multi-step motion planning for free-climbing robots tim bretl, sanjay lall, jean-claude latombe,...
TRANSCRIPT
Multi-Step Motion Planning for Free-Climbing Robots
Tim Bretl, Sanjay Lall, Jean-Claude Latombe, Stephen Rock
Presenter: You-Wei Cheah
The Climbing Problem
• Goal: Enable multi-limbed robots to free-climb vertical rock
• Applications:– search and rescue– cave exploration– human assistance in rock and
mountain climbing
Probabilities-Roadmap (PRM) motion planning
• Widely used for path planning through high-dimensional configuration spaces with multiple constraints
• Can construct feasible paths quickly• Lacks a formal stopping criterion• Question: How much time to spent on query?
LEMUR IIb• Consists of 4 identical limbs attached to a
circular chasis• Total mass: 7kg• Each limb has 3
revote joins DOF’s: 2 in-plane (yaw) 1 out-of plane (pitch)
Model
• Configurations are defined by 15 parameters:– the position/orientation (xp, yp, θp) of the pelvis
– the joint angles (θ1, θ2, θ3) of each limb.
• Holds lie on inclined plane are defined by– a 2-D point (xi, yi)
– a 3-D point (vi)
Model
• Friction modeled using Coulomb’s law• LEMUR IIb maintains 3-hold and 4-hold stances while
climbing• Set of supporting holds is a stance, denoted σ• Robot’s continuous motion with 4 supporting holds
occurs on a 3-D manifold Cσ4
• 3 supporting holds, motion occurs on a 6-D manifold Cσ3
• four additional constraints: quasi-static equilibrium, joint angle limits, joint torque limits, and (self-)collision
Climbing motion
• Switch between 3-hold and 4-hold stances• σ3 and σ4 are adjacent if σ4 = σ3 {i} for some ∪
hold i• Robot can only switched between adjacent
stances σ and σ’ at points qt F∈ σ ∩ Fσ′
• If continuous path connecting qs to a transition point exists in that component, then reachable
One-step climbing algorithm
• Tries to build a path from some qs to a goal qg
• Sample uniformly at random the goal region for a goal configuration
• Build a PRM in the space of configurations that are collision free and satisfy the equilibrium test
Multi-step Planning
• Given a stance σ, a start configuration qs F∈ σ, and a goal hold g:– construct a sequence of one-step motions that
will bring the robot to a stance σg that contains g.
• Graph search problem• Nodes in the graph are components of feasible
spaces and not particular configurations
Multi-step Planning
Performance Analysis
• One-step planning– Most one-step moves
were planned quickly– Difficult moves
took more time • Multi-step planning– Total planning time
approaches linear growth
Proposed modifications
• Given each one-step motion query, run for a short length of time
• Attempt to prove the motion is infeasible if a solution is not found
• If disconnection proof is not found, allow planner to run for an additional Tmax
Proving one-step disconnection
• Assumption: the feasible space Fσ can be represented as a semialgebraic set
• Fnd a polynomial function g R[x∈ 1, … , xn] such that g(qs) > 0, g(qg) < 0, and Pcut(g) is empty:
Future work
• Robot will have to plan based on locally sensed information that is incomplete or uncertain
• Advances in computational algebra might be able to produce practical algorithms for proving disconnections
Thank you