431 research outputs found
Dynamically Stable 3D Quadrupedal Walking with Multi-Domain Hybrid System Models and Virtual Constraint Controllers
Hybrid systems theory has become a powerful approach for designing feedback
controllers that achieve dynamically stable bipedal locomotion, both formally
and in practice. This paper presents an analytical framework 1) to address
multi-domain hybrid models of quadruped robots with high degrees of freedom,
and 2) to systematically design nonlinear controllers that asymptotically
stabilize periodic orbits of these sophisticated models. A family of
parameterized virtual constraint controllers is proposed for continuous-time
domains of quadruped locomotion to regulate holonomic and nonholonomic outputs.
The properties of the Poincare return map for the full-order and closed-loop
hybrid system are studied to investigate the asymptotic stabilization problem
of dynamic gaits. An iterative optimization algorithm involving linear and
bilinear matrix inequalities is then employed to choose stabilizing virtual
constraint parameters. The paper numerically evaluates the analytical results
on a simulation model of an advanced 3D quadruped robot, called GR Vision 60,
with 36 state variables and 12 control inputs. An optimal amble gait of the
robot is designed utilizing the FROST toolkit. The power of the analytical
framework is finally illustrated through designing a set of stabilizing virtual
constraint controllers with 180 controller parameters.Comment: American Control Conference 201
Dynamic Walking of Bipedal Robots on Uneven Stepping Stones via Adaptive-frequency MPC
This paper presents a novel Adaptive-frequency MPC framework for bipedal
locomotion over terrain with uneven stepping stones. In detail, we intend to
achieve adaptive foot placement and gait period for bipedal periodic walking
gait with this MPC, in order to traverse terrain with discontinuities without
slowing down. We pair this adaptive-frequency MPC with a kino-dynamics
trajectory optimization for optimal gait periods, center of mass (CoM)
trajectory, and foot placements. We use whole-body control (WBC) along with
adaptive-frequency MPC to track the optimal trajectories from the offline
optimization. In numerical validations, our adaptive-frequency MPC framework
with optimization has shown advantages over fixed-frequency MPC. The proposed
framework can control the bipedal robot to traverse through uneven stepping
stone terrains with perturbed stone heights, widths, and surface shapes while
maintaining an average speed of 1.5 m/s.Comment: 6 pages, 7 figures, 1 tabl
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