Author name cluster

Jason White

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

1 author row

IROS Conference 2023 Conference Paper

Real-time Dynamic Bipedal Avoidance

Tianze Wang
Jason White
Christian Hubicki

In real-world settings, bipedal robots must avoid collisions with people and their environment. Further, a biped can choose between modes of avoidance: (1) adjust its pose while standing or (2) step to gain maneuverability. We present a real-time motion planner and multibody control framework for dynamic bipedal robots that avoids multiple moving obstacles and automatically switches between standing and stepping modes as necessary. By leveraging a reduced-order model (i. e. Linear Inverted Pendulum Model) and a half-space relaxation of the safe region, the planner is formulated as a convex optimization problem (i. e. Quadratic Programming) that can be used for real-time application with Model-Predictive-Control (MPC). To facilitate mode switching, we introduce center-of-pressure related slack-variables to the convex planning optimization that both shapes the planning cost function and provides a mode switching criterion for dynamic locomotion. Finally, we implement the proposed algorithm on a 3D Cassie bipedal robot and present hardware experiments showing real-time bipedal standing avoidance, stepping avoidance, and automatic switching of avoidance modes.

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IROS Conference 2022 Conference Paper

Avoiding Dynamic Obstacles with Real-time Motion Planning using Quadratic Programming for Varied Locomotion Modes

Jason White
David Jay
Tianze Wang
Christian Hubicki

We present a real-time motion planner that avoids multiple moving obstacles without knowing their dynamics or intentions. This method uses convex optimization to generate trajectories for linear plant models over a planning horizon (i. e. model-predictive control). While convex optimizations allow for fast planning, obstacle avoidance can be challenging to incorporate because Euclidean distance calculations tend to break convexity. By using a half-space convex relaxation, our planner reasons about an approximated distance-to-obstacle measure that is linear in its decision variables and preserves convexity. Further, by iteratively updating the relaxation over the planning horizon, the half-space approximation is improved, enabling nimble avoidance maneuvers. We further augment avoidance performance with a soft penalty slack-variable for-mulation that introduces a piecewise quadratic cost. As a proof of concept, we demonstrate the planner on double-integrator models in both single-agent and multi-agent tasks-avoiding multiple obstacles and other agents in 2D and 3D environments. We show extensions to legged locomotion by bipedally walking around obstacles in simulation using the Linear Inverted Pendulum Model (LIPM). We then present two sets of hardware experiments showing real-time obstacle avoid-ance with quadcopter drones: (1) avoiding a 10m/s swinging pendulum and (2) dodging a chasing drone.

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ICRA Conference 2022 Conference Paper

Trajectory Optimization Formulation with Smooth Analytical Derivatives for Track-leg and Wheel-leg Ground Robots

Adwait Mane
Dylan Swart
Jason White
Christian Hubicki

Tracks, wheels, and legs are all useful locomotion modes for Unmanned Ground Vehicles (UGVs), and ground robots that combine these mechanisms have the potential to climb over large obstacles. As robot morphologies include more degrees of freedom and obstacles become increasingly large and complex, UGVs will need to rely on automatic motion planning to compute the joint trajectories for traversal. This article presents a trajectory optimization formulation for multibody UGVs with combined wheel-leg and track-leg designs. We derive the dynamics and constraints for rolling wheels and circulating elliptical tracks. Using direct collocation, we formulate a model-based trajectory optimization where all constraints and objectives are written in closed-form with smooth and exact derivatives for tractable computation times with existing large-scale nonlinear optimization solvers (<1 minute). We demonstrate the trajectory optimization on numerous simulated planar wheel-leg and track-leg morphologies completing locomotion tasks, demonstrating full body dynamic coupling for the multibody system. Future work will extend this formulation to 3D and include contact planning.

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ICRA Conference 2020 Conference Paper

Force-based Control of Bipedal Balancing on Dynamic Terrain with the "Tallahassee Cassie" Robotic Platform

Jason White
Dylan Swart
Christian Hubicki

Out in the field, bipedal robots need to travel on terrain that is uneven, non-rigid, and sometimes moving beneath their feet. We present a force-based double support balancing controller for such dynamic terrain scenarios for bipedal robots, and test it on the robotic bipedal platform "Tallahassee Cassie. " The presented controller relies on minimal information about the robot model, requiring its kinematics and overall weight, but not inertias of individual links or components. The controller is pelvis-centric, commanding pelvis positions in Cartesian space, which a model-free PD controller converts to motor torques in joint space. By commanding forces, torques, and a frontal center of pressure in this fashion, Tallahassee Cassie is capable of balancing on a variety of scenarios, from a lifting/sliding platform, to soft foam, to a sudden drop. These results show the potential for bipedal control to balance successfully despite minimal model information, the presence of large dynamic impacts-e. g. , falling through trap door, and soft series-spring deflections. These results motivate future work for walking and running controllers on dynamic terrain with relatively low reliance on modeling information.

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