Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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Planning and Navigation of Biped Walking
Real-Time Footstep Planning for Humanoid Robots among 3D Obstacles Using a Hybrid Bounding BoxIn this paper we introduce a new bounding box method for footstep planning for humanoid robots. Similar to the classic bounding box method (which uses a single rectangular box to encompass the robot) it is computationally efficient, easy to implement and can be combined with any rigid body motion planning library. However, unlike the classic bounding box method, our method takes into account the stepping over capabilities of the robot, and generates precise leg trajectories to avoid obstacles on the ground. We demonstrate that this method is well suited for footstep planning in cluttered environments.
Foot Placement for Planar Bipeds with Point FeetWhen humanoid robots are going to be used in society, they should be capable to maintain the balance. Knowing where to step appears to be crucially important to remain balanced. This paper contributes the foot placement indicator (FPI), an extension to the foot placement estimator (FPE) for planar bipeds with point feet and an arbitrary number of non-massless links. The method uses conservation of energy to determine where the planar biped needs to step to remain in balance. Simulations of the FPI show improved foot placement for balance with respect to the FPE.
A Framework for Extreme Locomotion PlanningA person practicing parkour is an incredible display of intelligent planning; he must reason carefully about his velocity and contact placement far into the future in order to locomote quickly through an environment. We seek to develop planners that will enable robotic systems to replicate this performance. An ideal planner can learn from examples and formulate feasible full-body plans to traverse a new environment. The proposed approach uses momentum equivalence to reduce the full-body system into a simplified one. Low-dimensional trajectory primitives are then composed by a sampling planner called Sampled Composition A* to produce candidate solutions that are adjusted by a trajectory optimizer and mapped to a full-body robot. Using primitives collected from a variety of sources, this technique is able to produce solutions to an assortment of simulated locomotion problems.
Adaptive Level-of-Detail Planning for Efficient Humanoid NavigationIn this paper, we consider the problem of efficient path planning for humanoid robots by combining grid-based 2D planning with footstep planning. In this way, we exploit the advantages of both frameworks, namely fast planning on grids and the ability to find solutions in situations where grid-based planning fails. Our method computes a global solution by adaptively switching between fast grid-based planning in open spaces and footstep planning in the vicinity of obstacles. To decide which planning framework to use, our approach classifies the environment into regions of different complexity with respect to the traversability. Experiments carried out in a simulated office environment and with a Nao humanoid show that (i) our approach significantly reduces the planning time compared to pure footstep planning and (ii) the resulting plans are almost as good as globally computed optimal footstep paths.
Dominant Sources of Variability in Passive WalkingThis paper investigates possible sources of variability in the dynamics of legged locomotion, even in its most idealized form. The rimless wheel model is a seemingly deterministic legged dynamic system, popular within the legged locomotion community for understanding basic collision dynamics and energetics during passive phases of walking. Despite the simplicity of this legged model, however, experimental motion capture data recording the passive step-to-step dynamics of a rimless wheel down a constant-slope terrain actually demonstrates significant variability, providing strong evidence that stochasticity is an intrinsic-and thus unavoidable-property of legged locomotion that should be modeled with care when designing reliable walking machines. We present numerical comparisons of several hypotheses as to the dominant source(s) of this variability: 1) the initial distribution of the angular velocity, 2) the uneven profile of the leg lengths and 3) the distribution of the coefficients of friction and restitution across collisions. Our analysis shows that the 3rd hypothesis most accurately predicts the noise characteristics observed in our experimental data while the 1st hypothesis is also valid for certain contexts of terrain friction. These findings suggest that variability due to ground contact dynamics, and not simply due to geometric variations more typically modeled in terrain, is important in determining the stochasticity and resulting stability of walking robots. Althou
First Steps Toward Underactuated Human-Inspired Bipedal Robotic WalkingThis paper presents the first steps toward going from human data to formal controller design to experimental realization in the context of underactuated bipedal robots. Specifically, by studying experimental human walking data, we find that specific outputs of the human, i.e., functions of the kinematics, appear to be canonical to walking and are all characterized by a single function of time, termed a human walking function. Using the human outputs and walking function, we design a human-inspired controller that drives the output of the robot to the output of the human as represented by the walking function. The main result of the paper is an optimization problem that determines the parameters of this controller so as to guarantee stable underactuated walking that is as "close" as possible to human walking. This result is demonstrated through the simulation of a physical underactuated 2D bipedal robot, AMBER. Experimentally implementing this control on AMBER through "feed-forward" control, i.e., trajectory tracking, repeatedly results in 5-10 steps.