1. Vision-Guided Humanoid Footstep Planning for Dynamic Environments
P. Michel, J. Chestnutt, J. Kuffner, T. Kanade
Carnegie Mellon University – Robotics Institute
Humanoids 2005

2. Objective
Paper presents a vision- based footstep planning system that computes the best partial footstep path within its time-limited search horizon, according to problem-specific cost metrics and heuristics.

3. Related Work reliable, stable gait generation and feedback
Emphasis on pre-generating walking trajectories
Online trajectory generation
Dynamic balance
No accounting for obstacles!
Little work focused on developing global navigation autonomy for biped robots

4. Related Work
Obstacle avoidance and local planning based on visual feedback has been studied in humans
Several reactive perception-based obstacle avoidance techniques for bipeds have been developed
Environment mapping; obstacle detection; color-based segmentation

5. CMU’s Honda ASIMO Humanoid

6. Sensing and the Environment
ASIMO Robot
Global sensing
Overhead camera: compute position of robot, desired goal location, obstacles
All processing done on real-time

7. Sensing and the Environment: Color Segmentation
Colored markers
Bright pink: planar obstacles on the floor
Light blue: desired goal location
Yellow and green: identify robot’s location and orientation
Dark blue: 4 square delimiters to define a rectangular area within which the robot operates
Color segmentation performed directly on YUV stream generated by camera
Avoids processing overhead

8. Sensing and the Environment: Color Segmentation
Color thresholds = sample pixel values offline for each marker
Produced series of binary masks including presence or absence of markers pixel
Noise eliminated by erosion/dilation
Connected components labeling is applied to group of pixels
Calculate moments for each color blob
Centroid, area, major/minor aces, orientation of floor

9. Sensing and the Environment: Converting to World Coordinates
Assume physical distance between 4 delimiters that outline robot’s walking area are known
Scaling used to convert between pixel coordinate of each blob’s centroid and corresponding real-world distances
Orientation of robot determined from angle the line connecting the backpack markers forms with horizontal
Footstep planning requires precise location of robot’s feet

10. Sensing and the Environment: Converting to World Coordinates

11. Sensing and the Environment: Building the Environment Map
2D grid of binary value cells = environment
Value in cell = whether terrain is obstacle free or partially/totally occupied by obstacle
Bitmap representation of freespace and obstacles

12. Footstep Planning
Goal: to find as close to an optimal sequence of actions as possible that causes the robot to reach the goal location while avoiding obstacles in the environment

13. Footstep Planning: Basic Algorithm
Planner Algorithms
Input: environment map E, initial and goal robot states, mapping of possible actions that may be taken in each state and an action-effect mapping
Return: sequence of footstep actions after finding path to goal
Planner computes cost of each candidate footstep location using 3 metrics:
Location cost determining whether candidate location is “safe” in environment
Step costs which prefers ‘easy’ stepping actions
Estimated cost-to-go providing approximation of candidate’s proximity to goal using standard mobile-robot planner

14. Footstep Planning: Basic Algorithm
A* search performed on possible sequences of walking actions
Done until a path is found OR
Specified computation time limit is exceeded

15. Footstep Planning: Plan Reuse
At each step: plan a path towards the goal.
ASIMO takes first step and then replans for next step
Reuse computations from before using a forward search

16. Evaluation: Vision-Planner Integration

17. Evaluation: Obstacle Avoidance – Unpredictably Moving Obstacles

18. Discussion
Approach to autonomous humanoid walking in presence of dynamically moving obstacles
Combines sensing, planning and execution in closed loop
Currently working:
more realistic estimate of floor directly surrounding robot’s feet
On-body vision to satisfy real-time constraints for sensing loop