Obstacle

About: Obstacle is a research topic. Over the lifetime, 9517 publications have been published within this topic receiving 94760 citations. The topic is also known as: impediment & barrier.

A dynamical system approach to realtime obstacle avoidance

Abstract: 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 arm.

The effects of distant and on-line visual information on the control of approach phase and step over an obstacle during locomotion

Abstract: One of the goals of this study was to examine the nature and role of distant visual information sampled during locomotion in the feedforward control of leading and trailing limb while an individual is required to step over an obstacle in the travel path. In addition we were interested in whether or not on-line visual information available while the limb (lead or trail) is stepping over the obstacle influences limb trajectory control and whether the information provided during lead limb cross would be used to calibrate movement of the trail limb. Towards this end, we manipulated availability of vision following an initial dynamic sampling period during the approach phase in proximity to the obstacle and during the lead and trail limb stepping over the obstacle. Ten participants completed 40 trials of obstacle crossing in 8 testing conditions. Initial dynamic visual sampling was sufficient to ensure successful task performance in the absence of vision in the approach phase and during both lead and trail limb stepping over the obstacle. Despite successful task performance, foot placement of the lead and trail limb before obstacle crossing and limb elevation over the obstacle were increased after withdrawal of vision in the approach area. Furthermore, the correlation between toe clearance and foot placement was diminished. While both limbs require feedforward visual information to control the step over the obstacle, only lead limb elevation was influenced by availability of on-line visual information during obstacle crossing. Results were in agreement with the notion of primacy of information inherent in the optic array over those from static samples of the environment in guiding locomotion. It is suggested that the expected proprioceptive feedback information associated with the limb posture before the obstacle, reconstructed using visual memory from dynamic sampling of the environment, mismatched with those from the actual limb position. Accordingly, participants adopted a different strategy that enabled them to clear the obstacle with a higher safety margin.

Predictive Control of Autonomous Ground Vehicles With Obstacle Avoidance on Slippery Roads

Abstract: Two frameworks based on Model Predictive Control (MPC) for obstacle avoidance with autonomous vehicles are presented. A given trajectory represents the driver intent. An MPC has to safely avoid obstacles on the road while trying to track the desired trajectory by controlling front steering angle and differential braking. We present two different approaches to this problem. The first approach solves a single nonlinear MPC problem. The second approach uses a hierarchical scheme. At the high-level, a trajectory is computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model in order to avoid an obstacle. At the low-level an MPC controller computes the vehicle inputs in order to best follow the high level trajectory based on a nonlinear vehicle model. This article presents the design and comparison of both approaches, the method for implementing them, and successful experimental results on icy roads.Copyright © 2010 by ASME

Controlled Motion in an Elastic World

Abstract: The flexibility of the drives and structures of controlled motion systems are presented as an obstacle to be overcome in the design of high performance motion systems, particularly manipulator arms. The task and the measure of performance to be applied determine the technology appropriate to overcome this obstacle. Included in the technologies proposed are control algorithms (feedback and feed forward), passive damping enhancement, operational strategies, and structural design. Modeling of the distributed, nonlinear system is difficult, and alternative approaches are discussed. The author presents personal perspectives on the history, status, and future directions in this area.

Obstacle avoidance and path planning for humanoid robots using stereo vision

Abstract: This work presents methods for path planning and obstacle avoidance for the humanoid robot QRIO, allowing the robot to autonomously walk around in a home environment. For an autonomous robot, obstacle detection and localization as well as representing them in a map are crucial tasks for the success of the robot. Our approach is based on plane extraction from data captured by a stereo-vision system that has been developed specifically for QRIO. We briefly overview the general software architecture composed of perception, short and long term memory, behavior control, and motion control, and emphasize on our methods for obstacle detection by plane extraction, occupancy grid mapping, and path planning. Experimental results complete the description of our system.