W911NF-12-R-0011-03: Predictive Models for Sensorimotor Control of Legged Locomotion
Abstract
This project seeks to establish a predictive modeling paradigm for sensorimotor control of legged locomotion that applies to both robots--to improve legged robot autonomy--and animals--to improve bio-inspired design. The project has six specific aims that fall under three technical thrusts: 1. Intrinsic properties of locomotion mechanics Aim 1.1: Characterize impact restitution laws in models for multi-legged gaits yielding unique trajectories that vary continuously with respect to initial conditions and parameters. Aim 1.2: Develop dynamical systems theory for nonsmooth dynamics of legged locomotion, explore tradeoffs between mechanical and digital feedback. 2. Scalable computational tools for modeling and control Aim 2.1: Generalize scalable identification and estimation algorithms to apply to self-consistent models for locomotion mechanics. Aim 2.2: Apply scalable nonsmooth optimization algorithms to synthesize robot maneuvers that exploit dynamics including energy transformations and mechanical stabilization. 3. Quantitative predictions for robotics and neuromechanics Aim 3.1: Identify neuromechanical perturbation recovery strategies in cockroaches using reduced-order models. Aim 3.2: Identify a model for a legged robot and use identified model to automate synthesis of dynamic gaits and maneuvers.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 11, 2018
- Source ID
- W911NF1610158
Entities
People
- Samuel A. Burden
Organizations
- Army Contracting Command
- United States Army
- University of Washington