Randomized Planning for Short Inspection Paths Tim Danner and Lydia E. Kavraki 2000 Presented by Dongkyu, Choi On the day of 28 th May 2003 CS326a: Motion Planning, Spring Prof. Jean-Claude Latombe

Inspection Problem Given: –Known workspace W –Robot with vision capabilities –Omni-directional camera w/ visibility constraints Compute: A short path s.t. the entire boundary(  W ) of the workspace is visible at some point on the path Applications: Inspection of bridges, space station, or any other structures Exploration of virtual worlds W WWWW

Visibility “Visible” The line of sight from the guard point to the point in question lies entirely in the workspace W Constraints – Max. viewing distance – Max. angle of incidence

Visibility (cont’d) The node can only see the resulting red lines

Visibility (cont’d) Some environments can’t be fully covered If this ‘corner’ angle is less than

Algorithm for 2D Step 1: Guard Selection –NP-hard to find a true minimal set of ‘art gallery’ guards –Use Randomized, Incremental algorithm [Gonzalez-Banos, Latombe 1998] Step 2: Guard Connection –n! orders to visit n points –Approximation to TSP* using Shortest Paths Graph * Traveling Salesman Problem

Step 1: Guard Selection Randomized, incremental approach While unguarded border exists, 1: Randomly pick an unguarded point p from 2: Find region which can see p under visibility constraints 3: Pick k samples from the region 4: Find the sample that can guard the most new length of border and store it as a guard 5: Update the border representation (balanced tree)

Step 1: Guard Selection (cont’d) Randomized, incremental approach

Algorithm for 2D Step 1: Guard Selection –NP-hard to find a true minimal set of ‘art gallery’ guards –Use Randomized, Incremental algorithm [Gonzalez-Banos, Latombe 1998] Step 2: Guard Connection –n! orders to visit n points –Approximation to TSP* using Shortest Paths Graph * Traveling Salesman Problem

Step 2: Guard Connection Traveling Salesman Problem –Preorder walk of a minimum spanning tree has total length less than or equal to twice the weight of a shortest Traveling Salesman tour Guard distribution Minimum spanning tree Preorder walk Computed tour (19.074) Optimal tour (14.715)

– Requirements Complete graph Triangle inequality Step 2: Guard Connection Computed tour (19.074) Optimal tour (14.715)

Step 2: Guard Connection (cont’d) –Characteristics One node for each guard One node for each vertex One edge for each pair of guards visibility graphLength of the shortest collision- free path assigned as weight to each edge (shortest path generated using visibility graph method) Shortest Paths Graph

Step 2: Guard Connection (cont’d) Shortest Paths Graph complete – A complete graph since the inspection problem has connected workspace – Composed of shortest paths which satisfy the triangle inequality Can be approximated to TSP

Step 2: Guard Connection (cont’d) Optimized Graph Building –Complete graph of n nodes yields n 2 edges –Desirable to keep the guard connection step sub-quadratic: Each node is only connected to a constant number of nearby nodes Number of computed shortest paths reduced to O(n)

Experimental Results guard selectionguard connectiontestnumber of guardsconstraints sec sec sec sec sec sec. 60 deg./1 grid 60 deg./none none Test 2Test 3 Most of the computation time is spent computing visibility polygons Possible future works to speed this procedure by using maximum constraint and filtering out workspace features far away

Algorithm for 3D Basic procedure unchanged Challenge: Very hard to compute a visibility polyhedron Solution: Avoid explicit representation

Step 1: Guard Selection Use of the visibility polyhedron –Visible surface determination Arrange faces in front-to-back order using Binary Space Partitioning tree Find the visible surfaces by clipping –Sampling in the visibility polyhedron Represent visibility constraints as the intersection of a sphere and a cone Sample in the intersection and check for visibility

Step 2: Guard Connection No simple algorithm to compute optimal shortest paths Random points in the free space are chosen instead of obtaining workspace-guard roadmap

Preliminary Results 2 unit cubes: computed in 20 sec. 4 cubes and 3 tetrahedra: 143 sec.

Future Works Criteria for path validity (ex. dynamics, lowest fuel consumption) Flexibility by replacing nodes with small regions Directional cameras