Providing Haptic ‘Hints’ to Automatic Motion Planners Providing Haptic ‘Hints’ to Automatic Motion Planners Burchan Bayazit Joint Work With Nancy Amato and Guang Song Burchan Bayazit Joint Work With Nancy Amato and Guang Song Department of Computer Science Texas A&M University

Motion Planning GIVEN: an environment and start and goal positions FIND: a path from start to goal which avoids collision with obstacles GIVEN: an environment and start and goal positions FIND: a path from start to goal which avoids collision with obstacles fit elbow through holefind part removal pathAlpha Puzzle - separate tubesDrug Design (docking) Automatic planners are good, but sometimes can’t find critical configurations obvious to the user

Planner creates initial roadmap User collects critical configurations/paths ( may have collisions to make task easier,i.e., approximate path ) –a haptic device is ideal for this Planner improves roadmap with user’s collection –i.e, planner fixes approximate path Hybrid Human/Planner System (Enhancing PRMs with Haptic Hints)

Outline Roadmap Visualization Haptic Interaction: Collecting Paths Processing User-Generated Paths Results Conclusion Roadmap Visualization Haptic Interaction: Collecting Paths Processing User-Generated Paths Results Conclusion

Roadmap Visualization and Sensing Problem: need to show the planner’s progress (in C-space) to the user (in workspace) Displaying paths/roadmaps Feeling paths/roadmaps (future work) Problem: need to show the planner’s progress (in C-space) to the user (in workspace) Displaying paths/roadmaps Feeling paths/roadmaps (future work)

Roadmap Visualization Roadmap nodes are represented as translations in workspace (reference point can be center of mass, end effector position etc.) Roadmap edges are the lines connecting the workspace representations Different components have different colors Small robots helps to visualize current configuration at each point

Displaying Roadmap Edges and Configurations Each connected component is shown in a different color

PHANToM User attaches haptic device to robot, and moves it around user feels when robot touches obstacles and adjusts trajectory collision detection too slow (~10 Hz), so distribute process and use extrapolation techniques (almost all) Robot configurations passed to planner automatically sampled at regular intervals Haptic Interaction: Collecting Paths

Proposed System

Applying Force

Ideal Algorithm for Force Feedback Robot Position Haptic Loop Calculate Force return Force Based on penetration distance no force some force more force

PROBLEM: Penetration not available for complex environments SOLUTION: If robot in collision, push outside with constant force –user can select full, half, or zero (collision off) force PROBLEM: Penetration not available for complex environments SOLUTION: If robot in collision, push outside with constant force –user can select full, half, or zero (collision off) force Calculating Reactive Force no forcesame force

Collision Detection PROBLEM: –too slow and highly variable (~ 10 Hz SGI O2) SOLUTION: –distributed system (computations on another computer) –use heuristic to determine when collision occurs PROBLEM: –too slow and highly variable (~ 10 Hz SGI O2) SOLUTION: –distributed system (computations on another computer) –use heuristic to determine when collision occurs

Calculate Collision Computation Loop Heuristically decide collision Heuristically decide collision Robot Position Get result Haptic Loop Yes No Previous result ready? Collision ? Yes No Return No ForceReturn Constant Force Request new calculation Request new calculation

P P LF last free cfg (computed) minimum distance (md) P P LF md current distance (cd) if projection of cd > md then collision else free When last computed configuration was free

P P LF LC last colliding cfg (computed) P P P LF LC P collision distance (xd) current distance (cd) if projection of cd > xd then collision else free When last computed configuration was colliding current cfg last free cfg (computed)

Processing User-Generated Paths User generates approximate path P –it may contain collisions Planner “pushes” colliding portions of P to C-free –techniques inspired by OBPRM ideal for this C-obstacle pushed path generated by planner approximate path generated by user

Push Towards Line Segment C-obstacle

Push Toward the Closest Surface cfg C-obstacle

Push in Workspace (using vertices) Workspace Obstacle Robot d4 d1d3 d2 Pair each vertex of robot with workspace obstacle Move in the closest direction Pair each vertex of robot with workspace obstacle Move in the closest direction

Iterative Pushing Original Problem Simplify Initial Solution Haptic/OBPRM Initial Solution Haptic/OBPRM Push Increase Accuracy Return Path Solved Original Problem? Solved Original Problem? No Yes Approximate Path Original Problem Simplify Initial Solution Haptic/OBPRM Initial Solution Haptic/OBPRM Increase Accuracy` Solved Original Problem? Solved Original Problem? No Push Increase Accuracy Push Yes Solved Original Problem? Solved Original Problem? Return Path

Original C-SpaceC-Space after dilation Difficulty: Topology can change Dilated C-Space Previous researchers have recognized potential of working in dilated space (Hsu, et.al., WAFR’98) Advantage of our method: For some problems, user insight narrows search to promising directions.

Dilation Methods Change Penetration Depth Change Scale of the Robot

Haptic Hints: Flange Example Step 1: User collected pathStep 2: Pushed configs (planner) Final path (planner)

Haptic Hints: Results Flange Problem

Conclusion Heuristic collision detection provides support for approximate path collection –ok since we’re collecting approximate paths Automatic planners can effectively transform approximate paths to free paths –faster than fully automatic –iterative transformation works well (in some cases) Roadmap visualization is useful Heuristic collision detection provides support for approximate path collection –ok since we’re collecting approximate paths Automatic planners can effectively transform approximate paths to free paths –faster than fully automatic –iterative transformation works well (in some cases) Roadmap visualization is useful