Sampling-BasedMethods for Motion Planning with Constraints

Robots with many degrees of freedom (e.g., humanoid robots andmobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-basedmotion planning algorithms have been shown to be effective for these high-dimensional systems. However, incorporating task constraints (e.g., keeping a cup level, writing on a board) into the planning process introduces significant challenges. This survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based upon two core primitive operations: () sampling constraint-satisfying configurations and () generating constraintsatisfying continuous motion. Although the basics of samplingbased planning are presented for contextual background, the survey focuses on the representation of constraints and samplingbased planners that incorporate constraints.  Accepted for publication in Annual Review of Control, Robotics, and Autonomous Systems, 2018 http://www.annualreviews.org/journal/control