Parameter Prediction of Control Barrier Function Parameters for Robotic Manipulator Obstacle Avoidance

In this paper we present the implementation of a Control Barrier Function (CBF) safety filter that provides obstacle avoidance for a robotic manipulator arm system in a simulated environment (Simulink). CBF is a control technique that has developed over the past decade and has been extensively explored in the literature on its mathematical foundations and potential applications for a variety of safety-critical control systems. In this work we will look at the design of CBF for the robotic manipulator obstacle avoidance, discuss the selection of the CBF parameters and present a search algorithm to find parameters that provide the most efficient trajectory for different obstacles. We then create a data-set across a range of obstacle scenarios that is used to train a Neural-Network (NN) model that can be used within the control scheme to allow the system to efficiently adapt to different obstacle scenarios.