  In this paper we present the implementation of a Control Barrier Function
(CBF) using a quadratic program (QP) formulation that provides obstacle
avoidance for a robotic manipulator arm system. CBF is a control technique that
has emerged and developed over the past decade and has been extensively
explored in the literature on its mathematical foundations, proof of set
invariance 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 Reinforcement Learning (RL) scheme to assist with finding parameters
values that provide the most efficient trajectory to successfully avoid
different sized obstacles. We then create a data-set across a range of
scenarios 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. Computer simulations (based on Matlab/Simulink) demonstrate the
effectiveness of the proposed algorithm.
