planning biped navigation strategies in complex environments
DESCRIPTION
Planning Biped Navigation Strategies in Complex Environments. Jiaan Zeng. Problem. Plan goal-directed footstep navigation strategies for biped robots through obstacle-filled environments and uneven ground. - PowerPoint PPT PresentationTRANSCRIPT
Planning Biped Navigation Strategies in Complex EnvironmentsJiaan Zeng
Plan goal-directed footstep navigation strategies for biped robots through obstacle-filled environments and uneven ground.
Conventional 2D planning algorithms designed for wheeled robots would be unable to find a solution.
Problem
This paper models the problem as a standard search problem.
The planner uses an A* search to generate a sequence of footstep locations to reach a given goal state.
Solution
InputA map representing the terrain to plan over, an initial and goal state.
OutputIf a path is found the planner returns the solution as an ordered list of the footsteps that should be taken to reach the goal.
Model
State Space The state variables x, y, and θ denote the relative position and orientation of the footstep, and the binary variable s ∈ {R,L} denotes which foot is currently the support foot (right or left).
Model
Successor Function(State Transition)
An upper and lower allowable height change HCupper and HClower, and an obstacle clearance value, clearance, representing the largest obstacle that the transition can step over.
Model
Transitions
Model
Evaluation Function (State Evaluation) f = L(Q)+S(Q, T, Qc)+R(Q, Qg)
Model
Evaluation Function: L(Q) Location Metrics
L(Q) =∑ƜiMi (i=1, …, 5) M1 how slope the plane is M2 how roughness the plane is M3 how stability the plane is M4 the largest bump M5 how safety the plane is
Model
Location Metrics ― Plane Fitting
Model
Evaluation Function: S(Q, T, Qc)
Evaluation Function: R(Q, Qg)
Model
BFS (Best First) f=g= L(Q)+S(Q, T, Qc) A* f=g+h=L(Q)+S(Q, T, Qc)+ R(Q,
Qg)
Algorithms
Environment Simulation
Physical Robot
Empirical Results
Various Types of Terrain
Empirical Results
Distance to Goal
Empirical Results
TransitionsPerformance comparison of BFS (left) and A* (right) for different sets of available footstep transitions.
Empirical Results
TransitionsPerformance comparison of A* (left) and BFS (right) for increasing numbers of stairs along the path from the initial to goal state.
Empirical Results
TransitionsComparison of the output of BFS versus A* on environments with local minima.
Empirical Results
WeightsCarefully choosing the weights for the different metrics is very important for the runtime of the algorithm
Empirical Results
Online Footstep PlanningVision Processing steps. Raw camera images and resulting 3D Depthmap; Mesh model, planar surface identification, and final walking area map.
Empirical Results