Multi-robot task allocation for safe planning under dynamic uncertainties

This paper considers the problem of multi-robot safe mission planning in uncertain dynamic environments. This problem arises in several applications including safety-critical exploration, surveillance, and emergency rescue missions. Computation of a multi-robot optimal control policy is challenging not only because of the complexity of incorporating dynamic uncertainties while planning, but also because of the exponential growth in problem size as a function of the number of robots. Leveraging recent works obtaining a tractable safety maximizing plan for a single robot, we propose a scalable two-stage framework to solve the problem at hand. Specifically, the problem is split into a low-level single-agent planning problem and a high-level task allocation problem. The low-level problem uses an efficient approximation of stochastic reachability for a Markov decision process to handle the dynamic uncertainty. The task allocation, on the other hand, is solved using polynomial-time forward and reverse greedy heuristics. The safety objective of our multi-robot safe planning problem allows an implementation of the greedy heuristics through a distributed auction-based approach. Moreover, by leveraging the properties of the safety objective function, we ensure provable performance bounds on the safety of the approximate solutions proposed by these two heuristics. Our result is illustrated through case studies

On Formal Methods for Collective Adaptive System Engineering. {Scalable Approximated, Spatial} Analysis Techniques. Extended Abstract

In this extended abstract a view on the role of Formal Methods in System Engineering is briefly presented. Then two examples of useful analysis techniques based on solid mathematical theories are discussed as well as the software tools which have been built for supporting such techniques. The first technique is Scalable Approximated Population DTMC Model-checking. The second one is Spatial Model-checking for Closure Spaces. Both techniques have been developed in the context of the EU funded project QUANTICOL.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200

Information Dissemination in Random Networks

Die vorliegende Dissertation beschÃ$ftigt sich mit der Dissemination von Information in einem Kommunikationsnetzwerk mit Broadcast-Kanal. Die zentrale Frage, welcher wir uns in dieser Arbeit widmen, ist wie man eine Nachricht ausgehend von einem Quellknoten effizient an alle anderen Knoten im Netzwerk verteilt. Hierbei verfolgen wir zwei Hauptziele: (1) Die Nachricht soll mit hoher Wahrscheinlichkeit alle Knoten im Netzwerk erreichen; (2) Es sollen so wenige Ébertragungen wie mÃglich stattfinden. In diesem Zusammenhang wenden wir uns hauptsÃ$chlich Algorithmen zur probabilistischen Dissemination von Information zu. Wir modellieren Kommunikationsnetzwerke als Zufallsgraphen, die auf stochastischen Prozessen beruhen. Wir verwenden Methoden der Graphentheorie sowie der stochastischen Geometrie um Disseminationsalgorithmen basierend sowohl auf Nachrichtenweiterleitung als auch auf Network Coding zu analysieren. Unser erstes Resultat ist eine analytische Studie von probabilistischem Flooding. In dieser Studie zeigen wir, wie die netzwerkweite Weiterleitungswahrscheinlichkeit gewÃ$hlt werden soll, sodass eine Nachricht mit hoher Wahrscheinlichkeit alle Knoten im Netzwerk erreicht. Als nÃ$chstes widmen wir uns der Frage, welche Vorteile ein probabilistischer Flooding-Algorithmus basierend auf Network Coding gegenÃber klassischen Methoden hat. Dabei wird die Network-Coding Methode mit dem weit verbreiteten MultiPoint Relay-Algorithmus verglichen. Der Vergleich erfolgt mittels analytischer und numerischer Methoden. Schlussendlich verwenden wir die Erkenntnisse der oben beschriebenen Studien dazu, um ein vernetztes Sensor-Aktuator-System zu entwerfen, welches als Notfallschutzsystem innerhalb von GebÃ$uden zum Einsatz kommen soll. Es soll Personen den kuerzesten sicheren Pfad zu den NotausgÃ$ngen anzeigen. Das Auffinden dieser Pfade erfolgt dabei verteilt basierend auf den Messungen der einzelnen Knoten, die Ãber das gesamte Netzwerk disseminiert werden.This dissertation focuses on the study of information dissemination in communication networks with a broadcast medium. The main problem we address is how to disseminate efficiently a message from a source node to all other network nodes. In terms of efficiency we target two goals: (1) to deliver a source message to all network nodes with high probability; and (2) to use as few transmissions as possible for a given target reachability. In this context, our main focus is devoted to probabilistic dissemination algorithms. Modeling networks as random graphs, which are built from stochastic processes, and using methods from graph theory and stochastic geometry we address both replication based and network coded information dissemination approaches. The first contribution is an analytical study of probabilistic flooding which answers the question of which is the minimum common network-wide forwarding probability each node should use such that a flooded message is obtained by all nodes with high probability. Next, we address the question of which benefits can be expected from network coded based probabilistic flooding. We compare these benefits with the ones from the well established replication based MultiPoint Relay flooding. The study of their efficiency is performed both by analytical techniques and numerical methods. Finally, we apply the insights gained from the study of information dissemination algorithms to the design of a sensor-actuator networked system for emergency response in indoor scenarios. The system guides people to the exits of a building via the shortest safe paths, computed autonomously by each node whenever a new measurement collected by a sensor is flooded throughout the network.Sérgio Armindo Lopes CrisóstomoAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in dt. und span. SpracheKlagenfurt, Alpen-Adria-Univ., Diss., 2012OeBB(VLID)241066

