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Constraint-Based Motion Planning for Multiple Agents

Explore constraint-based motion planning for multiple agents, essential for scenarios like team rescue operations and game scenarios. Learn about hard and soft constraints, feasibility studies, virtual environments, and the potential for adding higher levels of intelligence. References to Garber & Lin's work and Reynolds' behavioral model included.

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Constraint-Based Motion Planning for Multiple Agents

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  1. Constraint-Based Motion Planning for Multiple Agents Luv Kohli COMP259 March 5, 2003

  2. Motion planning • What is it? • Basically, determine a path (e.g., for a robot) from one point to another, avoiding obstacles along the way • Useful for many applications, including virtual assembly, automatic painting, etc.

  3. Constraint-based? • Garber & Lin formulated the motion planning problem as a dynamical system simulation • Each robot is a rigid body or a collection of rigid bodies influenced by constraint forces in the environment

  4. Constraints • Hard constraints • Absolutely must be satisfied (e.g. non-penetration, articulated robot joint connectivity) • Soft constraints • Encourage objects to follow certain behaviors (e.g. moving towards a goal, obstacle avoidance)

  5. Multiple agents • I would like to extend the constraint-based framework to study scenarios involving multiple interacting agents • Possible scenarios: • Team rescue operations – compromised senses • Game scenarios (e.g., sports) • Military operations – continuous line of sight

  6. What’s this all for, anyway? • If the constraints of a real multiple-agent system can be identified and modeled, then the feasibility of the goal can be studied • Virtual environments • Games

  7. Tasks • Minimally I would like to get a constraint-based system working with multiple agents • The multiple agents will be acting either against each other or with one another towards some global goal, but influenced by local behavior

  8. Other fun stuff • It might be interesting to add higher levels of behavior and intelligence • Flocking-style algorithms • Agents that learn skills that can be applied to multiple scenarios

  9. References • Garber, M. and Lin, M. Constraint-Based Motion Planning using Voronoi Diagrams. Proc. Fifth International Workshop on Algorithmic Foundations of Robotics (WAFR), 2002. • Garber, M. and Lin, M. Constraint-Based Motion Planning for Virtual Prototyping. Proc. ACM Symposium on Solid Modeling and Applications, 2002. • Reynolds, C. W.. Flocks, Herds, and Schools: A Distributed Behavioral Model. Computer Graphics, 21(4): 25-34, 1987. • Goldenstein, S., Large, E., and Metaxas, D. Dynamic Autonomous Agents: Game Applications. Computer Animation, 1998.

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