Verification of logical consistency in robotic reasoning

Most autonomous robotic agents use logic inference to keep themselves to safe and permitted behaviour. Given a set of rules, it is important that the robot is able to establish the consistency between its rules, its perception-based beliefs, its planned actions and their consequences. This paper investigates how a robotic agent can use model checking to examine the consistency of its rules, beliefs and actions. A rule set is modelled by a Boolean evolution system with synchronous semantics, which can be translated into a labelled transition system (LTS). It is proven that stability and consistency can be formulated as computation tree logic (CTL) and linear temporal logic (LTL) properties. Two new algorithms are presented to perform realtime consistency and stability checks respectively. Their implementation provides us a computational tool, which can form the basis of efficient consistency checks on-board robots

Sistema inteligente baseado na lógica paraconsistente anotada evidencial Eτ para controle e navegação de robôs móveis autônomos em um ambiente não estruturado.

Apresenta-se nesta tese um Sistema de Navegação e Controle de Robôs Móveis Autônomos baseado na Lógica Paraconsistente Anotada Evidencial Eτ através da utilização das Redes Neurais Artificiais Paraconsistentes. Esse sistema se divide em três módulos: Subsistema de Sensoriamento, Subsistema de Planejamento e Subsistema Mecânico. O funcionamento independente, mas, interligado, desses três módulos, constituem um robô móvel autônomo capaz de encontrar um ponto destino pré-determinado num ambiente não estruturado. Neste trabalho optou-se por dar maior ênfase às implementações dos Subsistemas de Sensoriamento e de Planejamento onde são aplicadas as técnicas de construção dos algoritmos baseados na Lógica Paraconsistente Anotada Evidencial Eτ. Os resultados envolvendo os algoritmos nesses dois Subsistemas mostraram-se muito promissores, capacitando-os a serem empregados com êxito em sistemas de navegação móvel autônoma. Como trabalho futuro, deixa-se a sugestão de construção do Subsistema Mecânico sobre uma plataforma similar com a construída no Robô Emmy II que, em trabalho anterior, utilizou procedimentos de controle baseados na Lógica Paraconsistente Evidencial Eτ

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. They can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a walking robot is a challenging task. In this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a biomechanical walking robot. The turning information is transmitted as descending steering signals to the locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations as well as escaping from sharp corners or deadlocks. Using backbone joint control embedded in the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments

Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems

In future autonomous systems, wireless multi-hop communication is key to enable collaboration among distributed agents at low cost and high flexibility. When many agents need to transmit information over the same wireless network, communication becomes a shared and contested resource. Event-triggered and self-triggered control account for this by transmitting data only when needed, enabling significant energy savings. However, a solution that brings those benefits to multi-hop networks and can reallocate freed up bandwidth to additional agents or data sources is still missing. To fill this gap, we propose control-guided communication, a novel co-design approach for distributed self-triggered control over wireless multi-hop networks. The control system informs the communication system of its transmission demands ahead of time, and the communication system allocates resources accordingly. Experiments on a cyber-physical testbed show that multiple cart-poles can be synchronized over wireless, while serving other traffic when resources are available, or saving energy. These experiments are the first to demonstrate and evaluate distributed self-triggered control over low-power multi-hop wireless networks at update rates of tens of milliseconds.Comment: Accepted final version to appear in: IEEE Control Systems Letter