The complexity of robot motion planning
The complexity of robot motion planning
The power of physical representations
AI Magazine
Qualitative kinematics in mechanisms
Artificial Intelligence
Efficiently Computing and Representing Aspect Graphs of Polyhedral Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Qualitative spatial reasoning: the CLOCK project
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Learning to explore and build maps
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Geometric reasoning about mechanical assembly
Artificial Intelligence
A comparison of methods for representing topological relationships
Information Sciences—Applications: An International Journal
Maintaining knowledge about temporal intervals
Communications of the ACM
Robot Motion Planning
Spatial Planning: A Configuration Space Approach
IEEE Transactions on Computers
Complexity of the mover's problem and generalizations
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
Negotiating design specifications: evolving functional constraints in mechanical assembly design
Proceedings of the 2008 ACM symposium on Solid and physical modeling
Discovering implicit constraints in design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
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In this work we use contact alignments as qualitative landmarks to discretize the relative motion between two 3D objects. We use assembly planning as a sample domain, and address the question of obtaining the assembly blocking graphs from the geometry and the motion constraints. Starting from a geometrical description of the objects we characterize contacts involving topologically distinct feature sets, called contact formations (CF) and obtain a qualitative decomposition of the configuration space based on CFs. We show how standard algorithms for finding the configuration-space routinely discard CF information, and how these can be extracted at no additional computational cost. Finally we show how CFs can be used to generate assembly solutions and for correcting jamming and other assembly.