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Distributed Particle Swarm Optimization for Limited Time Adaptation in Autonomous Robots

  • Authors: Di Mario, Ezequiel; Martinoli, Alcherio

Evaluative techniques offer a tremendous potential for on-line controller design. However, when the optimization space is large and the performance metric is noisy, the time needed to properly evaluate candidate solutions becomes prohibitively large and, as a consequence, the overall adaptation process becomes extremely time consuming. Distributing the adaptation process reduces the required time and increases robustness to failure of individual agents. In this paper, we analyze the role of the four algorithmic parameters that determine the total evaluation time in a distributed implementation of a Particle Swarm Optimization algorithm. For a multi-robot obstacle avoidance case study, we explore in simulation the lower boundaries of these parameters with the goal of reducing the total evaluation time so that it is feasible to implement the adaptation process within a limited amount of time determined by the robots’ energy autonomy. We show that each parameter has a different impact on the final fitness and propose some guidelines for choosing these parameters for real robot implementations.

Posted on: November 20, 2012

The Effect of the Environment in the Synthesis of Robotic Controllers: A Case Study in Multi-Robot Obstacle Avoidance using Distributed Particle Swarm Optimization

  • Authors: Di Mario, Ezequiel; Navarro, Inaki; Martinoli, Alcherio

The ability to move in complex environments is a fundamental requirement for robots to be a part of our daily lives. While in simple environments it is usually straightforward for human designers to foresee the different conditions a robot will be exposed to, for more complex environments the human design of high-performing controllers becomes a challenging task, especially when the on-board resources of the robots are limited. In this article, we use a distributed implementation of Particle Swarm Optimization to design robotic controllers that are able to navigate around obstacles of different shape and size. We analyze how the behavior and performance of the controllers differ based on the environment where learning takes place, showing that different arenas lead to different avoidance behaviors. We also test the best controllers in environments not encountered during learning, both in simulation and with real robots, and show that no single learning environment is able to generate a behavior general and robust enough to succeed in all testing environments.

Posted on: July 1, 2013