12 research outputs found
Impact-Aware Online Motion Planning for Fully-Actuated Bipedal Robot Walking
The ability to track a general walking path with specific timing is crucial
to the operational safety and reliability of bipedal robots for avoiding
dynamic obstacles, such as pedestrians, in complex environments. This paper
introduces an online, full-body motion planner that generates the desired
impact-aware motion for fully-actuated bipedal robotic walking. The main
novelty of the proposed planner lies in its capability of producing desired
motions in real-time that respect the discrete impact dynamics and the desired
impact timing. To derive the proposed planner, a full-order hybrid dynamic
model of fully-actuated bipedal robotic walking is presented, including both
continuous dynamics and discrete lading impacts. Next, the proposed
impact-aware online motion planner is introduced. Finally, simulation results
of a 3-D bipedal robot are provided to confirm the effectiveness of the
proposed online impact-aware planner. The online planner is capable of
generating full-body motion of one walking step within 0.6 second, which is
shorter than a typical bipedal walking step
Lyapunov-Barrier Characterization of Robust Reach-Avoid-Stay Specifications for Hybrid Systems
Stability, reachability, and safety are crucial properties of dynamical
systems. While verification and control synthesis of reach-avoid-stay
objectives can be effectively handled by abstraction-based formal methods, such
approaches can be computationally expensive due to the use of state-space
discretization. In contrast, Lyapunov methods qualitatively characterize
stability and safety properties without any state-space discretization. Recent
work on converse Lyapunov-barrier theorems also demonstrates an approximate
completeness or verifying reach-avoid-stay specifications of systems modelled
by nonlinear differential equations. In this paper, based on the topology of
hybrid arcs, we extend the Lyapunov-barrier characterization to more general
hybrid systems described by differential and difference inclusions. We show
that Lyapunov-barrier functions are not only sufficient to guarantee
reach-avoid-stay specifications for well-posed hybrid systems, but also
necessary for arbitrarily slightly perturbed systems under mild conditions.
Numerical examples are provided to illustrate the main results
Learning for Humanoid Multi-Contact Navigation Planning
Humanoids' abilities to navigate uneven terrain make them well-suited for disaster response efforts, but humanoid motion planning in unstructured environments remains a challenging problem. In this dissertation we focus on planning contact sequences for a humanoid robot navigating in large unstructured environments using multi-contact motion, including both foot and palm contacts. In particular, we address the two following questions: (1) How do we efficiently generate a feasible contact sequence? and (2) How do we efficiently generate contact sequences which lead to dynamically-robust motions?
For the first question, we propose a library-based method that retrieves motion plans from a library constructed offline, and adapts them with local trajectory optimization to generate the full motion plan from the start to the goal. This approach outperforms a conventional graph search contact planner when it is difficult to decide which contact is preferable with a simplified robot model and local environment information. We also propose a learning approach to estimate the difficulty to traverse a certain region based on the environment features. By integrating the two approaches, we propose a planning framework that uses graph search planner to find contact sequences around easy regions. When it is necessary to go through a difficult region, the framework switches to use the library-based method around the region to find a feasible contact sequence faster.
For the second question, we consider dynamic motions in contact planning. Most humanoid motion generators do not optimize the dynamic robustness of a contact sequence. By querying a learned model to predict the dynamic feasibility and robustness of each contact transition from a centroidal dynamics optimizer, the proposed planner efficiently finds contact sequences which lead to dynamically-robust motions. We also propose a learning-based footstep planner which takes external disturbances into account. The planner considers not only the poses of the planned contact sequence, but also alternative contacts near the planned contact sequence that can be used to recover from external disturbances. Neural networks are trained to efficiently predict multi-contact zero-step and one-step capturability, which allows the planner to generate contact sequences robust to external disturbances efficiently.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162908/1/linyuchi_1.pd