5,690 research outputs found
Learning Feedback Terms for Reactive Planning and Control
With the advancement of robotics, machine learning, and machine perception,
increasingly more robots will enter human environments to assist with daily
tasks. However, dynamically-changing human environments requires reactive
motion plans. Reactivity can be accomplished through replanning, e.g.
model-predictive control, or through a reactive feedback policy that modifies
on-going behavior in response to sensory events. In this paper, we investigate
how to use machine learning to add reactivity to a previously learned nominal
skilled behavior. We approach this by learning a reactive modification term for
movement plans represented by nonlinear differential equations. In particular,
we use dynamic movement primitives (DMPs) to represent a skill and a neural
network to learn a reactive policy from human demonstrations. We use the well
explored domain of obstacle avoidance for robot manipulation as a test bed. Our
approach demonstrates how a neural network can be combined with physical
insights to ensure robust behavior across different obstacle settings and
movement durations. Evaluations on an anthropomorphic robotic system
demonstrate the effectiveness of our work.Comment: 8 pages, accepted to be published at ICRA 2017 conferenc
Wavefront Propagation and Fuzzy Based Autonomous Navigation
Path planning and obstacle avoidance are the two major issues in any
navigation system. Wavefront propagation algorithm, as a good path planner, can
be used to determine an optimal path. Obstacle avoidance can be achieved using
possibility theory. Combining these two functions enable a robot to
autonomously navigate to its destination. This paper presents the approach and
results in implementing an autonomous navigation system for an indoor mobile
robot. The system developed is based on a laser sensor used to retrieve data to
update a two dimensional world model of therobot environment. Waypoints in the
path are incorporated into the obstacle avoidance. Features such as ageing of
objects and smooth motion planning are implemented to enhance efficiency and
also to cater for dynamic environments
A dynamical system approach to realtime obstacle avoidance
This paper presents a novel approach to real-time obstacle avoidance based on Dynamical Systems (DS) that ensures impenetrability of multiple convex shaped objects. The proposed method can be applied to perform obstacle avoidance in Cartesian and Joint spaces and using both autonomous and non-autonomous DS-based controllers. Obstacle avoidance proceeds by modulating the original dynamics of the controller. The modulation is parameterizable and allows to determine a safety margin and to increase the robot's reactiveness in the face of uncertainty in the localization of the obstacle. The method is validated in simulation on different types of DS including locally and globally asymptotically stable DS, autonomous and non-autonomous DS, limit cycles, and unstable DS. Further, we verify it in several robot experiments on the 7 degrees of freedom Barrett WAM ar
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