25,644 research outputs found
A macroscopic analytical model of collaboration in distributed robotic systems
In this article, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick-pulling experiment, studied experimentally and in simulation by Ijspeert et al. [Autonomous Robots, 11, 149-171]. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through the collaboration of two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), in which computational time scales with the robot group size, the macroscopic model is computationally efficient, because its solutions are independent of robot group size. Analysis reproduces several qualitative conclusions of Ijspeert et al.: namely, the different dynamical regimes for different values of the ratio of robots to sticks, the existence of optimal control parameters that maximize system performance as a function of group size, and the transition from superlinear to sublinear performance as the number of robots is increased
Reinforcement Learning for UAV Attitude Control
Autopilot systems are typically composed of an "inner loop" providing
stability and control, while an "outer loop" is responsible for mission-level
objectives, e.g. way-point navigation. Autopilot systems for UAVs are
predominately implemented using Proportional, Integral Derivative (PID) control
systems, which have demonstrated exceptional performance in stable
environments. However more sophisticated control is required to operate in
unpredictable, and harsh environments. Intelligent flight control systems is an
active area of research addressing limitations of PID control most recently
through the use of reinforcement learning (RL) which has had success in other
applications such as robotics. However previous work has focused primarily on
using RL at the mission-level controller. In this work, we investigate the
performance and accuracy of the inner control loop providing attitude control
when using intelligent flight control systems trained with the state-of-the-art
RL algorithms, Deep Deterministic Gradient Policy (DDGP), Trust Region Policy
Optimization (TRPO) and Proximal Policy Optimization (PPO). To investigate
these unknowns we first developed an open-source high-fidelity simulation
environment to train a flight controller attitude control of a quadrotor
through RL. We then use our environment to compare their performance to that of
a PID controller to identify if using RL is appropriate in high-precision,
time-critical flight control.Comment: 13 pages, 9 figure
FAST : a fault detection and identification software tool
The aim of this work is to improve the reliability and safety of complex critical control systems by contributing to the systematic application of fault diagnosis. In order to ease the utilization of fault detection and isolation (FDI) tools in the industry, a systematic approach is required to allow the process engineers to analyze a system from this perspective. In this way, it should be possible to analyze this system to find if it provides the required fault diagnosis and redundancy according to the process criticality. In addition, it should be possible to evaluate what-if scenarios by slightly modifying the process (f.i. adding sensors or changing their placement) and evaluating the impact in terms of the fault diagnosis and redundancy possibilities.
Hence, this work proposes an approach to analyze a process from the FDI perspective and for this purpose provides the tool FAST which covers from the analysis and design phase until the final FDI supervisor implementation in a real process. To synthesize the process information, a very simple format has been defined based on XML. This format provides the needed information to systematically perform the Structural Analysis of that process. Any process can be analyzed, the only restriction is that the models of the process components need to be available in the FAST tool. The processes are described in FAST in terms of process variables, components and relations and the tool performs the structural analysis of the process obtaining: (i) the structural matrix, (ii) the perfect matching, (iii) the analytical redundancy relations (if any) and (iv) the fault signature matrix.
To aid in the analysis process, FAST can operate stand alone in simulation mode allowing the process engineer to evaluate the faults, its detectability and implement changes in the process components and topology to improve the diagnosis and redundancy capabilities. On the other hand, FAST can operate on-line connected to the process plant through an OPC interface. The OPC interface enables the possibility to connect to almost any process which features a SCADA system for supervisory control. When running in on-line mode, the process is monitored by a software agent known as the Supervisor Agent.
FAST has also the capability of implementing distributed FDI using its multi-agent architecture. The tool is able to partition complex industrial processes into subsystems, identify which process variables need to be shared by each subsystem and instantiate a Supervision Agent for each of the partitioned subsystems. The Supervision Agents once instantiated will start diagnosing their local components and handle the requests to provide the variable values which FAST has identified as shared with other agents to support the distributed FDI process.Per tal de facilitar la utilitzaciĂł d'eines per la detecciĂł i identificaciĂł de fallades (FDI) en la indĂşstria, es requereix un enfocament sistemĂ tic per permetre als enginyers de processos analitzar un sistema des d'aquesta perspectiva. D'aquesta forma, hauria de ser possible analitzar aquest sistema per determinar si proporciona el diagnosi de fallades i la redundĂ ncia d'acord amb la seva criticitat. A mĂ©s, hauria de ser possible avaluar escenaris de casos modificant lleugerament el procĂ©s (per exemple afegint sensors o canviant la seva localitzaciĂł) i avaluant l'impacte en quant a les possibilitats de diagnosi de fallades i redundĂ ncia. Per tant, aquest projecte proposa un enfocament per analitzar un procĂ©s des de la perspectiva FDI i per tal d'implementar-ho proporciona l'eina FAST la qual cobreix des de la fase d'anĂ lisi i disseny fins a la implementaciĂł final d'un supervisor FDI en un procĂ©s real. Per sintetitzar la informaciĂł del procĂ©s s'ha definit un format simple basat en XML. Aquest format proporciona la informaciĂł necessĂ ria per realitzar de forma sistemĂ tica l'AnĂ lisi Estructural del procĂ©s. Qualsevol procĂ©s pot ser analitzat, nomĂ©s hi ha la restricciĂł de que els models dels components han d'estar disponibles en l'eina FAST. Els processos es descriuen en termes de variables de procĂ©s, components i relacions i l'eina realitza l'anĂ lisi estructural obtenint: (i) la matriu estructural, (ii) el Perfect Matching, (iii) les relacions de redundĂ ncia analĂtica, si n'hi ha, i (iv) la matriu signatura de fallades. Per ajudar durant el procĂ©s d'anĂ lisi, FAST pot operar aĂŻlladament en mode de simulaciĂł permetent a l'enginyer de procĂ©s avaluar fallades, la seva detectabilitat i implementar canvis en els components del procĂ©s i la topologia per tal de millorar les capacitats de diagnosi i redundĂ ncia. Per altra banda, FAST pot operar en lĂnia connectat al procĂ©s de la planta per mitjĂ d'una interfĂcie OPC. La interfĂcie OPC permet la possibilitat de connectar gairebĂ© a qualsevol procĂ©s que inclogui un sistema SCADA per la seva supervisiĂł. Quan funciona en mode en lĂnia, el procĂ©s estĂ monitoritzat per un agent software anomenat l'Agent Supervisor. Addicionalment, FAST tĂ© la capacitat d'implementar FDI de forma distribuĂŻda utilitzant la seva arquitectura multi-agent. L'eina permet dividir sistemes industrials complexes en subsistemes, identificar quines variables de procĂ©s han de ser compartides per cada subsistema i generar una instĂ ncia d'Agent Supervisor per cadascun dels subsistemes identificats. Els Agents Supervisor un cop activats, començaran diagnosticant els components locals i despatxant les peticions de valors per les variables que FAST ha identificat com compartides amb altres agents, per tal d'implementar el procĂ©s FDI de forma distribuĂŻda.Postprint (published version
- …