Authors:
Daniel Schmidt
and
Karsten Berns
Affiliation:
University of Kaiserslautern, Germany
Keyword(s):
Genetic Algorithm, Behavior-based, Risk Prediction, Climbing Robot.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computation and Control
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Mobile Robots and Autonomous Systems
;
Perception and Awareness
;
Robotics and Automation
;
Soft Computing
Abstract:
Risk analysis in combination with terrain classification is a common approach in mobile robotics to adapt
robot control to surface conditions. But for climbing robots it is hard to specify, how the robotic system
and especially the adhesion is affected by different surfaces and environmental features. This paper will
introduce the climbing robot CROMSCI using negative pressure adhesion via multiple chambers, adaptive
inflatable sealings and an omnidirectional drive system. It presents the used behavior-based control network
which allows the balancing of adhesion force, but fails in extreme situations. Therefore, a risk prediction has
been developed which evaluates behavioral meta-data and allows an estimation of current hazards caused by
the environment. This prediction is used to perform evasive actions to prevent the robot from falling down.