Software agents & human behavior

People make important decisions in emergencies. Often these decisions involve high stakes in terms of lives and property. Bhopal disaster (1984), Piper Alpha disaster (1988), Montara blowout (2009), and explosion on Deepwater Horizon (2010) are a few examples among many industrial incidents. In these incidents, those who were in-charge took critical decisions under various ental stressors such as time, fatigue, and panic. This thesis presents an application of naturalistic decision-making (NDM), which is a recent decision-making theory inspired by experts making decisions in real emergencies. This study develops an intelligent agent model that can be programed to make human-like decisions in emergencies. The agent model has three major components: (1) A spatial learning module, which the agent uses to learn escape routes that are designated routes in a facility for emergency evacuation, (2) a situation recognition module, which is used to recognize or distinguish among evolving emergency situations, and (3) a decision-support module, which exploits modules in (1) and (2), and implements an NDM based decision-logic for producing human-like decisions in emergencies. The spatial learning module comprises a generalized stochastic Petri net-based model of spatial learning. The model classifies routes into five classes based on landmarks, which are objects with salient spatial features. These classes deal with the question of how difficult a landmark turns out to be when an agent observes it the first time during a route traversal. An extension to the spatial learning model is also proposed where the question of how successive route traversals may impact retention of a route in the agent’s memory is investigated. The situation awareness module uses Markov logic network (MLN) to define different offshore emergency situations using First-order Logic (FOL) rules. The purpose of this module is to give the agent the necessary experience of dealing with emergencies. The potential of this module lies in the fact that different training samples can be used to produce agents having different experience or capability to deal with an emergency situation. To demonstrate this fact, two agents were developed and trained using two different sets of empirical observations. The two are found to be different in recognizing the prepare-to-abandon-platform alarm (PAPA ), and similar to each other in recognition of an emergency using other cues. Finally, the decision-support module is proposed as a union of spatial-learning module, situation awareness module, and NDM based decision-logic. The NDM-based decision-logic is inspired by Klein’s (1998) recognition primed decision-making (RPDM) model. The agent’s attitudes related to decision-making as per the RPDM are represented in the form of belief, desire, and intention (BDI). The decision-logic involves recognition of situations based on experience (as proposed in situation-recognition module), and recognition of situations based on classification, where ontological classification is used to guide the agent in cases where the agent’s experience about confronting a situation is inadequate. At the planning stage, the decision-logic exploits the agent’s spatial knowledge (as proposed in spatial-learning module) about the layout of the environment to make adjustments in the course of actions relevant to a decision that has already been made as a by-product of situation recognition. The proposed agent model has potential to be used to improve virtual training environment’s fidelity by adding agents that exhibit human-like intelligence in performing tasks related to emergency evacuation. Notwithstanding, the potential to exploit the basis provided here, in the form of an agent representing human fallibility, should not be ignored for fields like human reliability analysis

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Disaster Risk Analysis of the Emergency Transportation Road for Large-scale Disasters in Japan

13301乙第2075号博士(学術)金沢大学博士論文本文Full 以下に掲載：自然災害科学 35(1) pp.39-53 2016. 日本自然災害学会. 共著者：アハメド ワヒド ウッディン, 藤生 慎, 髙山 純一, 中山 晶一朗, 轟 直

Development of an efficient Ad Hoc broadcasting scheme for critical networking environments

Mobile ad hoc network has been widely deployed in support of the communications in hostile environment without conventional networking infrastructure, especially in the environments with critical conditions such as emergency rescue activities in burning building or earth quick evacuation. However, most of the existing ad hoc based broadcasting schemes either rely on GPS location or topology information or angle-of-arrival (AoA) calculation or combination of some or all to achieve high reachability. Therefore, these broadcasting schemes cannot be directly used in critical environments such as battlefield, sensor networks and natural disasters due to lack of node location and topology information in such critical environments. This research work first begins by analyzing the broadcast coverage problem and node displacement form ideal locations problem in ad hoc networks using theoretical analysis. Then, this research work proposes an efficient broadcast relaying scheme, called Random Directional Broadcasting Relay (RDBR), which greatly reduces the number of retransmitting nodes and end-to-end delay while achieving high reachability. This is done by selecting a subset of neighboring nodes to relay the packet using directional antennas without relying on node location, network topology and complex angle-of-arrival (AoA) calculations. To further improve the performance of the RDBR scheme in complex environments with high node density, high node mobility and high traffic rate, an improved RDBR scheme is proposed. The improved RDBR scheme utilizes the concept of gaps between neighboring sectors to minimize the overlap between selected relaying nodes in high density environments. The concept of gaps greatly reduces both contention and collision and at the same time achieves high reachability. The performance of the proposed RDBR schemes has been evaluated by comparing them against flooding and Distance-based schemes. Simulation results show that both proposed RDBR schemes achieve high reachability while reducing the number of retransmitting nodes and end-to-end delay especially in high density environments. Furthermore, the improved RDBR scheme achieves better performance than RDBR in high density and high traffic environment in terms of reachability, end-to-end delay and the number of retransmitting nodes

Interpretive structural model of key performance indicators for sustainable manufacturing evaluation in automotive companies

This paper aims to analyze the interrelationships among the key performance indicators of sustainable manufacturing evaluation in automotive companies. The initial key performance indicators have been identified and derived from literature and were then validated by industry survey. Interpretive structural modeling (ISM) methodology is applied to develop a hierarchical structure of the key performance indicators in three levels. Of nine indicators, there are five unstable indicators which have both high driver and dependence power, thus requiring further attention. It is believed that the model can provide a better insight for automotive managers in assessing their sustainable manufacturing performance