Planning motions with intentions
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Synthesizing animations of human manipulation tasks
ACM SIGGRAPH 2004 Papers
Planning Algorithms
Manipulation planning on constraint manifolds
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Manipulation planning with workspace goal regions
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Object recognition and full pose registration from a single image for robotic manipulation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Addressing pose uncertainty in manipulation planning using task space regions
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Randomized path planning on manifolds based on higher-dimensional continuation
International Journal of Robotics Research
Dynamic walking and whole-body motion planning for humanoid robots: an integrated approach
International Journal of Robotics Research
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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.