) multiple sensors are combined in the perception stage, (c) this information is then used with h o w responses to give m o n complex triggered response, (d) analysis along with learned BSFD are lead w specific responses.

IDARA's multi-faceted response mechanism repre- sents a significant advance over traditional algorithms; in that, IDARA maps the different aspects of the im- mune system to various modules and tiers of the re- sponse ladder and not to actions or cextain robots. Some of these advances are as follows:

GreaterJlexibilify and ease of implementation - IDARA's modular framework places objects of higher specificity into separate modules which fo- cuses the scope and nature of each response class.

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Use of “histamines” (one-way broadcasting communications) in response to perceived threats -Analogous to CB radio, this communication model is simpler than point-to-point techniques and allows the signals of one robot to be quickly interpreted by neighboring robots without any central coordination or synchronization; however, it does not guarantee error-free signal transfer. Third-party robots or systems can monitor the communications - The broadcasting method al- lows the signals emitted to be “tapped” without impact by any system capable of receiving and decoding them. For example, this allows map- ping data to be transmitted to near-by operators or for directing specialized response mechanisms. Robust to errant signals - The tiered architecture along with the arbitration module helps make a robot’s actions to errant signals by factoring higher-level commands that (presumably) will not change as quickly to errant sensor inputs. The failure of an individual (disposable) agent (or “antigen’) i s not detrimental to the entire system. IDARA’s distributed nature, by design, does not place responsibility of a particular task on any in- dividual unit. Failures in kilorobotic populations, by definition, result in a minimal impact on the entire system (e.g., IO failures would be less than 1%). Also, it is possible that a unit failure may actually be group beneficial, especially when this provides information not otherwise obtainable. The architecture yields a mobile, robust, and adaptive control method IDARA uses a well- defined hierarchy of control based on specificity. This can quickly react; yet combine the functions and critical mass of robots to solve complex tasks. The architecture can be more economically viable as standord. fault intolerant components could be used Since the architecture is fault tolerant, the hardware and software do not need to be “bard- ened,” rigorously tested, nor true real-time.

By separating tasks with respect to specificity and interactions, many robotics tasks can be encoded in the IDARA framework. In general, the task needs to be decomposed into “antigens” and “cells.” Where “antigens” are the principal object of the robotic activ- ity that needs to be addressed (e.g., unexplored area, mines, time, costs, etc.) and “cells” represent distrib- uted responses varying from general to specific. Sens- ing modalities are integrated as the mecbanism for having “cells” recognize “antigens.” Manipulation mechanisms (of varying specificity) are encoded as methods for addressing a “recognized” threat and are controlled by a corresponding level of response.

While the IDARA architecture has a number of strengths, especially in the coordination and control of large robot colonies, it is not perfect. One weakness is that agents initially base interaction on Brownian mo-

tion until an antigen is found locally and then use local gradient-optimization to follow the signals from initial interactions. This, however, predicates that there is an initial interaction between the two effectors. Thus, this architecture needs an inherent “critical mass” and may not operate well in small populations. Further, gradient techniques are only locally optimal. Thus, in order to obtain a highly (and perhaps globally) optimal solution, IDARA needs to be somewhat random in its initial motion so that it is fairly well distributed.

4. Exploration & Mapping Simulation

An exploration and mapping (E&M) simulation was used to characterize the DARA kilorobotics architec- ture. This task was chosen because it presents a clas- sic “hard” multi-robot coordination problem that needs reactive and deliberative processing and for which al- ternative multi-robot processing metbods exist. Fur- ther, the world was modeled as being quasi-static, which should present a least beneficial for IDARA.

E&M was implemented by focusing on the general and triggered response mechanisms for recruitment and coordinating actions as this was computationally simpler and placed the bulk of the processing in the general (and more reactive) layers of the architecture,

The robots were modeled with a classifyng prox- imity sensor and a stereo radio beacon for differential broadcasting communications. More advanced sens- ing modalities can be added and are discussed in [8].

