CS594, Section 30682: Distributed Intelligence in Autonomous Robotics Spring 2003 Tuesday/Thursday 11:10 12:25 http://www.cs.utk.edu/~parker/courses/cs594-spring03 Instructor: Dr. Lynne E. Parker ½ TA: William Duncan
Outline Overview syllabus and class policies Overview of Distributed Robotics Introduction to topics to be studied this semester
Overview of Syllabus and Class Policies (See handout)
Overview of Distributed Robotics
Research in Multi-Robot Systems: Growing Rapidly
How Rapidly is this Research Growing? To investigate, conducted an INSPEC* Search: Yearly query, 1979-2001 Searched for articles including at least one of the following terms: Multi-robot Multirobot Cooperative robot Collaborative robot Distributed robot * Citation index for physics, electronics, and computing
Results of INSPEC Search Show Enormous Growth in Multi-Robot Systems Research 350 300 # Articles in INSPEC 250 200 150 100 50 0 1975 1980 1985 1990 1995 2000 2005
What are Reasons for Enormous Growth? Advances in individual autonomous robotics Advances in understanding of complex systems Increased computational capabilities Many potential application domains RoboCup influence Etc...
Military: Surveillance and Surveillance reconnaissance and Reconnaissance U.S. Department of Energy: Hazardous Hazardous site Waste cleanup Cleanup Many Potential Application Domains for Multi-Robot Teams NASA: Space exploration Space Exploration Industry: Mining, construction,... Mining
Eight Primary Areas of Research in Distributed Robotics 1. Biological Inspirations 2. Motion Coordination 3. Communication 4. Object Transport and Manipulation 5. Reconfigurable Robotics 6. Architectures, Task Planning, and Control 7. Localization, Mapping, and Exploration 8. Learning For each area: Different extents of study Many excellent solutions Open research issues remain in all areas
Relative Concentration in Each Area of Multi-Robot Systems 500 450 400 350 300 250 200 150 100 50 (Values based upon INSPEC search for years 1979-2001) # Articles in INSPEC 0 1 2 3 4 5 6 7 8 Learning Localization, etc. Architectures, etc. Reconfigurable robots Manipulation Communication Motion planning Biological Inspirations
1. Biological Inspirations Objective: Study biological systems to achieve engineering goals Communication Auditory, chemical, tactile, visual, electrical Direct, indirect, explicit, implicit Roles Strict division vs. loose assignments Hierarchies Absolute linear ordering, partial ordering, relative ordering Purpose: reduction in fighting, efficiency Territoriality Reduces fighting, disperses group, simplifies interactions Social facilitation/sympathetic induction Allows for efficient use of resources Imitation Complex mechanism for learning Leaf cutter ants Bee colony
Example Movie Swarm-type dispersion: Andrew Howard, Univ. of Southern California
Biological Inspirations: Future Directions High-impact, impact, open research issues: Can we actually demonstrate large numbers of physical robots (>=100) working together? How do we use autonomous, dynamic physical interconnectivity (like insects) to enable collective navigation over challenging terrains?
2. Motion Coordination Objective: enable robots to navigate collaboratively to achieve spatial positioning goals Issues studied: Multi-robot path planning Traffic control Formation generation Formation keeping Target tracking Target search Multi-robot docking Parker Murphy
Multi-Robot Motion Control: Keeping-Formation Our research has led to new insights into achieving global control via local interactions: fundamental limitations in small team members achieving globally optimal solutions with limited global knowledge. L. E. Parker, Designing control laws for cooperative agent teams, Proc. of IEEE International Conference on Robotics and Automation, 1993. S. Carpin, L. E. Parker, Cooperative Leader Following in a Distributed Multi-Robot System, IEEE International Conference on Robotics and Automation, 2002.
Motion Coordination: Future Directions High-impact, impact, open research issue: How do we generate robust motion coordination for 3D environments in previously unknown terrain amidst dynamically moving obstacles?
3. Communication Objective: Enable robots to exchange state and environmental information with a minimum bandwidth requirement Issues studied: Explicit vs. Implicit Local vs. Global Impact of bandwidth restrictions Awareness Variety of mediums: radio, IR, chemical scents, breadcrumbs, etc. Balch and Arkin Jung and Zelinsky
Communication: Future Directions High impact, open research issues: How do we enable multi-robot teams to work reliably in faulty communication environments? Can we develop passive action recognition capabilities in robot teams? How do we enable multi-robot teams to set up dynamic, mobile communications networks?
