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Toward Autonomous Free- Climbing Robots. Tim Bretl, Jean-Claude Latombe, and Stephen Rock May 2003. Presented by Randall Schuh. Motivation. Non-specific autonomous rock-climbing robots could benefit several applications: Search-and-rescue mountainous terrain broken urban environments
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Toward Autonomous Free-Climbing Robots Tim Bretl, Jean-Claude Latombe, and Stephen Rock May 2003 Presented by Randall Schuh
Motivation • Non-specific autonomous rock-climbing robots could benefit several applications: • Search-and-rescue • mountainous terrain • broken urban environments • Exploration • Sub-surface environments • Planetary, especially on Mars • New modes of motion for humanoid robots
Previous Work • Climbing robots • exploit unnatural surface properties, e.g.: • Peg into hole • Suction pads (grass, steel surfaces) • Track and legged robots • ascend slopes up to 50 degrees • Few works consider choosing foot placement • Grasping • usually emphasizes force-closure
Planar Model • 3 identical limbs, 8 dof • Ignores self-collision • Coulomb friction • Motion occurs in 4-D subspace of C-space
Planar Motion Planning • One-Step-Climbing Problem • Geometrical insight allows solution path planning in 2 dimensions (pelvis position) • Uses PRM techniques • Instead of collisions, the planner tests for equilibrium • Uses dynamic testing algorithm (Class 3) • Uses a simple smoothing technique
Coulomb friction cones Ffriction ≤ μ N Ffriction ≤ μ F cos θ Stable if: F sin θ < μ F cos θ Friction cone: tan φ ≤ μ φ ≤ tan–1μ
+ = – + = 0 E Dependent on x Dependent only on x Equilibrium Region
Geometrical Analysis • Free space of the free limb consists of 2 connected subsets
3D Model – LEMUR1 II • Each limb has a spherical shoulder and a revolute knee (4 dof); limbs are 30 cm long • Joints are mechanically limited • Robot can push or pull from each endpoint • Motion occurs in 13-D subspace of C-space 1Limbed Excursion Mobile Utility Robot – developed by JPL
3D Motion Planning • Still tests for equilibrium • Uses PQP to test for self-collisions and collisions with environment • Uses a more sophisticated technique for sampling closed kinematic chains • Not yet reduced dimension of problem with geometrical analysis.
Future Work • Apply geometric insight to be able to capture narrow passages more efficiently • Add torque constraints • Implement the algorithm on hardware, which will require • Visual and tactile sensing of grasps • Tactile feedback (slippage detection) • Multi-step planning based on incomplete information
Paper Comparison Common Features: • Planning from a discrete series of grasps • Applying PRM techniques Differences: • Application to real vs. digital environment • Kinematic & equilibrium vs. kinematic constraints