Automatic Control Generation Research Articles

PuRSUE -from specification of robotic environments to synthesis of controllers

Developing robotic applications is a complex task, which requires skills that are usually only possessed by highly-qualified robotic developers. While formal methods that help developers in the creation and design of robotic applications exist, they must be explicitly customized to be impactful in the robotics domain and to support effectively the growth of the robotic market. Specifically, the robotic market is asking for techniques that: (i) enable a systematic and rigorous design of robotic applications though high-level languages; and (ii) enable the automatic synthesis of low-level controllers, which allow robots to achieve their missions. To address these problems we present the PuRSUE (Planner for RobotS in Uncontrollable Environments) approach, which aims to support developers in the rigorous and systematic design of high-level run-time control strategies for robotic applications. The approach includes PuRSUE-ML a high-level language that allows for modeling the environment, the agents deployed therein, and their missions. PuRSUE is able to check automatically whether a controller that allows robots to achieve their missions might exist and, then, it synthesizes a controller. We evaluated how PuRSUE helps designers in modeling robotic applications, the effectiveness of its automatic computation of controllers, and how the approach supports the deployment of controllers on actual robots. The evaluation is based on 13 scenarios derived from 3 different robotic applications presented in the literature. The results show that: (i) PuRSUE-ML is effective in supporting designers in the formal modeling of robotic applications compared to a direct encoding of robotic applications in low-level modeling formalisms; (ii) PuRSUE enables the automatic generation of controllers that are difficult to create manually; and (iii) the plans generated with PuRSUE are indeed effective when deployed on actual robots.

Engineered self-organization for resilient robot self-assembly with minimal surprise

In collective robotic systems, the automatic generation of controllers for complex tasks is still a challenging problem. Open-ended evolution of complex robot behaviors can be a possible solution whereby an intrinsic driver for pattern formation and self-organization may prove to be important. We implement such a driver in collective robot systems by evolving prediction networks as world models in pair with action–selection networks. Fitness is given for good predictions which causes a bias towards easily predictable environments and behaviors in the form of emergent patterns, that is, environments of minimal surprise. There is no task-dependent bias or any other explicit predetermination for the different qualities of the emerging patterns. A careful configuration of actions, sensor models, and the environment is required to stimulate the emergence of complex behaviors. We study self-assembly to increase the scenario’s complexity for our minimal surprise approach and, at the same time, limit the complexity of our simulations to a grid world to manage the feasibility of this approach. We investigate the impact of different swarm densities and the shape of the environment on the emergent patterns. Furthermore, we study how evolution can be biased towards the emergence of desired patterns. We analyze the resilience of the resulting self-assembly behaviors by causing damages to the assembled pattern and observe the self-organized reassembly of the structure. In summary, we evolved swarm behaviors for resilient self-assembly and successfully engineered self-organization in simulation. In future work, we plan to transfer our approach to a swarm of real robots.

Generation of STEP AP214 Models From Discrete Event Systems for Process Planning and Control

The aim of this paper is to show how the international standard STEP-AP214 can be used for communication and storing of process specifications. Even though there are several software tools available for the generation of both product and resource information systems, there is still a lack of tools related to the STEP standard for producing process information, e.g., sequence of operations and system capabilities for resource allocation. Therefore, such a tool is suggested, which makes use of a high-level language for discrete-event systems based on process algebra and Petri nets. This language, called process algebra Petri net), has been developed in accordance with the process relations defined in STEP-AP214. More specifically, it is shown how process specifications created with the PPN tool can be mapped to the STEP AP-214 format. Note to Practioners-Rapidly changing market needs is making demands on flexibility and ability to shorten lead times. Standards for exchanging information, as well as formal methods for automatic development of programmable controller code have been important research topics for many years. There are a lot of software tools available for the generation of both product- and resource information, but there is still a lack of tools for producing process information. Moreover, the connection between information exchange standards and such tools is absent, which makes the development of programmable controller code an isolated activity. This activity is often time consuming and performed in an ad hoc manner resulting in unnecessary production delay. The aim of this paper is to show how the international standard STEP-AP214 (a standard for exchange of product-, process-, and resource related information) can be used for communicating and storing process specifications. In order to achieve this, a tool which makes use of a formal high-level language is suggested. This tool can be used for automatic control generation and has been developed in accordance with the process relations defined in STEP-AP214. A further aim is to shown how the mentioned tool can be used to specify complex systems in a compact, yet highly readable manner, which is a crucial incentive for industry to use formal methods. The presented method will guarantee that expected information is delivered quickly and without errors caused by the human factor, something that is very important in our ambition to achieve shortened lead times. The quick information exchange also makes it possible to perform simulation, supervisor synthesis, and verification early in the development phase. This is a first attempt at using a formal language for creating a tool that can automatically generate specifications in accordance with the international STEP-standard.

Automatic generation of controllers for embodied legged organisms: a Pareto evolutionary multi-objective approach.

In this paper, we investigate the use of a self-adaptive Pareto evolutionary multi-objective optimization (EMO) approach for evolving the controllers of virtual embodied organisms. The objective of this paper is to demonstrate the trade-off between quality of solutions and computational cost. We show empirically that evolving controllers using the proposed algorithm incurs significantly less computational cost when compared to a self-adaptive weighted sum EMO algorithm, a self-adaptive single-objective evolutionary algorithm (EA) and a hand-tuned Pareto EMO algorithm. The main contribution of the self-adaptive Pareto EMO approach is its ability to produce sufficiently good controllers with different locomotion capabilities in a single run, thereby reducing the evolutionary computational cost and allowing the designer to explore the space of good solutions simultaneously. Our results also show that self-adaptation was found to be highly beneficial in reducing redundancy when compared against the other algorithms. Moreover, it was also shown that genetic diversity was being maintained naturally by virtue of the system's inherent multi-objectivity.