5 research outputs found
Reliable Trajectories for Dynamic Quadrupeds using Analytical Costs and Learned Initializations
Dynamic traversal of uneven terrain is a major objective in the field of
legged robotics. The most recent model predictive control approaches for these
systems can generate robust dynamic motion of short duration; however, planning
over a longer time horizon may be necessary when navigating complex terrain. A
recently-developed framework, Trajectory Optimization for Walking Robots
(TOWR), computes such plans but does not guarantee their reliability on real
platforms, under uncertainty and perturbations. We extend TOWR with analytical
costs to generate trajectories that a state-of-the-art whole-body tracking
controller can successfully execute. To reduce online computation time, we
implement a learning-based scheme for initialization of the nonlinear program
based on offline experience. The execution of trajectories as long as 16
footsteps and 5.5 s over different terrains by a real quadruped demonstrates
the effectiveness of the approach on hardware. This work builds toward an
online system which can efficiently and robustly replan dynamic trajectories.Comment: Video: https://youtu.be/LKFDB_BOhl
Simultaneous Scene Reconstruction and Whole-Body Motion Planning for Safe Operation in Dynamic Environments
Recent work has demonstrated real-time mapping and reconstruction from dense
perception, while motion planning based on distance fields has been shown to
achieve fast, collision-free motion synthesis with good convergence properties.
However, demonstration of a fully integrated system that can safely re-plan in
unknown environments, in the presence of static and dynamic obstacles, has
remained an open challenge. In this work, we first study the impact that signed
and unsigned distance fields have on optimisation convergence, and the
resultant error cost in trajectory optimisation problems in 2D path planning,
arm manipulator motion planning, and whole-body loco-manipulation planning. We
further analyse the performance of three state-of-the-art approaches to
generating distance fields (Voxblox, Fiesta, and GPU-Voxels) for use in
real-time environment reconstruction. Finally, we use our findings to construct
a practical hybrid mapping and motion planning system which uses GPU-Voxels and
GPMP2 to perform receding-horizon whole-body motion planning that can smoothly
avoid moving obstacles in 3D space using live sensor data. Our results are
validated in simulation and on a real-world Toyota Human Support Robot (HSR).Comment: 8 pages, 4 figures, 2 tables, submitted to IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS