L-ALLIANCE: a mechanism for adaptive action selection in heterogeneous multi-robot teams
In practical applications of robotics, it is usually quite difficult, if not impossible, for the system designer to fully predict the environmental states in which the robots will operate. The complexity of the problem is further increased when dealing with teams of robots which themselves may be incompletely known and characterized in advance. It is thus highly desirable for robot teams to be able to adapt their performance during the mission due to changes in the environment, or to changes in other robot team members. In previous work, we introduced a behavior-based mechanism called the ALLIANCE architecture -- that facilitates the fault tolerant cooperative control of multi-robot teams. However, this previous work did not address the issue of how to dynamically update the control parameters during a mission to adapt to ongoing changes in the environment or in the robot team, and to ensure the efficiency of the collective team actions. In this paper, we address this issue by proposing the L-ALLIANCE mechanism, which defines an automated method whereby robots can use knowledge learned from previous experience to continually improve their collective action selection when working on missions composed of loosely coupled, discrete subtasks. This ability to dynamically update robotic control parameters provides a number of distinct advantages: it alleviates the need for human tuning of control parameters, it facilitates the use of custom-designed multi-robot teams for any given application, it improves the efficiency of the mission performance, and It allows robots to continually adapt their performance over time due to changes in the robot team and/or the environment. We describe the L-ALLIANCE mechanism, present the results of various alternative update strategies we investigated, present the formal model of the L-ALLIANCE mechanism, and present the results of a simple proof of concept implementation on a small team of heterogeneous mobile robots.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States); Office of Naval Research, Washington, DC (United States)
- DOE Contract Number:
- AC05-84OR21400
- OSTI ID:
- 211400
- Report Number(s):
- ORNL/TM-13000; ON: DE96003596; CNN: N00014-86-K-0685; N00014-85-K-0124
- Resource Relation:
- Other Information: PBD: Nov 1995
- Country of Publication:
- United States
- Language:
- English
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