Emotion inspired adaptive robotic path planning

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.

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