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

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.

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

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

CMU’s Honda ASIMO Humanoid

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

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

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

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

Sensing and the Environment: Converting to World Coordinates

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

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

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

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

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

Evaluation: Vision-Planner Integration

Evaluation: Obstacle Avoidance – Unpredictably Moving Obstacles

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