Journal ArticleDOI
Task Space Regions: A framework for pose-constrained manipulation planning
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TLDR
A manipulation planning framework that allows robots to plan in the presence of constraints on end-effector pose, as well as other common constraints, and proves probabilistic completeness for the planning approach is presented.Abstract:
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.read more
Citations
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Journal ArticleDOI
Sampling-Based Robot Motion Planning: A Review
Mohamed Elbanhawi,Milan Simic +1 more
TL;DR: The state of the art in motion planning is surveyed and selected planners that tackle current issues in robotics are addressed, for instance, real-life kinodynamic planning, optimal planning, replanning in dynamic environments, and planning under uncertainty are discussed.
Journal ArticleDOI
Hierarchical quadratic programming
TL;DR: This paper proposes a complete solution to solve multiple least-square quadratic problems of both equality and inequality constraints ordered into a strict hierarchy using a generic solver used to resolve the redundancy of humanoid robots while generating complex movements in constrained environments.
Journal ArticleDOI
Dynamic Whole-Body Motion Generation Under Rigid Contacts and Other Unilateral Constraints
Layale Saab,Oscar E. Ramos,François Keith,Nicolas Mansard,Philippe Souères,Jean-Yves Fourquet +5 more
TL;DR: The task-function approach is extended to handle the full dynamics of the robot multibody along with any constraint written as equality or inequality of the state and control variables to keep a low computation cost.
Journal ArticleDOI
Integrated Task and Motion Planning
Caelan Reed Garrett,Rohan Chitnis,Rachel Holladay,Beomjoon Kim,Tom Silver,Leslie Pack Kaelbling,Tomás Lozano-Pérez +6 more
TL;DR: This paper defines a class of TAMP problems and survey algorithms for solving them, characterizing the solution methods in terms of their strategies for solving the continuous-space subproblems and their techniques for integrating the discrete and continuous components of the search.
Journal ArticleDOI
Development of Human Support Robot as the research platform of a domestic mobile manipulator
TL;DR: This paper describes HSR’s development background since 2006, and technical detail of hardware design and software architecture, and describes its omnidirectional mobile base using the dual-wheel caster-drive mechanism, which is the basis of HSR's operational movement and a novel whole body motion control system.
References
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Book
Dynamic Programming
TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
MonographDOI
Planning Algorithms: Introductory Material
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Journal ArticleDOI
What is dynamic programming
TL;DR: Sequence alignment methods often use something called a 'dynamic programming' algorithm, which can be a good idea or a bad idea, depending on the method used.
Journal ArticleDOI
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
TL;DR: Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).