The “general” responses were modeled as a random exploration of an area (when possible) using a local gradient of the beacons broadcast. The ‘’triggered” re- sponses were constructed to emit a beacon and initiate wall following when the robot detected a wall. The deliberative layers used a unit’s action history to de- termine patterns (such as prolonged wall following) and to respond to these observations (e.g., by ran- domly moving to a new location). The simulator did not share the global state or absolute robot positions.

The map was constructed by a centralized, thud party system that used multiple points around the pe- riphery to “tap” the beacon signals. The system then back traced the signal and used its estimate of the source position to fill an occupancy grid. Operator in- puts are integrated by varying the initial state of the antigen map, such that antigens were concentrated in locations of operator interest. For example, if no in- formation is available, the map would be uniformly distributed; by comparison, if the system was in- structed to explore the center, then the antigens are distributed in a “bulls-eye” pattern. The initial pattern is then sent to the robots (via an initializing data- transfer broadcast) before they are deployed.

The simulation also accounted for several “practi- calities,” such as false-positive and false-negative sen- sor noise (up to lo%), robot failure (1% probability of failure per step), and broadcast errors (1% error rate).

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5. Simulation Results

The IDARA-based search method was simulated to validate this architecture and to characterize its re- sponse mechanisms and related mannerisms. The simulation tested five types of distribution by skewing the antigen generation method as described earlier. The following initial patterns were tested and their mean results shown:

Center - A Gaussian at the center with 0.=~~=,=50 cells units and x =y=grid center Perimeter - Antigens spread towards the edges, essentially an inverse of the Center configuration Random - A random distribution in both x and y Uniform -Antigen locations spaced uniformly. Side - A Gaussian centered along the first col- umn, such that y=O and x=p’dcenter As seen in the visitation map (Figure 3), IDARA

coordinate robots to proceed towards and then explore the goal area. Figure 4 shows the performance and coverage strategy for the ID--based exploration

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For completely unlolown environments a random or uniform exploration strategy provides the most effec- tive method for exploration. However, when priors are available (and can be encoded in the distribution of the “antigens”) the IDARA method satisfies user’s goals while maintaining global exploration at the cost of reduced coverage to non-specified areas.

The simulation also included simulation of random and raster scanning (also referred to as Boustrophedon coverage) as these methods also act as a means of per- forming exploration and mapping tasks using kilorc- botic colonies [13]. As expected, these algorithms re- sult in significantly different converge patterns and system performance.

Figure 5 shows the percentage of wall features in the environment found as a function of the number of iterations executed. While the simulations show that IDARA maps more of wall features, it also shows that its performance is significantly non-linear. This can be attributed to IDARA’s recruitment mechanisms, which have degrading performance when new features (and their associated signals become sparse. Figure 6, shows the relative computational performance of IDARA and raster scanning algorithms with respect to the random scanning method. This was done to gauge the computational performance of these methods inde- pendently of the PC-hardware used to run the simula- tor. This graph shows that IDARA is competitive with the raster-scanning methods and use less than 50% more time than a random motion method.

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Fig. 5 : F e a m Recovely (Number of well feaolrer found relative to random method)

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6. Conclusions

The results, detailed in [2], showed that the IDARA framework is a promising technique for coordinating large populations of heterogeneous robots in highly unstructured environments. Using the human immune system analogues of a specificity response ladder and clonal expansion as a guide, the IDARA coordination architecture was developed along with a method for kilorobotic exploration and mapping. In general, the results of the simulation were as hypothesized and show that the IDARA methods were able to efficiently coordinate 1,500 robots in a complex task domain, such as the directed-stochastic search needed for per- forming search and rescue operations. Furthermore, IDARA‘s ability to perform searches in noisy, non- uniform environments was used in reverse to perform rescue operations after fmding the goal (such as a vic- tim in distress) because an efficient method of return- ing hack to the start area in a dynamic environment may not necessarily be to back track the path taken.

