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Legged Locomotion Planning. Kang Zhao B659 Intelligent Robotics Spring 2013. Planning Biped Navigation Strategies in Complex Environments Joel Chestnutt , James Kuffner , Koichi Nishiwaki , Satoshi Kagami. Global terrain map M Goal Primitive set {Trans} Search algorithm. - PowerPoint PPT Presentation

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Legged Locomotion Planning

Legged Locomotion PlanningKang ZhaoB659 Intelligent RoboticsSpring 20131Planning Biped Navigation Strategies in Complex EnvironmentsJoel Chestnutt, James Kuffner, Koichi Nishiwaki, Satoshi Kagami

2AIST supports HRP project2Global terrain map MGoalPrimitive set {Trans}Search algorithm

3Algorithm - Biped Robot Model44Algorithm- State transitions

01234567A 16-transitions set55Algorithm- Environment

6Algorithm- State EvaluationLocation metric to evaluate a locations cost

Slope angleRoughnessStabilityLargest bumpSafety7Each of the location metrics has both a weight and a cutoff value associated with it. If any metric passes its cutoff value, the location is discarded. Otherwise, the locations cost is the weighted sum of these metrics.

Based on the foots positions and orientation, we test every cell in the rectangular box around it. We rotate these cells coordinates into the foots coordinate system and then test them against the foots shape. This gives us a set of cells, C, to use7

Slope angleRoughnessStabilityLargest bumpSafety8The slope angle of the surface at the candidate location. Perfectly horizontal surfaces are desired. The slope angle is computed by fitting a plane h(x, y) to the cells in the location.Its purpose is to take into account the possible inaccuracy of foot positioning. This can be computed using the roughness and largest bump metrics, using the cells around the foot locationeach of the metrics defined below

Slope angle:Roughness: Computed by averaging the difference in height of each cell to the planes height at the that cell.Stability: Largest bump: This metric finds the largest deviation above the plane.Safety: refers to the area around the location..8Algorithm- State EvaluationStep metric to evaluate cost of taking a stepCost of transitionPenalty for height changeCollision check9Algorithm- State EvaluationHeuristic metric to evaluate remaining costEuclidean distanceRelative angleHeight difference10Best First Search11A* Search12Search treeSearching the State SpaceA schematic view13Search treeSearching the State SpaceA schematic view14Search treeSearching the State SpaceA schematic view15Search treeSearching the State SpaceA schematic view16Search treeSearching the State SpaceA schematic view17Search treeSearching the State SpaceA schematic view18ResultsCluttered terrain

19The planner was able to find suitable paths for a wide variety of terrain types.Figure 4 shows examples involving cluttered terrain with obstacles that can be stepped over, stepped onto, or large obstacles that must be stepped around.Cluttered terrain with obstacles that can be stepped over (left), stepped onto (center), or large obstacles that must be stepped around (right)19ResultsMulti-level terrain

20Multi-level terrain with footsteps planned over clear stairs (left), obstacle-filled stairs (center), and platforms of varying heights.20ResultsUneven ground with obstacles

21Planning over terrain with obstacles on uneven ground.21ComparisonsDistance to goalTransitions and obstacle effectsMetric weights22

A 26-transitions set

A 40-transitions setBFS

23Interestingly, the regions to the back and right relative to the robots current location can be planned significantly faster using the forward-only transitions. The reason for this difference appears to arise from having fewer movement options that actually make progress towards a goal at that location, whereas having more movement options causes the planner to expand many more relatively equal-cost paths.2324

Performance comparison of A* and BFS for increasing numbers of stairs along the pathFig: Performance comparison of A* (left) and BFS (right) for increasing numbers of stairs along the path from the initial to goal state. The transition set containing only 8 transitions is fails to find a path as soon as the first step is added.

In general, decreasing transitions decreases runtime, since the branching factor is smaller. However, this smaller number of options decreases the set of solvable maps.2425

In the presence of local minima, Best-first search can increase significantly the running time and often generates highly undesirable paths. Figure 10 shows a comparison of the output of BFS versus A* on environments with local minima. A* search outperforms Best-first in running time as well as producing optimal footstep paths.2526

In the presence of local minima, Best-first search can increase significantly the running time and often generates highly undesirable paths. Figure 10 shows a comparison of the output of BFS versus A* on environments with local minima. A* search outperforms Best-first in running time as well as producing optimal footstep paths.2627Local-minimum problem28Online Experiments

Stereo vision system

PlannerFootstep sequenceTrajectory generatorWalking area map

Following workA tired planning Strategy for biped navigation, 2004Biped navigation in rough environments using on-board sensing, 20092929Multi-Step Motion Planning for Free-climbing RobotsTim Bretl, Sanjay Lall, Jean-Claude Latombe, Stephen Rock

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