Emotion inspired adaptive robotic path planning

Abstract

This paper presents an emotion inspired adaptive path planning approach for autonomous robotic navigation. Ideally a robotic navigation system should adapt its path planning and behaviour to overcome a variety of obstacles within an environment, without the need for single location planning approaches. Emotional analogies are appealing as they enable general planning, but require hard coding of ‘emotions’. Humans have a bias on what is an emotion, e.g. fear, which can adversely affect performance. We aim to provide the robot with the generalising ability of emotion without the pre-specifying bias. Inspired by theories on ‘emotion’, the system presented utilises a Learning Classifier System (LCS) to learn a ‘bow-tie’ structure of emotional reinforcers to intermediary emotion categories to a behavioural modifier that adapts the robot's navigation behaviour. The emotional states are not pre-set and are judged post learning based on the learned behaviour. The bow-tie creates a simple compact set of rules to adapt a robot's behaviour to better navigate its environment. The emotion system was verified on a state-of-the-art navigation system to learn a variety of parameters that control the robot's behaviour. The results show two easy to understand learned emotional states; the first is considered to be a model ‘fear’, which increases obstacle avoidance while lowering speed when pain is induced or novelty is high. The second emotion is considered to be ‘happiness’, which increases speed and lowers wall avoidance when pain is not present. Compared to the default non-adapting navigation system, the emotional responses decreased the overall number of collisions and improved time to navigate.