A fuzzified systematic adjustment of the robotic Darwinian PSO

The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm adapting the behavior of robots based on a set of context-based evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate, susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups of physical robots, being further explored using larger populations of simulated mobile robots within a larger scenario.

Upper Ontology for Multi-Agent Energy Systems’ Applications

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors research group has developed three multi-agent systems: MASCEM, which simulates the electricity markets; ALBidS that works as a decision support system for market players; and MASGriP, which simulates the internal operations of smart grids. To take better advantage of these systems, their integration is mandatory. For this reason, is proposed the development of an upper-ontology which allows an easier cooperation and adequate communication between them. Additionally, the concepts and rules defined by this ontology can be expanded and complemented by the needs of other simulation and real systems in the same areas as the mentioned systems. Each system’s particular ontology must be extended from this top-level ontology.

A Multi-DAG Model for Real-Time Parallel Applications with Conditional Execution

The 30th ACM/SIGAPP Symposium On Applied Computing (SAC 2015). 13 to 17, Apr, 2015, Embedded Systems. Salamanca, Spain.

Embedded Multi-Core systems for Mixed Criticality applications in dynamic and changeable real-time environments

EMC2 finds solutions for dynamic adaptability in open systems. It provides handling of mixed criticality multicore applications in r eal-time conditions, withscalability and utmost flexibility, full-scale deployment and management of integrated tool chains, through the entire lifecycle.

Multi-agent based electricity market simulator with VPP: Conceptual and implementation issues

This paper presents a new architecture for the MASCEM, a multi-agent electricity market simulator. This is implemented in a Prolog which is integrated in the JAVA program by using the LPA Win-Prolog Intelligence Server (IS) provides a DLL interface between Win-Prolog and other applications. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets.

Intelligent multi-player smart grid management considering distributed energy resources and demand response

The smart grid concept is rapidly evolving in the direction of practical implementations able to bring smart grid advantages into practice. Evolution in legacy equipment and infrastructures is not sufficient to accomplish the smart grid goals as it does not consider the needs of the players operating in a complex environment which is dynamic and competitive in nature. Artificial intelligence based applications can provide solutions to these problems, supporting decentralized intelligence and decision-making. A case study illustrates the importance of Virtual Power Players (VPP) and multi-player negotiation in the context of smart grids. This case study is based on real data and aims at optimizing energy resource management, considering generation, storage and demand response.

Supporting real-time distributed computer-controlled systems with multi-hop P-NET networks

Fieldbus communication networks aim to interconnect sensors, actuators and controllers within process control applications. Therefore, they constitute the foundation upon which real-time distributed computer-controlled systems can be implemented. P-NET is a fieldbus communication standard, which uses a virtual token-passing medium-access-control mechanism. In this paper pre-run-time schedulability conditions for supporting real-time traffic with P-NET networks are established. Essentially, formulae to evaluate the upper bound of the end-to-end communication delay in P-NET messages are provided. Using this upper bound, a feasibility test is then provided to check the timing requirements for accessing remote process variables. This paper also shows how P-NET network segmentation can significantly reduce the end-to-end communication delays for messages with stringent timing requirements.

Replica management in real-time Ada 95 applications

In this paper, we present some of the fault tolerance management mechanisms being implemented in the Multi-μ architecture, namely its support for replica non-determinism. In this architecture, fault tolerance is achieved by node active replication, with software based replica management and fault tolerance transparent algorithms. A software layer implemented between the application and the real-time kernel, the Fault Tolerance Manager (FTManager), is the responsible for the transparent incorporation of the fault tolerance mechanisms The active replication model can be implemented either imposing replica determinism or keeping replica consistency at critical points, by means of interactive agreement mechanisms. One of the Multi-μ architecture goals is to identify such critical points, relieving the underlying system from performing the interactive agreement in every Ada dispatching point.

Towards real multi-criticality scheduling

Componentised systems, in particular those with fault confinement through address spaces, are currently emerging as a hot topic in embedded systems research. This paper extends the unified rate-based scheduling framework RBED in several dimensions to fit the requirements of such systems: we have removed the requirement that the deadline of a task is equal to its period. The introduction of inter-process communication reflects the need to communicate. Additionally we also discuss server tasks, budget replenishment and the low level details needed to deal with the physical reality of systems. While a number of these issues have been studied in previous work in isolation, we focus on the problems discovered and lessons learned when integrating solutions. We report on our experiences implementing the proposed mechanisms in a commercial grade OKL4 microkernel as well as an application with soft real-time and best-effort tasks on top of it.

