Robot Evolution for Autonomous Behavior in Dynamic Environments

EasyChair Preprint 11545

Abstract

This paper introduces a novel robot parallel evolution design algorithm,
leveraging the concept of a module network, to optimize the learning
process of collision avoidance, approach, and wall switching behaviors
in evolutionary robots. The proposed algorithm is validated and
tested, demonstrating its efficacy in enabling evolutionary robots to autonomously
exhibit behaviors such as collision avoidance, movement, replication,
and attack.
The learning methodology focuses on refining the neural network-based
strategies for collision avoidance, approach, and wall switching behaviors.
The evolutionary robots, operating in a simulated environment, showcase
the ability to adapt and enhance their performance over time. The
simulation environment includes randomly generated rectangular obstacles
with varying side lengths, strategically placed to represent real-world
challenges. Additionally, the environment features randomly scattered
approach targets, serving as goals for the robots.
The modular design of the neural network allows for the integration of
fundamental behaviors such as collision avoidance and approach, enabling
a progressive enhancement of the robot’s capabilities. As the neural network
evolves, the robots demonstrate an increasingly sophisticated ability
to navigate their surroundings, avoid obstacles, approach targets, and
adapt to dynamic scenarios.
Through extensive simulations, the proposed algorithm proves effective
in training evolutionary robots to navigate complex environments
autonomously. The study contributes to the field of evolutionary robotics
by presenting a modular neural network approach that enables the gradual
acquisition and integration of diverse behaviors, showcasing the potential
for autonomous and adaptive robotic systems in dynamic and challenging
environments.

Keyphrases: autonomous behavior, environments, robot evolution

@booklet{EasyChair:11545,
  author    = {Budee U Zaman},
  title     = {Robot Evolution for Autonomous Behavior in Dynamic Environments},
  howpublished = {EasyChair Preprint 11545},
  year      = {EasyChair, 2023}}