# CS 4649/7649 Robot Intelligence: Planning

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1 CS 4649/7649 Robot Intelligence: Planning Motion Planning: Introduction & Reactive Strategies Sungmoon Joo School of Interactive Computing College of Computing Georgia Institute of Technology S. Joo 1 *Slides based in part on Dr. Mike Stilman & Howie Choset s lecture slides Course Info. TA - Saul HW#1 due next week - Trouble with group matching? - Need a repo.? - Trouble with install & running open-source planners? - Late policy Final Project Topic? S. Joo 2 1

2 Motion Planning: Goal Motion planning is the ability for a robot to compute its own motions in order to achieve certain goals. (i) To compute motion strategies - geometric path - time-parameterized trajectories - sequence of sensor-based motion commands - (ii) To achieve high-level goals - go to A without colliding with obstacles - pick up the mug - All (autonomous) robots should eventually have this ability S. Joo 3 Motion Planning: Sealing Process Figure from Planning Algorithms by Steven M. LaValle S. Joo 4 2

3 Motion Planning: Piano Mover Figure from Planning Algorithms by Steven M. LaValle S. Joo 5 Motion Planning: Parking Figure from Planning Algorithms by Steven M. LaValle S. Joo 6 3

4 Motion Planning: Goal S. Joo 7 Basic Path Planning Example Free Space Workspace Robot* World *Point robot S. Joo 8 4

5 Motion Planning Statement Compute a continuous sequence of collision-free robot configurations connecting the initial and goal configurations. q: the robot s configuration, a complete specification of the position of every point in the system (a set of parameters that define the robot geometry in the world) W : the robot s workspace, the points the robot can possibly reach Wo i : the i th obstacle, Notations/Definitions W free : the robot s freespace, W free = W - ( WO i ) a path c C 0, c : [0,1] W free where c(0) is q init and c(1) is q goal *Class C 0 : all continuous functions S. Joo 9 Completeness S. Joo 10 5

6 Optimality S. Joo 11 Bug Algorithms (Lumelsky & Stepanov 87) Bug behaviors (insect-inspired) Follow a wall (right or left) Move in a straight line toward goal S. Joo 12 6

7 Bug Algorithms (Lumelsky & Stepanov 87) S. Joo 13 Bug Algorithms Assumptions - 2D World. Point robot. Finite # of Obstacles. Obstacles have bounded perimeters - Robot has not prior knowledge of locations, shapes of the obstacles - Robot can senses its position with perfect accuracy. Goal is known. - Robot can measure the distance btw two points. Robot can measure its traveled distance - Robot can perfectly detect contact with an obstacle S. Joo 14 7

8 Bug 0 Algorithm Hit point Right turn assumption Leave point S. Joo 15 Bug 0 Algorithm Goal L 2 H 2 L 1 H 1 Init S. Joo 16 8

9 Bug 0 Algorithm L 2 L 1 H 1 H 2 Goal Init S. Joo 17 Bug 0 Algorithm S. Joo 18 9

10 Bug 0 Algorithm S. Joo 19 Bug 0 Algorithm S. Joo 20 10

11 Bug 1 Algorithm circumnavigate * L i L 2 L 1 *By following the shortest path along the object boundary S. Joo 21 Bug 1 Algorithm H 2 L 2 H 1 L 1 Goal Init S. Joo 22 11

12 Bug 1 Algorithm (Lower bound) (Upper bound) S. Joo 23 Bug 1 Algorithm Leave point Why? S. Joo 24 12

13 Bug 1 Algorithm How Can Bug 1 Recognize that the goal is not reachable? L i If the direction from L i toward the goal points into the obstacle, then the goal can t be reached. Goal S. Joo 25 Improvements? S. Joo 26 13

14 Improvements? m-line: The straight line from the initial configuration to the goal configuration S. Joo 27 Bug 2 Algorithm (following m-line) * L 1 H 2 L 2 H 1 *Usually toward the left S. Joo 28 14

15 Bug 2 Algorithm H 1 L 1 H 2 L 2 Goal Init S. Joo 29 Bug 2 Algorithm L 1 H 2 L 2 H 1 S. Joo 30 15

16 Bug 2 Algorithm D D + i n i 2 P i n i = # of intersections of the i th obstacle with m-line S. Joo 31 Comparison of Bug 1 & Bug 2 S. Joo 32 16

17 Comparison of Bug 1 & Bug 2 S. Joo 33 Bug Algorithms Summary S. Joo 34 17

18 Bug Algorithms Summary 35 Bug Algorithms Summary Algorithm Bug 0 Bug 1 Bug 2 Completeness X 0, Exhaustive 0, Greedy Characteristic - Safe, Reliable Better in some cases. But worse in other cases *None of them is optimal 36 18

19 Planning Requires Models Bug algorithms don t plan ahead. They are not really motion planners, but reactive motion strategies To plan its actions, a robot needs a (possibly imperfect) predictive model of the effects of its actions, so that it can choose among several possible combinations of actions 37 A Useful Notion: Competitive Ratio (CR) Bug algorithms are examples of online algorithms where a robot discovers its environment while moving Competitive Ratio: To evaluate algorithms that utilize different information - An on-line algorithm vs an algorithm that receives more information - The competitive ratio of an online algorithm A is the maximum over all possible environments of the ratio of the length of the path computed by A by the length of the path computed by an optimal offline algorithm B that is given a model of the environment 38 19

20 A Useful Notion: Competitive Ratio (CR) Lost Cow Problem a short-sighted cow is following along an infinite fence and wants to find the gate (a)if the cow is told that the gate is exactly distance 1unit away CR? (b)if the cow is told only that the gate is at least distance 1unit away CR? Figure from Planning Algorithms by Steven M. LaValle 39 20

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