1,911 research outputs found
COCrIP: Compliant OmniCrawler In-pipeline Robot
This paper presents a modular in-pipeline climbing robot with a novel
compliant foldable OmniCrawler mechanism. The circular cross-section of the
OmniCrawler module enables a holonomic motion to facilitate the alignment of
the robot in the direction of bends. Additionally, the crawler mechanism
provides a fair amount of traction, even on slippery surfaces. These advantages
of crawler modules have been further supplemented by incorporating active
compliance in the module itself which helps to negotiate sharp bends in small
diameter pipes. The robot has a series of 3 such compliant foldable modules
interconnected by the links via passive joints. For the desirable pipe diameter
and curvature of the bends, the spring stiffness value for each passive joint
is determined by formulating a constrained optimization problem using the
quasi-static model of the robot. Moreover, a minimum friction coefficient value
between the module-pipe surface which can be vertically climbed by the robot
without slipping is estimated. The numerical simulation results have further
been validated by experiments on real robot prototype
Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots
Millirobots are a promising robotic platform for many applications due to
their small size and low manufacturing costs. Legged millirobots, in
particular, can provide increased mobility in complex environments and improved
scaling of obstacles. However, controlling these small, highly dynamic, and
underactuated legged systems is difficult. Hand-engineered controllers can
sometimes control these legged millirobots, but they have difficulties with
dynamic maneuvers and complex terrains. We present an approach for controlling
a real-world legged millirobot that is based on learned neural network models.
Using less than 17 minutes of data, our method can learn a predictive model of
the robot's dynamics that can enable effective gaits to be synthesized on the
fly for following user-specified waypoints on a given terrain. Furthermore, by
leveraging expressive, high-capacity neural network models, our approach allows
for these predictions to be directly conditioned on camera images, endowing the
robot with the ability to predict how different terrains might affect its
dynamics. This enables sample-efficient and effective learning for locomotion
of a dynamic legged millirobot on various terrains, including gravel, turf,
carpet, and styrofoam. Experiment videos can be found at
https://sites.google.com/view/imageconddy
Practical application of pseudospectral optimization to robot path planning
To obtain minimum time or minimum energy trajectories for robots it is necessary to employ planning methods which adequately consider the platformās dynamic properties. A variety of sampling, graph-based or local receding-horizon optimisation methods have previously been proposed. These typically use simpliļ¬ed kino-dynamic models to avoid the signiļ¬cant computational burden of solving this problem in a high dimensional state-space. In this paper we investigate solutions from the class of pseudospectral optimisation methods which have grown in favour amongst the optimal control community in recent years. These methods have high computational efficiency and rapid convergence properties. We present a practical application of such an approach to the robot path planning problem to provide a trajectory considering the robotās dynamic properties. We extend the existing literature by augmenting the path constraints with sensed obstacles rather than predeļ¬ned analytical functions to enable real world application
A Quadratic Programming Approach to Quasi-Static Whole-Body Manipulation
This paper introduces a local motion planning method for robotic systems with manipulating limbs, moving bases (legged or wheeled), and stance stability constraints arising from the presence of gravity. We formulate the problem of selecting local motions as a linearly constrained quadratic program (QP), that can be solved efficiently. The solution to this QP is a tuple of locally optimal joint velocities. By using these velocities to step towards a goal, both a path and an inverse-kinematic solution to the goal are obtained. This formulation can be used directly for real-time control, or as a local motion planner to connect waypoints. This method is particularly useful for high-degree-of-freedom mobile robotic systems, as the QP solution scales well with the number of joints. We also show how a number of practically important geometric constraints (collision avoidance, mechanism self-collision avoidance, gaze direction, etc.) can be readily incorporated into either the constraint or objective parts of the formulation. Additionally, motion of the base, a particular joint, or a particular link can be encouraged/discouraged as desired. We summarize the important kinematic variables of the formulation, including the stance Jacobian, the reach Jacobian, and a center of mass Jacobian. The method is easily extended to provide sparse solutions, where the fewest number of joints are moved, by iteration using Tibshiraniās method to accommodate an l_1 regularizer. The approach is validated and demonstrated on SURROGATE, a mobile robot with a TALON base, a 7 DOF serial-revolute torso, and two 7 DOF modular arms developed at JPL/Caltech
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