4. Object Transport and Manipulation Objective: Enabling multiple robots to collaboratively push, move, or carry objects that cannot be handled by one robot alone Issues studied: Constrained vs. unconstrained motions Two-robot teams versus "swarm"-type teams Compliant vs. non-compliant grasping mechanisms Cluttered vs. uncluttered environments Global system models vs. distributed models Etc. Kube Parker
Cooperative Baton Passing Our research has shown how mobile robots can perform tightlycoupled cooperation embedded in a loosely-coupled cooperative architecture.
Object Transport and Manipulation: Future Directions High-impact, impact, open research issue: Can we achieve cooperative transport over uneven outdoor terrains?
5. Reconfigurable Robotics Objective: Obtain function from shape, allowing modules to (re)connect to form shapes that achieve desired purpose Earliest research included reconfigurable/cellular robotics Several newer projects: Various navigation configurations (rolling track, spider, snake, etc.) Lattices, matrices (for stair climbing, object support, etc.) Castano et. al.
Movies of PolyBot (Mark Yim, Xerox PARC) Stair Climbing Tricycle pedaling Porous material climbing
Reconfigurable Robotic: Future Directions High-impact, impact, open research issues: How do we facilitate autonomous (vs. manual) reconfiguration? How can we extend concepts to practical applications?
6. Architectures, Task Planning, and Control Objective: Development of overall control approach enabling robot teams to effectively accomplish given tasks Issues studied: Action selection Delegation of authority and control Communication structure Heterogeneity versus homogeneity of robots Achieving coherence amidst local actions Resolution of conflicts
Example 1: Mock hazardous waste cleanup -- Part I L. E. Parker, On the design of behavior-based multi-robot teams, Advanced Robotics, 1996. L. E. Parker, ALLIANCE: An architecture for fault tolerant multi-robot cooperation, IEEE Transactions on Robotics and Automation, 1998. L. E. Parker, Evaluating success in autonomous multi-robot teams: Experiences from ALLIANCE architecture implementations, Journal of Theoretical and Experimental Artificial Intelligence, 2001.
Example 1: Mock hazardous waste cleanup -- Part II
Example 1: Mock hazardous waste cleanup -- Part III
Architectures, Task Planning, and Control: Future Directions High-impact, impact, open research issues: Can a general architecture be developed that is easily tailored to fit a wide range of multi-robot systems and applications? Or, are specialized architectures required? How do we quantitatively measure and compare different multi-robot team architectures?
7. Localization, Mapping, and Exploration Objective: Enable robot teams to cooperatively build models of their environment, or to accomplish spatial tasks requiring knowledge of other robot positions Issues studied: Extension of single-robot mapping approach to multirobot teams Hardware, algorithms for robot positioning Sonar vs. laser vs. stereo imagery vs. fusion of several sensors Landmarks vs. scan-matching
Example Movie of Distributed Localization (Univ. of Southern California)
Localization, Mapping, and Exploration: Future Directions High-impact, impact, open research issues: How do we reliably localize, map, and explore in 3D environments (especially with limited or unavailable DGPS)? As we extend to cooperative localization, how do we determine the limit of effectiveness of adding more robots to the team? How do we merge information efficiently, in light of communication limitations, noisy sensors, and localization errors?
8. Learning Objective: Enable multi-robot teams to adapt or develop own control approach to solve a task with minimal human operator input Application domains studied: Predator/prey Box pushing Foraging Multi-robot soccer Cooperative target observation Parker
Example: Adaptive Box Pushing L. E. Parker, Adaptive Heterogeneous Multi-Robot Teams, Neurocomputing, 1999.
Learning: Future Directions High-impact, impact, open research issues: Can we generate efficient learning in inherently cooperative tasks? How do we deal with credit assignment in groups -- determining individual robot contributions from group performance?
New Research Areas Receiving Increased Attention Robot-Agent-People teams as peers Heterogeneous teams Physical demonstrations of large numbers of robots (>=100)
Summary of Introduction to Topic Dramatic increase in research publications in multi-robot systems in last 5 years. Eight traditional topic areas studied within a variety of applications New research areas emerging In all research, before it will be used practically in real-world applications, need increased focus on: Robustness Reliability
Topics to be Studied this Semester Taxonomies, Metrics, Evaluation Biological Inspirations Low-level, homogeneous, swarm robots Swarming, dispersion, homing, etc. Search/coverage Sensor networks Communication and communications networks Formations Pursuit/herding Tracking Reconfigurable robots
Topics to be Studied this Semester (con t.) Higher-level strategies, Heterogeneous Robots Multi-robot path planning, traffic management Task allocation: Negotiation-based Market-based Modeling-based Marsupial teams Air-ground teams Multi-robot soccer Embedded Systems Smart dust Intelligent rooms/smart homes Amorphous/pervasive computing
Preview of Next Class Biological Inspiration Birds and Bees ;-) Herds and schools Stigmergy Etc. Reading list distributed, presentation selections begin Homework 1 distributed