Exploitation of distributed solar radiation databases through a smart network: the project SoDa

The project SoDa answers the needs of industry and research for information on solar radiation parameters with a satisfactory quality. The methodology is user-driven with a large involvement of users in the project, who will gauge the progresses and achievements. A prototype service will be developed, using Internet technology, that will integrate and efficiently exploit diverse networked information sources to supply value-added information. A multi-disciplinary consortium has been assembled, which gathers companies and researchers with the necessary expertise in solar radiation and information and communication technologies. Customers and potential users are also represented as partners in the consortium via the involvement of commercial private vendors of solar radiation databases and of representatives of large research and development programs. A call is launched to recruit customers to assess the prototype. The project SoDa builds on the expertise gained in previous projects, such as the digital atlases MeteoNorm and European Solar Radiation Atlas, the Web servers Satel-Light and Helioserve, and the Guide of the Chartered Institute of Building Services Engineers of United Kingdom. Access to data and applications will be improved; efforts will be made on interpolation methods and satellite data processing to achieve better quality; emphasis will be put on applications to supply information actually needed by customers, instead of raw data

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

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 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 (PSO) algorithm. For an obstacle avoidance case study using up to eight robots, we explore in simulation the lower boundaries of these parameters and propose a set of empirical guidelines for choosing their values. We then apply these guidelines to a real robot implementation and show that it is feasible to optimize 24 control parameters per robot within 2 h, a limited amount of time determined by the robots' battery life. We also show that a hybrid simulate-and-transfer approach coupled with a noise-resistant PSO algorithm can be used to further reduce experimental time as compared to a pure real-robot implementation

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

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 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 (PSO) algorithm. For an obstacle avoidance case study using up to eight robots, we explore in simulation the lower boundaries of these parameters and propose a set of empirical guidelines for choosing their values. We then apply these guidelines to a real robot implementation and show that it is feasible to optimize 24 control parameters per robot within 2 h, a limited amount of time determined by the robots' battery life. We also show that a hybrid simulate-and-transfer approach coupled with a noise-resistant PSO algorithm can be used to further reduce experimental time as compared to a pure real-robot implementatio

MESoR - Management and exploitation of solar resource knowledge

CD-ROMKnowledge of the solar resource is essential for the planning and operation of solar energy systems. A number of data bases giving information on solar resources have been developed over the past years. The result is a fragmentation of services each having each own mechanism of access and all are giving different results due to different methods, input data and base years. The project MESoR, co-funded by the European Commission, reduces the associated uncertainty by setting up standard benchmarking rules and measures for comparing the data bases, user guidance to the application of resource data and unifying access to various data bases

SoDa: a Web service on solar radiation

ISBN 3-9809656-4-3International audienceA Web service has been developed for answering the needs of industry and research for information on solar radiation parameters with a satisfactory quality. It intends to solve the three major problems identified by customers: i) improving access to information, ii) improving knowledge on space and time structure and iii) improving matching of delivered information to actual needs of customers. This service (http://www.soda-is.com) is also innovative with respect to the Internet technologies: it is performing a smart networking of information sources of different natures: databases (radiation, meteorology, elevation, geography...) and algorithms (computation of radiation on slopes, sizing of systems...). These sources were available separately and are geographically dispersed. The service SoDa makes them cooperating and combines them in order to answer to requests, ranging from a series of irradiation values to the sizing of a system

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

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 pose a considerable challenge on the optimization of robotic controllers. In this paper, we study the trade-offs between the complexity of reactive controllers and the complexity of the environment in the optimization of multi-robot obstacle avoidance for resource-constrained platforms. The optimization is carried out in simulation using a distributed, noise-resistant implementation of Particle Swarm Optimization, and the resulting controllers are evaluated both in simulation and with real robots. We show that in a simple environment, linear controllers with only two parameters perform similarly to more complex non-linear controllers with up to twenty parameters, even though the latter ones require more evaluation time to be learned. In a more complicated environment, we show that there is an increase in performance when the controllers can differentiate between front and backwards sensors, but increasing further the number of sensors and adding non-linear activation functions provide no further benefit. In both environments, augmenting reactive control laws with simple memory capabilities causes the highest increase in performance. We also show that in the complex environment the performance measurements are noisier, the optimal parameter region is smaller, and more iterations are required for the optimization process to converge

A Comparison of PSO and Reinforcement Learning for Multi-Robot Obstacle Avoidance

The design of high-performing robotic controllers constitutes an example of expensive optimization in uncertain environments due to the often large parameter space and noisy performance metrics. There are several evaluative techniques that can be employed for on-line controller design. Adequate benchmarks help in the choice of the right algorithm in terms of final performance and evaluation time. In this paper, we use multi-robot obstacle avoidance as a benchmark to compare two different evaluative learning techniques: Particle Swarm Optimization and Q-learning. For Q-learning, we implement two different approaches: one with discrete states and discrete actions, and another one with discrete actions but a continuous state space. We show that continuous PSO has the highest fitness overall, and Q-learning with continuous states performs significantly better than Q-learning with discrete states. We also show that in the single robot case, PSO and Q-learning with discrete states require a similar amount of total learning time to converge, while the time required with Q-learning with continuous states is significantly larger. In the multi-robot case, both Q-learning approaches require a similar amount of time as in the single robot case, but the time required by PSO can be significantly reduced due to the distributed nature of the algorithm

Distributed vs. Centralized Particle Swarm Optimization for Learning Flocking Behaviors

In this paper we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots. We use a noise-resistant version of Particle Swarm Optimization to learn in simulation a set of 50 weights of a plastic artificial neural network. Two learning strategies are applied: homogeneous centralized learning, in which every robot runs the same controller and the performance is evaluated externally with a global metric, and heterogeneous distributed learning, in which robots run different controllers and the performance is evaluated independently on each robot with a local metric. The two sets of metrics enforce Reynolds’ flocking rules, resulting in a good correspondence between the metrics and the flocking behaviors obtained. Results demonstrate that it is possible to learn the collective task using both learning approaches. The solutions from the centralized learning have higher fitness and lower standard deviation than those learned in a distributed manner. We test the learned controllers in real robot experiments and also show in simulation the performance of the controllers with increasing number of robots