Legged robots that balance
Converse Lyapunov functions for exponentially stable periodic orbits
Systems & Control Letters
RHex: A Biologically Inspired Hexapod Runner
Autonomous Robots
Dynamically-Stable Motion Planning for Humanoid Robots
Autonomous Robots
Sampling-based motion planning with differential constraints
Sampling-based motion planning with differential constraints
Planning Algorithms
Motion planning for legged and humanoid robots
Motion planning for legged and humanoid robots
Path planning in 1000+ dimensions using a task-space Voronoi bias
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Dynamically diverse legged locomotion for rough terrain
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
CHOMP: gradient optimization techniques for efficient motion planning
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Reachability-guided sampling for planning under differential constraints
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Maneuver-based motion planning for nonlinear systems with symmetries
IEEE Transactions on Robotics
Stable dynamic walking over uneven terrain
International Journal of Robotics Research
Finite-time regional verification of stochastic non-linear systems
International Journal of Robotics Research
Path planning for crawler crane using RRT*
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Dynamic walking and whole-body motion planning for humanoid robots: an integrated approach
International Journal of Robotics Research
A direct method for trajectory optimization of rigid bodies through contact
International Journal of Robotics Research
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A motion planning algorithm is described for bounding over rough terrain with the LittleDog robot. Unlike walking gaits, bounding is highly dynamic and cannot be planned with quasi-steady approximations. LittleDog is modeled as a planar five-link system, with a 16-dimensional state space; computing a plan over rough terrain in this high-dimensional state space that respects the kinodynamic constraints due to underactuation and motor limits is extremely challenging. Rapidly Exploring Random Trees (RRTs) are known for fast kinematic path planning in high-dimensional configuration spaces in the presence of obstacles, but search efficiency degrades rapidly with the addition of challenging dynamics. A computationally tractable planner for bounding was developed by modifying the RRT algorithm by using: (1) motion primitives to reduce the dimensionality of the problem; (2) Reachability Guidance, which dynamically changes the sampling distribution and distance metric to address differential constraints and discontinuous motion primitive dynamics; and (3) sampling with a Voronoi bias in a lower-dimensional â聙聹task spaceâ聙聺 for bounding. Short trajectories were demonstrated to work on the robot, however open-loop bounding is inherently unstable. A feedback controller based on transverse linearization was implemented, and shown in simulation to stabilize perturbations in the presence of noise and time delays.