1. F 1 ← y = x 2 F 2 ← y = 0.05 sin(10 x) F1 → F2F1 → F2F1 → F2F1 → F2 F1 → F2F1 → F2F1 → F2F1 → F2 To manage the robot formation, a graphical user interface will be developed that will provide a human operator with a visualization of the formation, as well as information on the state of each individual robotic unit. If the robots are not initially put into a formation, then an auctioning method is required so that a neighborhood can be established dynamically. We have identified the potential for different categorizations of formations, including those that are defined by multiple functions and those that generate erroneous neighbors. After successfully showing a proof-of-concept in a simulated environment, it will be implemented and tested on a modest number of physical robots, proving that the approach is viable in real space. For more information, please visit http://roboti.cs.siue.edu/projects/formations. http://roboti.cs.siue.edu/projects/formations In April 2000, NSF and NASA met to discuss harvesting solar power in space to help meet future energy needs. One solution that received considerable attention was the use of robots to form a solar reflector. Imagine the space shuttle releasing thousands of robotic units, each with a piece of the reflector attached. These robots then navigate themselves to form a large parabolic structure, which is then used to harvest solar energy (Bekey, et al 2000). How can this swarm, or massive collection that moves with no group organization, coordinate to form an organized, global structure, or formation? Once organized, how can this formation be effectively controlled? Algorithms for Control and Interaction of Large Formations of Robots Ross Mead and Jerry B. Weinberg Introduction This approach to the autonomous control of creating and maintaining multi-robot formations is similar to work done in coordinating formations of Earth-bound, mobile robots (Fredslund, et al 2002 & Balch, et al 1998). This work has been inspired by biological and organizational systems, such as geese flying in formation. A variety of work has also been done in applying reactive control structures to generate emergent group behaviors. A digital hormone model, inspired by biological cell interactions, has also been proposed for robotic organization (Shen, et al 2004). Background Southern Illinois University Edwardsville A desired formation, F, is defined as a geometric description (i.e., mathematical function). A human operator chooses a robot as the seed, or starting point, of the formation. Formation Control F ← y = a x 2 seed Originating at the seed, calculate a relationship vector from c, the formation-relative position (x i, y i ) of a robot i, and the intersection of the function F and a circle centered at c with radius r, where r is the distance to maintain between neighbors in the formation. 1 1 2 2 Relationships and states are communicated locally in the seed’s neighborhood, which propagates changes in each robot’s neighborhood in succession. Using sensor readings, robots attempt to acquire and maintain the calculated relationships with their neighbors. 3 3 Despite only local communication, the calculated relationships between neighbors result in the overall organization of the desired global structure. 4 4 F ← y = a x 2 c ← (x i, y i ) r 2 ← (x-c x ) 2 + (y-c y ) 2 Future Work The approach of this project is to treat the formation as a type of cellular automaton, where each robotic unit is a cell. A robot’s behavior is governed by a set of rules for changing its state with respect to its neighbors. By designating a small percentage of robots as “seeds”, human intervention would cause state changes directly, instigating a type of chain reaction in the formation. Objectives A movement command sent to a single robot would cause a chain reaction in neighboring robots, resulting in a global transformation. 5 5 To change a formation, a seed robot is simply given the new geometric description and the process is repeated. 6 6 F ← y = 0 seed Balch T. & Arkin R. 1998. “Behavior-based Formation Control for Multi-robot Teams” IEEE Transactions on Robotics and Automation, 14(6), pp. 926-939.Balch T. & Arkin R. 1998. “Behavior-based Formation Control for Multi-robot Teams” IEEE Transactions on Robotics and Automation, 14(6), pp. 926-939. Bekey G., Bekey I., Criswell D., Friedman G., Greenwood D., Miller D., & Will P. 2000. “Final Report of the NSF-NASA Workshop on Autonomous Construction and Manufacturing for Space Electrical Power Systems”, 4-7 April, Arlington, Virginia.Bekey G., Bekey I., Criswell D., Friedman G., Greenwood D., Miller D., & Will P. 2000. “Final Report of the NSF-NASA Workshop on Autonomous Construction and Manufacturing for Space Electrical Power Systems”, 4-7 April, Arlington, Virginia. Fredslund J. & Mataric M.J. 2002. “Robots in Formation Using Local Information”, The 7th International Conference on Intelligent Autonomous Systems, Marina del Rey, California.Fredslund J. & Mataric M.J. 2002. “Robots in Formation Using Local Information”, The 7th International Conference on Intelligent Autonomous Systems, Marina del Rey, California. Shen W., Will P., Galstyan A., & Chuong C. 2004. “Hormone-Inspired Self- Organization and Distributed Control of Robotic Swarms”, Autonomous Robots, 17, pp. 93-105.Shen W., Will P., Galstyan A., & Chuong C. 2004. “Hormone-Inspired Self- Organization and Distributed Control of Robotic Swarms”, Autonomous Robots, 17, pp. 93-105. References Simulation F ← y = a x 2 c ← (x i, y i ) r 2 ← (x-c x ) 2 + (y-c y ) 2 rr c rr F ← y = x √3 F ← y = -x √3 F ← y = 0 AAAI-06