Iñaki Navarro

An Introduction to Swarm Robotics

Swarm robotics is a field of multi-robotics in which large number of robots are coordinated in a distributed and decentralised way. It is based on the use of local rules, and simple robots compared to the complexity of the task to achieve, and inspired by social insects. Large number of simple robots can perform complex tasks in a more efficient way than a single robot, giving robustness and flexibility to the group. In this article, an overview of swarm robotics is given, describing its main properties and characteristics and comparing it to general multi-robotic systems. A review of different research works and experimental results, together with a discussion of the future swarm robotics in real world applications completes this work.

A Survey of Collective Movement of Mobile Robots

Collective movement of mobile robots is the problem of how to control a group of robots making them move as a group, in a cohesive way, towards a common direction. Collective movement serves not only to move a group of robots from one point to another, but to perform more complex tasks such as using the group of robots as a moving sensor array, collective mapping and searching tasks. In this article, a survey of collective movement of mobile robots is done, including a classification and characterization of its different types, a review of the most important architectures and a list of its promising applications.

Ten Years of Cybertech: The Educational Benefits of Bullfighting Robotics

After 10 years of organizing the Cybertech robotics competition, this paper presents this unique and innovative educational experience of teaching engineering at Universidad Politécnica de Madrid (UPM), Spain. Cybertech is not only a well-known robotic contest in Spain due to the Robotaurus bullfighting, but is also a whole academic activity spanning theory, laboratory practical lessons, seminars, tutoring, and a spectacular contest in which robots, developed by the students, compete. It is an open activity, for all students and grades, requiring knowledge of various subjects such as mechanics, microcontrollers, control, and electronics. The experience acquired has shown how this novel educational approach can boost the motivation of students, who in a real applied project effectively learn not only the particular subject matter, but also skills in teamwork, oral presentations, budget management, and so on. This is considered the flagship of innovation in education at UPM. This paper describes the evolution of Cybertech over the past 10 years, summarizes the educational experience, and provides some statistics and results as well as a perspective for future editions of the competition.

A Plume Tracking Algorithm Based on Crosswind Formations

We introduce a plume tracking algorithm based on robot formations. The algorithm is inherently designed for multi-robot systems, and requires at least two robots to collaborate. The robots try to keep themselves centered around the plume while moving upwind towards the source, and share their odor concentration and wind direction measurements with each other. In addition, robots know the relative poses of other team members. Systematic experiments with up to 5 real robots in a wind tunnel show that the robots achieve close-to-optimal performance in our scenario, and by far outperform previous approaches. The performance gain is attributed to the fact that robots continuously share information about the plume (odor concentration, wind direction) without spatially competing for acquiring it.

A framework for the collective movement of mobile robots based on distributed decisions

Abstract A novel framework for the control of the collective movement of mobile robots is presented and analyzed in this article. It allows a group of robots to move as a unique entity performing the following functions: obstacle avoidance at group level, speed control and modification of the inter-robot distance. Its flocking controller is distributed among the robots, allowing them to move in the desired common direction and maintain a desired inter-robot distance. The framework is made up of different modules that modify the behavior of the group thus allowing different functions. They are based on consensus algorithms that allow the robots to agree on different parameters, taking into account which robot has more relevant information. New modules can be easily designed and incorporated into the framework in order to augment its capabilities. It can be easily implemented on any mobile robot capable of measuring the relative positions of neighboring robots and communicating with them. It has been successfully tested using 8 real robots and in simulation with up to 40 robots, demonstrating experimentally its scalability with an increasing number of robots.

Analysis of Fitness Noise in Particle Swarm Optimization: From Robotic Learning to Benchmark Functions

Population-based learning techniques have been proven to be effective in dealing with noise and are thus promising tools for the optimization of robotic controllers, which have inherently noisy performance evaluations. This article discusses how the results and guidelines derived from tests on benchmark functions can be extended to the fitness distributions encountered in robotic learning. We show that the large-amplitude noise found in robotic evaluations is disruptive to the initial phases of the learning process of PSO. Under these conditions, neither increasing the population size nor increasing the number of iterations are efficient strategies to improve the performance of the learning. We also show that PSO is more sensitive to good spurious evaluations of bad solutions than bad evaluations of good solutions, i.e., there is a non-symmetric effect of noise on the performance of the learning.

A distributed noise-resistant Particle Swarm Optimization algorithm for high-dimensional multi-robot learning

Population-based learning techniques have been proven to be effective in dealing with noise in numerical benchmark functions and are thus promising tools for the high-dimensional optimization of controllers for multiple robots with limited sensing capabilities, which have inherently noisy performance evaluations. In this article, we apply a statistical technique called Optimal Computing Budget Allocation to improve the performance of Particle Swarm Optimization in the presence of noise for a multi-robot obstacle avoidance benchmark task. We present a new distributed PSO OCBA algorithm suitable for resource-constrained mobile robots due to its low requirements in terms of memory and limited local communication. Our results from simulation show that PSO OCBA outperforms other techniques for dealing with noise, achieving a more consistent progress and a better estimate of the ground-truth performance of candidate solutions. We then validate our simulations with real robot experiments where we compare the controller learned with our proposed algorithm to a potential field controller for obstacle avoidance in a cluttered environment. We show that they both achieve a high performance through different avoidance behaviors.

A Distributed Scalable Approach to Formation Control in Multi-robot Systems

A new algorithm for the control of formations of mobile robots is presented. Formations with a triangular lattice structure are created using distributed control rules, using only local information on each robot. The overall direction of movement of the formation is not pre-established but rather results from local interactions, giving all the robots a common, self-organized heading. Experiments were done to test the algorithm, yielding results in which robots behaved as expected, moving at a reasonable speed and maintaining the desired distances among themselves. Up to seven robots were used in real experiments and up to forty in simulation.

Distributed Learning of Cooperative Robotic Behaviors using Particle Swarm Optimization

In this paper we study the automatic synthesis of robotic controllers for the coordinated movement of multiple mobile robots. The algorithm used to learn the controllers is a noise-resistant version of Particle Swarm Optimization, which is applied in two different settings: centralized and distributed learning. In centralized learning, every robot runs the same controller and the performance is evaluated with a global metric. In the distributed learning, robots run different controllers and the performance is evaluated independently on each robot with a local metric. Our results from learning in simulation show that it is possible to learn a cooperative task in a fully distributed way employing a local metric, and we validate the simulations with real robot experiments where the best solutions from distributed and centralized learning achieve similar performances.

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

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.