7 research outputs found
Preliminary survey of backdrivable linear actuators for humanoid robots
This paper presents a preliminary survey of the use of direct drive linear motors for joint actuation of a humanoid robot. Their prime asset relies on backdrivability, a significant feature to properly cushion high impacts between feet and ground during dynamic walking or running. Our long-term goal is the design of high performance human size bipedal walking robots. However, this paper focuses on a preliminary feasibility study: the design and experimentation of a mono-actuator lower limb
Control of A High Performance Bipedal Robot using Viscoelastic Liquid Cooled Actuators
This paper describes the control, and evaluation of a new human-scaled biped
robot with liquid cooled viscoelastic actuators (VLCA). Based on the lessons
learned from previous work from our team on VLCA [1], we present a new system
design embodying a Reaction Force Sensing Series Elastic Actuator (RFSEA) and a
Force Sensing Series Elastic Actuator (FSEA). These designs are aimed at
reducing the size and weight of the robot's actuation system while inheriting
the advantages of our designs such as energy efficiency, torque density, impact
resistance and position/force controllability. The system design takes into
consideration human-inspired kinematics and range-of-motion (ROM), while
relying on foot placement to balance. In terms of actuator control, we perform
a stability analysis on a Disturbance Observer (DOB) designed for force
control. We then evaluate various position control algorithms both in the time
and frequency domains for our VLCA actuators. Having the low level baseline
established, we first perform a controller evaluation on the legs using
Operational Space Control (OSC) [2]. Finally, we move on to evaluating the full
bipedal robot by accomplishing unsupported dynamic walking by means of the
algorithms to appear in [3].Comment: 8 pages, 8 figure
Compliant actuators that mimic biological muscle performance with applications in a highly biomimetic robotic arm
This paper endeavours to bridge the existing gap in muscular actuator design
for ligament-skeletal-inspired robots, thereby fostering the evolution of these
robotic systems. We introduce two novel compliant actuators, namely the
Internal Torsion Spring Compliant Actuator (ICA) and the External Spring
Compliant Actuator (ECA), and present a comparative analysis against the
previously conceived Magnet Integrated Soft Actuator (MISA) through
computational and experimental results. These actuators, employing a
motor-tendon system, emulate biological muscle-like forms, enhancing artificial
muscle technology. A robotic arm application inspired by the skeletal ligament
system is presented. Experiments demonstrate satisfactory power in tasks like
lifting dumbbells (peak power: 36W), playing table tennis (end-effector speed:
3.2 m/s), and door opening, without compromising biomimetic aesthetics.
Compared to other linear stiffness serial elastic actuators (SEAs), ECA and ICA
exhibit high power-to-volume (361 x 10^3 W/m) and power-to-mass (111.6 W/kg)
ratios respectively, endorsing the biomimetic design's promise in robotic
development
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