European researchers have developed a robot capable of moving autonomously using humanlike visual processing. The robot is helping the researchers explore how the brain responds to its environment while the body is in motion. What they discover could lead to machines that are better able to navigate through cluttered environments.
The robot consists of a wheeled platform with a robotic “head” that uses two cameras to capture stereoscopic vision. The robot can turn its head and shift its gaze up and down or sideways to gauge its surroundings, and can quickly measure its own speed relative to its environment.
The machine is controlled by algorithms designed to mimic different parts of the human visual system. Rather than capturing and mapping its surroundings over and over in order to plan its route–the way most robots do–the European machine uses a simulated neural network to update its position relative to the environment, continually adjusting to each new input. This mimics human visual processing and movement planning.
Mark Greenlee, the chair for experimental psychology at Germany’s University of Regensburg and the coordinator of the project, says that computer models of the human brain need to be validated by experiment. The robot mimics several different functions of the human brain–object recognition, motion estimation, and decision making–to navigate around a room, heading for specific targets while avoiding obstacles and walls.
Ten different European research groups, each with expertise in fields including neuroscience, computer science, and robotics, designed and built the robot through a project called Decisions in Motion. The group’s challenge was to pull together traditionally disparate fields of neuroscience and integrate them into a “coherent model architecture,” says Heiko Neumann, a professor at the Vision and Perception Lab at the University of Ulm, in Germany, who helped develop the algorithms that control the robot’s motion.
Normally, Neumann says, neuroscientists focus on a particular aspect of vision and motion. For example, some study the “ventral stream” of the visual cortex, which is related to object recognition, while others study the “dorsal stream,” related to motion estimation from the environment, or “optic flow.” Still others study how the brain decides to move based on input from both the dorsal and ventral streams. But to develop a real, humanlike computer model for navigation, the researchers needed to incorporate all these aspects into one system.
To do this, Greenlee’s team started by studying fMRI brain images captured while people were moving around obstacles. The researchers passed on the findings to Neumann’s group, which developed algorithms designed to mimic the brain’s ability to detect the body’s motion, as well as that of objects moving through in the surrounding environment. The researchers also incorporated algorithms developed by participant Simon Thorpe of the French National Center for Scientific Research (CNRS). Thorpe’s SpikeNet software uses the order of neuron firing in a neural network to simulate how the brain quickly recognizes objects, rather than a more traditional approach based on neuron firing rate.
Smaller design teams can now prototype and deploy faster.