Date of Original Version




Abstract or Description

We present a manipulation planning framework that allows robots to plan in the presence of constraints on end-effector pose, as well as other common constraints. The framework has three main components: constraint representation, constraint-satisfaction strategies, and a general planning algorithm. These components come together to create an efficient and probabilistically complete manipulation planning algorithm called the Constrained BiDirectional Rapidly-exploring Random Tree (RRT) – CBiRRT2. The underpinning of our framework for pose constraints is our Task Space Regions (TSRs) representation. TSRs are intuitive to specify, can be efficiently sampled, and the distance to a TSR can be evaluated very quickly, making them ideal for sampling-based planning. Most importantly, TSRs are a general representation of pose constraints that can fully describe many practical tasks. For more complex tasks, such as manipulating articulated objects, TSRs can be chained together to create more complex end-effector pose constraints. TSRs can also be intersected, a property that we use to plan with pose uncertainty. We provide a detailed description of our framework, prove probabilistic completeness for our planning approach, and describe several real-world example problems that illustrate the efficiency and versatility of the TSR framework.



Included in

Robotics Commons



Published In

The International Journal of Robotics Research, 30, 12, 1435-1460.