Neuromorphic systems for legged robot control

Abstract

Locomotion automation is a very challenging and complex problem to solve. Besides the obvious navigation problems, there are also problems regarding the environment in which navigation has to be performed. Terrains with obstacles such as rocks, steps or high inclinations, among others, pose serious difficulties to normal wheeled vehicles. The flexibility of legged locomotion is ideal for these types of terrains but this alternate form of locomotion brings with it its own challenges to be solved, caused by the high number of degrees of freedom inherent to it.

This problem is usually computationally intensive, so an alternative, using simple and hardware amenable bio-inspired systems, was studied. The goal of this thesis was to investigate if using a biologically inspired learning algorithm, integrated in a fully biologically inspired system, can improve its performance on irregular terrain by adapting its gait to deal with obstacles in its path.

At first, two different versions of a learning algorithm based on unsupervised reinforcement learning were developed and evaluated. These systems worked by correlating different events and using them to adjust the behaviour of the system so that it predicts difficult situations and adapts to them beforehand. The difference between these versions was the implementation of a mechanism that allowed for some correlations to be forgotten and suppressed by stronger ones.

Secondly, a depth from motion system was tested with unsatisfactory results. The source of the problems are analysed and discussed. An alternative system based on stereo vision was implemented, together with an obstacle detection system based on neuron and synaptic models. It is shown that this system is able to detect obstacles in the path of the robot.

After the individual systems were completed, they were integrated together and the system performance was evaluated in a series of 3D simulations using various scenarios. These simulations allowed to conclude that both learning systems were able to adapt to simple scenarios but only the one capable of forgetting past correlations was able to adjust correctly in the more complex experiments.