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Bug Algorithms

Explore Bug Algorithms inspired by insects' local knowledge for efficient path planning in robotics. Learn about Bug 0, Bug 1, and Bug 2 algorithms and their evolution. Discover how Bug 2 improves on Bug 1 with a greedy approach. Dive into the design, benefits, and challenges of these algorithms for robotic motion planning.

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Bug Algorithms

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  1. Bug Algorithms

  2. Approach • Insects have only local knowledge • Even Stygmergy is local in space, though perhaps not in time. • Simple robots have similar constraints • The class of Bug Algorithms require local knowledge to accomplish some task.

  3. Bug Path Planning • Bugs know the direction to the goal (recall ants navigation via polarised light). • Bugs know the Euclidean distance between arbitrary points (not insect like). • Otherwise sensing is local (think range finders like an epuck and wheel encoders).

  4. Bug 0 • Algorithm: • Repeat • Head toward goal • While path to goal is blocked • Follow obstacle edges • End while • End Repeat

  5. Bug 0 • Algorithm: • Repeat • Head toward goal • While path to goal is blocked • Follow obstacle edges • End while • End Repeat

  6. Design obstacles to break Bug 0

  7. A Bug Trap Algorithm: Repeat Head toward goal While path to goal is blocked Follow obstacle edges End while End Repeat

  8. Bug 0 • The problem with Bug 0 was that is had no way to back track • Need to add some memory to the robot

  9. Bug 0 • The problem with Bug 0 was that is had no way to back track • Need to add some memory to the robot

  10. Bug 1 • Algorithm: • Repeat • Head toward goal • While path to goal is blocked • x := point where the obstacle was encountered • do • Follow obstacle edges all the way round • n := the minimum distance to the goal • while not back at x • go to n • End while • End Repeat Lumelsky, V.J. & Stepanov, A.A.

  11. Bug 1 n • Algorithm: • Repeat • Head toward goal • While path to goal is blocked • x := point where the obstacle was met • do • Follow obstacle edges all the way around • n := the minimum distance to the goal • while not back at x • go to n • End while • End Repeat x n x Lumelsky, V.J. & Stepanov, A.A.

  12. Bug 2

  13. Bug 2 • Algorithm: • While not at the goal • Follow the m-line • While path to goal is blocked • Follow obstacle edges • If m-line met then break • End While • End While

  14. Bug 2 • Algorithm: • While not at the goal • Follow the m-line • While path to goal is blocked • Follow obstacle edges • If m-line met then break • End While • End While

  15. Debugging: Bug 2 • Algorithm: • While not at the goal • Follow the m-line • While path to goal is blocked • Follow obstacle edges • If m-line met • then break • End While • End While What went wrong? What’s the fix?

  16. Bug 2 • Algorithm: • While not at the goal • Follow the m-line • While path to goal is blocked • Follow obstacle edges • If m-line met closer to the goal • then break • End While • End While Orange: Right Turning, Blue: Left Turning

  17. Bug 1 vs Bug 2

  18. Bug 1 vs Bug 2

  19. Bug 1 vs Bug 2

  20. Bug 1 vs Bug 2 Bug 1: Is exhaustive. Considers all choices before progressing. Bug 2: Is a greedy algorithm. Takes the first thing that looks better. Bug 2 will often outperform Bug 1, but is less predictable.

  21. Tangent Bug

  22. Tangent Bug

  23. References • Robotic Motion Planning: Bug Algorithms, H. Choset, G.D. Hager, and Z. Dodds, Carnagie Mellon University, Robotics Institute 16-735 • Lumelsky, V.J. & Stepanov, A.A., “Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape”, Algorithmica (1987) 2: 403

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