Contributions to the modeling and design of reconfigurable reflecting cells embedding discrete control elements

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The integration of weather forecasts and demand prediction into the energy management system of buildings is usually achieved using a model-based predictive control. The performance of such control techniques strongly depends on the accuracy of the thermal ...

This paper presents the results of a study on the effect of in-series compliance on the locomotion of a simulated 8-DoF Lola-OP Modular Snake Robot with added compliant elements. We explore whether there is an optimal stiffness for gait, terrain type, or s ...

In this article we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots, as a canonical example of cooperative robotic behavior. We use five distributed noise-resistant variations of Particle Swarm Optimizat ...

At each generation of an ant algorithm, each ant builds a solution step by step by adding an element to it. Each choice is based on the greedy force (short term profit or heuristic information) and the trail system (central memory which collects informatio ...

Radical changes are taking place in the landscape of modern power systems. This massive shift in the way the system is designed and operated has been termed the advent of the ``smart grid''. One of its implications is a strong market pull for faster power ...

Particle Swarm Optimization (PSO) is a meta-heuristic for solving high dimensional optimization problems. Due to the large number of dimensions usually employed with PSO, it is not trivial to visualize and monitor the progress of the algorithm. Because of ...

This paper presents an approach for formation control of multi-lane vehicular convoys in highways. We extend a Laplacian graph-based, distributed control law such that networked intelligent vehicles can join or leave the formation dynamically without jeopa ...

Evaluative techniques offer a tremendous potential for online controller design. However, when the optimization space is large and the performance metric is noisy, the overall adaptation process becomes extremely time consuming. Distributing the adaptation ...

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 capabili ...

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 result ...

This thesis studies the automatic design and optimization of high-performing robust controllers for mobile robots using exclusively on-board resources. Due to the often large parameter space and noisy performance metrics, this constitutes an expensive opti ...

The ability to move in complex environments is a fundamental requirement for robots to be a part of our daily lives. Increasing the controller complexity may be a desirable choice in order to obtain an improved performance. However, these two aspects may p ...

Contributions to the modeling and design of reconfigurable reflecting cells embedding discrete control elements

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Particle Swarm Optimization and Kalman Filtering for Demand Prediction of Commercial Buildings

Role of Compliance on the Locomotion of a Reconfigurable Modular Snake Robot

Distributed Particle Swarm Optimization - Particle Allocation and Neighborhood Topologies for the Learning of Cooperative Robotic Behaviors

Ant Metaheuristics with Adapted Personalities for Vehicle Routing Problem

A mixed-signal computer architecture and its application to power system problems

SwarmViz: An Open-Source Visualization Tool for Particle Swarm Optimization

Distributed Graph-based Convoy Control for Networked Intelligent Vehicles

Distributed Particle Swarm Optimization for limited-time adaptation with real robots

A Distributed Noise-Resistant Particle Swarm Optimization Algorithm for High-Dimensional Multi-Robot Learning

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

Distributed Multi-Robot Learning using Particle Swarm Optimization

The Role of Environmental and Controller Complexity in the Distributed Optimization of Multi-Robot Obstacle Avoidance