Fuzzy Logic Controller Design for Intelligent Robots

This paper presents a fuzzy logic controller by which a robot can imitate biological behaviors such as avoiding obstacles or following walls. The proposed structure is implemented by integrating multiple ultrasonic sensors into a robot to collect data from a real-world environment. The decisions that govern the robot’s behavior and autopilot navigation are driven by a field programmable gate array- (FPGA-) based fuzzy logic controller. The validity of the proposed controller was demonstrated by simulating three real-world scenarios to test the bionic behavior of a custom-built robot. The results revealed satisfactorily intelligent performance of the proposed fuzzy logic controller. The controller enabled the robot to demonstrate intelligent behaviors in complex environments. Furthermore, the robot’s bionic functions satisfied its design objectives

A hexapod robot modeled on the stick insect, Carausius morosus

Robot builders have often used insects as a source of inspiration when designing their mechanical systems, due to their ability to easily navigate uneven terrain, overcome or avoid obstacles, and adjust gaits based on traveling speed. Robotics has borrowed from nature with varying degrees of abstraction, from physical appearance to observed behaviours. This paper describes the design and construction of a robotic hexapod based on the stick insect, Carausius morosus. Physically, it is an 18.8:1 scale representation of the insect with 3-DoF legs. The to-scale design was chosen to provide similar physical attributes, such as joint and leg locations, sizes, and ranges-of-motion, which will allow more meaningful comparisons between robot performance and actual insect movements (as opposed to arbitrary hexapod designs). A custom-designed leg control board is responsible for deciding leg joint movements based on a model of the neurobiological systems identified in the insect. A distributed network of six boards will be used to control the legs based on internal parameters that can be modulated by descending commands or adaptively altered by ascending sensory signals when interacting with the environment. Our final aim in this work is to add a vision system to create depth maps, which will be used as an input to a learning system, coupled with the mechanical sensory system, such that terrain that triggers reflex actions can be associated with visual cues in order to predictively avoid obstacles and potholes. © 2011 IEEE

A hexapod robot modeled on the stick insect, Carausius morosus

Robot builders have often used insects as a source of inspiration when designing their mechanical systems, due to their ability to easily navigate uneven terrain, overcome or avoid obstacles, and adjust gaits based on traveling speed. Robotics has borrowed from nature with varying degrees of abstraction, from physical appearance to observed behaviours. This paper describes the design and construction of a robotic hexapod based on the stick insect, Carausius morosus. Physically, it is an 18.8:1 scale representation of the insect with 3-DoF legs. The to-scale design was chosen to provide similar physical attributes, such as joint and leg locations, sizes, and ranges-of-motion, which will allow more meaningful comparisons between robot performance and actual insect movements (as opposed to arbitrary hexapod designs). A custom-designed leg control board is responsible for deciding leg joint movements based on a model of the neurobiological systems identified in the insect. A distributed network of six boards will be used to control the legs based on internal parameters that can be modulated by descending commands or adaptively altered by ascending sensory signals when interacting with the environment. Our final aim in this work is to add a vision system to create depth maps, which will be used as an input to a learning system, coupled with the mechanical sensory system, such that terrain that triggers reflex actions can be associated with visual cues in order to predictively avoid obstacles and potholes. © 2011 IEEE