Session WedAT1 Pegaso A Wednesday, October 10, 2012 ... - Lirmm

Session WedDT7 Vega Wednesday, October 10, 2012, 14:00–15:00
Motion and Path Planning VI
Chair Songhwai Oh, Seoul National Univ.
Co-Chair
14:00–14:15 WedDT7.1
Sampling-based Nonholonomic Motion Planning in Belief
Space via Dynamic Feedback Linearization-based FIRM
Ali-akbar Agha-mohammadi 1 , Suman Chakravorty 2 ,
Nancy M. Amato 1
1 Dept. of Computer Science and Engineering, Texas A&M University, USA
2 Dept. of Aerospace Engineering, Texas A&M University, USA
• Sampling-based motion planning
in belief space for nonholonomic
systems with FIRM (Feedbackbased
Information RoadMap)
• Using a Dynamic Feedback
Linearization-based controller
along with a stationary Kalman
filter to perform belief stabilization
• Robust feedback motion planning
in belief space with real-time
replanning capabilities
Feedback solution in belief
space obtained by DFL-based
FIRM in a simple environment
Unicycle
14:30–14:45 WedDT7.3
Task-oriented Design of Concentric Tube Robots
using Mechanics-based Models
Luis G. Torres and Ron Alterovitz
Department of Computer Science, UNC-Chapel Hill, USA
Robert J. Webster III
Department of Mechanical Engineering, Vanderbilt University, USA
• New task-oriented approach for designing
concentric tube robots on a surgery- and
patient-specific basis
• Uses mechanics-based kinematic model
for more accuracy than prior design
methods
• Combines search in design space with
motion planning in configuration space for
probabilistic completeness in design space
• Leverages design coherence to accelerate
design process
• Applied design method to medically
motivated bronchial surgery scenario
A concentric tube robot designed
by our method reaching two
surgical targets in the lung
14:15–14:30 WedDT7.2
Local Randomization in Neighbor Selection
Improves PRM Roadmap Quality
Troy McMahon, Sam Jacobs, Bryan Boyd and Nancy M. Amato
Parasol Lab, Dept of Computer Science and Engineering,
Texas A&M University, USA
Lydia Tapia
Dept of Computer Science, University of New Mexico, USA
• Proposes a candidate neighbor
selection policy, LocalRand(k,k’),
which identifies k’ local nodes then
selects k of those nodes at random.
• LocalRand yields many benefits
associated with randomized methods
while maintaining the advantages
inherent to a localized method like kclosest.
• Experimental evaluation shows that
LocalRand produces better roadmaps
than k-closest at a comparable cost.
14:45–15:00 WedDT7.4
Sampling-Based Sweep Planning to Exploit
Local Planarity in the Inspection of Complex 3D
Structures
Brendan Englot and Franz S. Hover
Department of Mechanical Engineering,
Massachusetts Institute of Technology,
USA
• Hybrid algorithm for planning a fullcoverage
inspection of complex 3D
structures
• Rectangular, back-and-forth sweep paths
cover the open, planar areas
• Randomized configurations cover the
confined, occluded areas
• Used to plan ship hull inspection routes for
an autonomous underwater vehicle
• Back-and-forth sweep paths are seeded
through random sampling
• We show probabilistic completeness and
fast algorithm convergence
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems
–157–
A full-coverage AUV inspection
route for a ship’s stern using both
regularized and randomized
configurations