Paper Info
Title
Sigma hulls for Gaussian belief space planning for imprecise articulated robots amid obstacles
Abstract
In many home and service applications, an emerging class of articulated robots such as the Raven and Baxter trade off precision in actuation and sensing to reduce costs and to reduce the potential for injury to humans in their workspaces. For planning and control of such robots, planning in belief ssigma hullpace, i.e., modeling such problems as POMDPs, has shown great promise but existing belief space planning methods have primarily been applied to cases where robots can be approximated as points or spheres. In this paper, we extend the belief space framework to treat articulated robots where the linkage can be decomposed into convex components. To allow planning and collision avoidance in Gaussian belief spaces, we introduce the concept of sigma hulls: convex hulls of robot links transformed according to the sigma standard deviation boundary points generated by the Unscented Kalman filter (UKF). We characterize the signed distances between sigma hulls and obstacles in the workspace to formulate efficient collision avoidance constraints compatible with the Gilbert-Johnson-Keerthi (GKJ) and Expanding Polytope Algorithms (EPA) within an optimization-based planning framework. We report results in simulation for planning motions for a 4-DOF planar robot and a 7-DOF articulated robot with imprecise actuation and inaccurate sensors. These experiments suggest that the sigma hull framework can significantly reduce the probability of collision and is computationally efficient enough to permit iterative re-planning for model predictive control.
Year
DOI
Venue
2013
10.1109/IROS.2013.6697176
Intelligent Robots and Systems
Keywords
Field
DocType
Gaussian processes,Kalman filters,collision avoidance,convex programming,iterative methods,mobile robots,motion control,nonlinear filters,predictive control,probability,4-DOF planar robot,7-DOF articulated robot,EPA,GKJ,Gaussian belief space planning,Gaussian belief spaces,Gilbert-Johnson-Keerthi,POMDP,Raven and Baxter trade off precision,UKF,actuation sensor,articulated robots,belief space framework,belief space planning methods,collision avoidance constraints,convex components,convex hulls,expanding polytope algorithms,iterative replanning,model predictive control,optimization-based planning framework,planning motions,probability,robot control,robot links,robot planning,sigma hull framework,sigma hulls,sigma standard deviation boundary points,unscented Kalman filter
Motion control,Mathematical optimization,Computer science,Workspace,Model predictive control,Kalman filter,Robot,Convex optimization,Mobile robot,Articulated robot
Conference
ISSN
Citations 
PageRank 
2153-0858
2
0.45
References 
Authors
0
8
Name
Order
Citations
PageRank
Alex Lee134113.46
Yan Duan277527.97
Sachin Patil376437.93
John Schulman4180666.95
Zoe McCarthy5856.07
Jur van den Berg6197793.23
Ken Goldberg73785369.80
Pieter Abbeel86363376.48