Preliminary discussion on globally prioritized medium access for multi-channel wireless systems

We discuss the development of a simple globally prioritized multi-channel medium access control (MAC) protocol for wireless networks. This protocol provides “hard” pre-run-time real-time guarantees to sporadic message streams, exploits a very large fraction of the capacity of all channels for “hard” real-time traffic and also makes it possible to fully utilize the channels with non real-time traffic when hard real-time messages do not request to be transmitted. The potential of such protocols for real-time applications is discussed and a schedulability analysis is also presented.

Multi-criteria optimization manipulator trajectory planning

In the last twenty years genetic algorithms (GAs) were applied in a plethora of fields such as: control, system identification, robotics, planning and scheduling, image processing, and pattern and speech recognition (Bäck et al., 1997). In robotics the problems of trajectory planning, collision avoidance and manipulator structure design considering a single criteria has been solved using several techniques (Alander, 2003). Most engineering applications require the optimization of several criteria simultaneously. Often the problems are complex, include discrete and continuous variables and there is no prior knowledge about the search space. These kind of problems are very more complex, since they consider multiple design criteria simultaneously within the optimization procedure. This is known as a multi-criteria (or multiobjective) optimization, that has been addressed successfully through GAs (Deb, 2001). The overall aim of multi-criteria evolutionary algorithms is to achieve a set of non-dominated optimal solutions known as Pareto front. At the end of the optimization procedure, instead of a single optimal (or near optimal) solution, the decision maker can select a solution from the Pareto front. Some of the key issues in multi-criteria GAs are: i) the number of objectives, ii) to obtain a Pareto front as wide as possible and iii) to achieve a Pareto front uniformly spread. Indeed, multi-objective techniques using GAs have been increasing in relevance as a research area. In 1989, Goldberg suggested the use of a GA to solve multi-objective problems and since then other researchers have been developing new methods, such as the multi-objective genetic algorithm (MOGA) (Fonseca & Fleming, 1995), the non-dominated sorted genetic algorithm (NSGA) (Deb, 2001), and the niched Pareto genetic algorithm (NPGA) (Horn et al., 1994), among several other variants (Coello, 1998). In this work the trajectory planning problem considers: i) robots with 2 and 3 degrees of freedom (dof ), ii) the inclusion of obstacles in the workspace and iii) up to five criteria that are used to qualify the evolving trajectory, namely the: joint traveling distance, joint velocity, end effector / Cartesian distance, end effector / Cartesian velocity and energy involved. These criteria are used to minimize the joint and end effector traveled distance, trajectory ripple and energy required by the manipulator to reach at destination point. Bearing this ideas in mind, the paper addresses the planning of robot trajectories, meaning the development of an algorithm to find a continuous motion that takes the manipulator from a given starting configuration up to a desired end position without colliding with any obstacle in the workspace. The chapter is organized as follows. Section 2 describes the trajectory planning and several approaches proposed in the literature. Section 3 formulates the problem, namely the representation adopted to solve the trajectory planning and the objectives considered in the optimization. Section 4 studies the algorithm convergence. Section 5 studies a 2R manipulator (i.e., a robot with two rotational joints/links) when the optimization trajectory considers two and five objectives. Sections 6 and 7 show the results for the 3R redundant manipulator with five goals and for other complementary experiments are described, respectively. Finally, section 8 draws the main conclusions.

A two-level genetic algorithm for the multi-mode resource-constrained project scheduling problem

This paper presents a genetic algorithm for the multimode resource-constrained project scheduling problem (MRCPSP), in which multiple execution modes are available for each of the activities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme by introducing an improvement procedure. It is evaluated the quality of the schedule and present detailed comparative computational results for the MRCPSP, which reveal that this approach is a competitive algorithm.

Meta-heuristics self-parameterization in a multi-agent scheduling system using case-based reasoning

This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. Finally, some conclusions are reached and future work outlined.

Sistema Multi-Agente de prestação de serviços de seguimento adaptados aos requisitos do utilizador

Mestrado em Engenharia Electrotécnica e de Computadores - Área de Especialização de Telecomunicações

Multi-agent system for personalization of location-based services

The current ubiquitous network access and increase in network bandwidth are driving the sales of mobile location-aware user devices and, consequently, the development of context-aware applications, namely location-based services. The goal of this project is to provide consumers of location-based services with a richer end-user experience by means of service composition, personalization, device adaptation and continuity of service. Our approach relies on a multi-agent system composed of proxy agents that act as mediators and providers of personalization meta-services, device adaptation and continuity of service for consumers of pre-existing location-based services. These proxy agents, which have Web services interfaces to ensure a high level of interoperability, perform service composition and take in consideration the preferences of the users, the limitations of the user devices, making the usage of different types of devices seamless for the end-user. To validate and evaluate the performance of this approach, use cases were defined, tests were conducted and results gathered which demonstrated that the initial goals were successfully fulfilled.