Particle computation: Designing worlds to control robot swarms with only global signals

Micro- and nanorobots are often controlled by global input signals, such as an electromagnetic or gravitational field. These fields move each robot maximally until it hits a stationary obstacle or another stationary robot. This paper investigates 2D motion-planning complexity for large swarms of simple mobile robots (such as bacteria, sensors, or smart building material). In previous work we proved it is NP-hard to decide whether a given initial configuration can be transformed into a desired target configuration; in this paper we prove a stronger result: the problem of finding an optimal control sequence is PSPACE-complete. On the positive side, we show we can build useful systems by designing obstacles. We present a reconfigurable hardware platform and demonstrate how to form arbitrary permutations and build a compact absolute encoder. We then take the same platform and use dual-rail logic to build a universal logic gate that concurrently evaluates AND, NAND, NOR and OR operations. Using many of these gates and appropriate interconnects we can evaluate any logical expression.National Science Foundation (U.S.) (CPS-1035716

A Dynamical System Approach for Resource-Constrained Mobile Robotics

The revolution of autonomous vehicles has led to the development of robots with abundant sensors, actuators with many degrees of freedom, high-performance computing capabilities, and high-speed communication devices. These robots use a large volume of information from sensors to solve diverse problems. However, this usually leads to a significant modeling burden as well as excessive cost and computational requirements. Furthermore, in some scenarios, sophisticated sensors may not work precisely, the real-time processing power of a robot may be inadequate, the communication among robots may be impeded by natural or adversarial conditions, or the actuation control in a robot may be insubstantial. In these cases, we have to rely on simple robots with limited sensing and actuation, minimal onboard processing, moderate communication, and insufficient memory capacity. This reality motivates us to model simple robots such as bouncing and underactuated robots making use of the dynamical system techniques. In this dissertation, we propose a four-pronged approach for solving tasks in resource-constrained scenarios: 1) Combinatorial filters for bouncing robot localization; 2) Bouncing robot navigation and coverage; 3) Stochastic multi-robot patrolling; and 4) Deployment and planning of underactuated aquatic robots. First, we present a global localization method for a bouncing robot equipped with only a clock and contact sensors. Space-efficient and finite automata-based combinatorial filters are synthesized to solve the localization task by determining the robot’s pose (position and orientation) in its environment. Second, we propose a solution for navigation and coverage tasks using single or multiple bouncing robots. The proposed solution finds a navigation plan for a single bouncing robot from the robot’s initial pose to its goal pose with limited sensing. Probabilistic paths from several policies of the robot are combined artfully so that the actual coverage distribution can become as close as possible to a target coverage distribution. A joint trajectory for multiple bouncing robots to visit all the locations of an environment is incrementally generated. Third, a scalable method is proposed to find stochastic strategies for multi-robot patrolling under an adversarial and communication-constrained environment. Then, we evaluate the vulnerability of our patrolling policies by finding the probability of capturing an adversary for a location in our proposed patrolling scenarios. Finally, a data-driven deployment and planning approach is presented for the underactuated aquatic robots called drifters that creates the generalized flow pattern of the water, develops a Markov-chain based motion model, and studies the long- term behavior of a marine environment from a flow point-of-view. In a broad summary, our dynamical system approach is a unique solution to typical robotic tasks and opens a new paradigm for the modeling of simple robotics system

Learning in behavioural robotics

The research described in this thesis examines how machine learning mechanisms can be used in an assembly robot system to improve the reliability of the system and reduce the development workload, without reducing the flexibility of the system. The justification foi' this is that for a robot to be performing effectively it is frequently necessary to have gained experience of its performance under a particular configuration before that configuration can be altered to produce a performance improvement. Machine learning mechanisms can automate this activity of testing, evaluating and then changing.From studying how other researchers have developed working robot systems the activities which require most effort and experimentation are:-• The selection of the optimal parameter settings. • The establishment of the action-sensor couplings which are necessary for the effective handling of uncertainty. • Choosing which way to achieve a goal.One way to implement the first two kinds of learning is to specify a model of the coupling or the interaction of parameters and results, and from that model derive an appropriate learning mechanism that will find a parametrisation for that model that will enable good performance to be obtained. From this starting point it has been possible to show how equal, or better performance can be obtained by using iearning mechanisms which are neither derived from nor require a model of the task being learned. Instead, by combining iteration and a task specific profit function it is possible to use a generic behavioural module based on a learning mechanism to achieve the task.Iteration and a task specific profit function can also be used to learn which behavioural module from a pool of equally competent modules is the best at any one time to use to achieve a particular goal. Like the other two kinds of learning, this successfully automates an otherwise difficult test and evaluation process that would have to be performed by a developer. In doing so effectively, it, like the other learning that has been used here, shows that instead of being a peripheral issue to be introduced to a working system, learning, carried out in the right way, can be instrumental in the production of that working system