The IDARA system builds upon immunology mod- els and other related concepts and results in a directed, hut flexible, system that mimics the nature of the im- mune system’s control structure. In conclusion, the IDARA method will allow kilorobotics to be able to more fully exploit the comparative advantages inber- ent in autonomous multi-robot systems, namely: paral- lel execution, redundant operations, increased reliabil- ity, and robustness to noise.

Finally, future versions of IDARA will incorporate three-dimensional terrain searches and comparisons to other applicable methods capable of scaling to multi- robot operation. This ’ype of architecture will play a significant role as the manufacture and assembly of kilorobotic colonies is realized. Recent advances in micro-robotics and MEMS platforms will allow the mass-production and deployment of arrays of simple, miniscule robots. IDARA (or its successor upgrades) will help provide the algorithms for their coordination and control.

7. References

I .

2.

D. Gage, private communications on the term “kiloro- bop, June 2001. S. Sin& and S. Thayer, ARMS (Autonomow Robots for Military Sysfems), Robotics InstifUte Tech. Report: CMU-RI-TR-OI-16, Camegie Mellon Univ., July 2001. S. Forrest and S. Hofmeyr, “Immunity by Design: An Artificial Immune Svstem”. in Proceedines of the Ge-

3. I

netic and Evolutionary Computation Conference (GECCO), 1999, pp. 1289-1296.

4.

5.

N. K Jme, “The~lmmune System”, Scientific Ameri- can, vol. 259, July 1973, pp. 52-60 A. Cuyton and 1. Hall, Tartbook of Medical Physiology. W.B. Saunders Co., Ninth edition, 1996.

6. D. Dasgupta, “An Overview of Artificial Immune Sys- tems and Their Applications”, Art@ciol Immune Sys- tems and ’their Applications, D. Dasgupta, Ed., Springer, 1999, pp. 3-21. A. Stentz and B. L. Bnunitt, “Dynamic Mission Plan- ning for Multiple Mobile Robots”, Proc. of the IEEE International Conference on Robotics and Automation, No. 3,pp. 2396-2401, 1996. S. Singh and S. Thayer, “Immunology Directed Meth- o d s for Dishibuted Robotics: A Novel, Immunity- Based Architecture for Robust Control & Cwrdina- tion”, SPIE: MobileRobotsXVI. wl. 4573, Nov. 2001. S. Shgh and S . Thayer, “A Foundation for Kilorobotic Exploration.” Proc. of the IEEE Congas on Ewlu- tionary Computation, May 2002.

10. RC. Arkin, and T. Balch, ‘AURA: Principles and hac- tice in Review”, J. ofExperimentol& Theoretic01 Arti- ficialktelligence, Vol. 9, No. 213, 1997, pp.175-188.

11. M. J. Mataric, ‘%sues and Approaches in the Design of Collective Autonomous Agents”, Robotic3 ond AutonomousSysfemi, vol. 16, 1995, pp. 321-331.

12. D.A. Scboenwald, I. T. Feddema, and F.J. Oppel, “De- centralized Control of a Collective of Autonomous Ro- botic Vehicles,” Proc. of the 2001 American Control Conference, Vol. 3, pp. 2087-2092.2001.

13. H. Chose< D. Latimer IV, et al., ‘Towards sensor based coverage with robot teams”, in Pmc. of the ht. Con- ference on Robotics & Automation, May 2002.

14. D. Dasgupta and N. AttobX)kine, “Immunity-based systems: A survey” in hoc. of the ht. Conference on Systems, Man, & Cybernetics, vol.1, pp.369-374, 1997.

15. D. Lee, H. Jun, et al, “Artificial Immune System for Realization of Cooperative Strategies and Group Be- havior in Collective Autonomous Mobile Robots”, In Pmc. of the Fourth Int Symposium on Artificial Life and Robotics (AROB), January 1999.

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