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Autonomous Navigation of a
Humanoid Robot over Unknown Rough Terrain using a Laser Range Sensor Koichi Nishiwaki, Joel Chestnutt, Satoshi Kagami The International Journal of Robotics Research. 17 August 2012.
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Bipedal Robots HRP-2 38-DOF
Good - Terrain with obstacles, discontinuous height changes, roughness Challenging Problem - Naturally unstable HRP-2 38-DOF
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Proposed Solution Advanced System
no need for assumptions or previous knowledge of objects in environment ability to step on inclined terrain Advanced System Laser-based Perception System Footstep Planner Robust Walking Controller Operational Interface - assign commands / goal positions
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Autonomous Navigation System
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Laser-based Perception System
Scanning-type laser range sensor with a swinging mechanism Terrain map for footstep planning - grid of 0.02m x 0.02m cells with height value Measurements from up to 30m Convert distance data from single sweep into absolute 3D positions using position of robot and angle of sensor
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Terrain shape measurement of a flat office floor
Observed cells
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Error increases with distance from sensor
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Terrain with regions of different heights and a wall
Calculated heights of cells
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Improved method to Reduce Noise
if standard dev < 0.01: average heights else: sort height large to small calc std dev top n repeat, n+1, while std dev < 0.01 take average of top n
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Footstep Planning example transition set
A* search to generate sequence of footstep locations for reaching given goal state Limit landing positions of swing foot relative to stance foot to a finite number Cost associated with each possible footstep: terrain height, inclination, roughness (Chestnutt 2003) Trajectories planned after footstep chosen “Guide curve” used as the A* heuristic – planning “ahead” guidance Adaptive Action Model example transition set
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Dynamically Stable Trajectory Generation
generate walking trajectories that start from estimate actual motion takes time – approx. 36ms per step estimate initial conditions
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Sensor Feedback Control System
execute specified torso motion, even if terrain shape is different than expected repetitive trajectory generation compensates for divergence ground reaction force control: let feet handle the uneven terrain desired reaction force and velocity in response to environment Keeps torso motion insensitive to difference of terrain shape
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Estimating Absolute Posture
positions and velocities of the torso and feet inertial measurement unit (IMU) - gyroscopes, servo accelerometers rotate robot around measured ZMP
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Operational Interface Graphical user interface
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Mixed-reality interface
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Joystick interface
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Navigation Experiments
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Evaluation of measurement accuracy for height and inclination
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Videos
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Conclusion Achieved autonomous navigation of humanoid robot on unknown rough terrains no use of assumptions or previous knowledge of the shapes of objects in the environment Different modes of operation: GUI, HMD, Joystick
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