Avoin alustakehitys IEEE 802.15.4 -standardin mukaisessa langattomassa automaatiossa

This doctoral dissertation focuses on open source platform development in wireless automation under IEEE 802.15.4 standard. Research method is empirical. A platform based approach, which targets to the design of a generic open source sensor platform, was selected as a design method. The design targets were further focused by interviewing the experts from the academia and industry. Generic and modular sensor platform, the UWASA Node, was developed as an outcome of this process. Based on the implementation results, a wireless sensor and actuator network based on the UWASA Node was a feasible solution for many types of wireless automation applications. It was also possible to interface it with the other parts of the system. The targeted level of sensor platform genericity was achieved. However, it was also observed that the achieved level of genericity increased the software complexity. The development of commercial sensor platforms, which support IEEE 802.15.4 sensor networking, has narrowed down the role of open source sensor platforms, but they are not disappearing. Commercial software is usually closed and connected to a specified platform, which makes it unsuitable for research and development work. Even though there exits many commercial WSN solutions and the market expectations in this area are high, there is still a lot of work to do before the visions about Internet of Things (IoT) are fulfilled, especially in the context of distributed and locally centralized operations in the network. In terms of control engineering, one of the main research issues is to figure out how the well-known control techniques may be applied in wireless automation where WSN is part of the automation system. Open source platforms offer an important tool in this research and development work.Tämä väitöskirja käsittelee avointa alustakehitystä IEEE 802.15.4 -standardin mukaisessa langattomassa automaatiossa. Tutkimusmenetelmä on empiirinen. Työssä sovelletaan alustaperustaista suunnittelutapaa, joka tähtää yleiskäyttöisen avoimen anturialustan kehittämiseen. Suunnittelun tavoitteita tarkennettiin haastattelemalla alan asiantuntijoita teollisuudesta ja yliopistomaailmasta. Tuloksena suunniteltiin ja toteutettiin anturialusta, the UWASA Node. Implementointituloksista voidaan vetää johtopäätös, että anturialustan tavoiteltu yleiskäyttöisyystaso saavutettiin. Toisaalta saavutettu yleiskäyttöisyystaso lisäsi alustan ohjelmistoarkkitehtuurin monimutkaisuutta. Kaupallisten IEEE 802.15.4 -standardia tukevien anturialustojen tulo markkinoille vähentää avointen anturialustojen käyttöä, mutta ne eivät ole katoamassa. Kaupalliset ohjelmistot ovat tyypillisesti suljettuja ja sidoksissa tiettyyn alustaan, mikä tekee niistä sopimattomia tutkimus- ja tuotekehityskäyttöön. Vaikka nykyään on saatavilla useita kaupallisia langattomia anturi- ja toimilaiteverkkoja, vaaditaan vielä paljon työtä ennen kun kaikki esineiden Internetiin (Internet of Things) liittyvät visiot voidaan toteuttaa. Tämä koskee erityisesti langattomassa anturi- ja toimilaiteverkossa hajautetusti tai paikallisesti toteutettavia toimintoja. Säätötekniikan näkökulmasta keskeinen kysymys on, miten tunnettuja säätömenetelmiä tulee soveltaa langattomassa automaatiossa, jossa langaton anturi- ja toimilaiteverkko on osa automaatiojärjestelmää. Avoimet anturialustat ovat tärkeä työkalu sen selvittämisessä.fi=vertaisarvioitu|en=peerReviewed