Multi-Risk-RRT: An Efficient Motion Planning Algorithm for Robotic Autonomous Luggage Trolley Collection at Airports

Robots have become increasingly prevalent in dynamic and crowded environments such as airports and shopping malls. In these scenarios, the critical challenges for robot navigation are reliability and timely arrival at predetermined destinations. While existing risk-based motion planning algorithms effectively reduce collision risks with static and dynamic obstacles, there is still a need for significant performance improvements. Specifically, the dynamic environments demand more rapid responses and robust planning. To address this gap, we introduce a novel risk-based multi-directional sampling algorithm, Multi-directional Risk-based Rapidly-exploring Random Tree (Multi-Risk-RRT). Unlike traditional algorithms that solely rely on a rooted tree or double trees for state space exploration, our approach incorporates multiple sub-trees. Each sub-tree independently explores its surrounding environment. At the same time, the primary rooted tree collects the heuristic information from these sub-trees, facilitating rapid progress toward the goal state. Our evaluations, including simulation and real-world environmental studies, demonstrate that Multi-Risk-RRT outperforms existing unidirectional and bi-directional risk-based algorithms in planning efficiency and robustness

Backward assembly planning with DFA analysis

An assembly planning system that operates based on a recursive decomposition of assembly into subassemblies is presented. The planning system analyzes assembly cost in terms of stability, directionality, and manipulability to guide the generation of preferred assembly plans. The planning in this system incorporates the special processes, such as cleaning, testing, labeling, etc., that must occur during the assembly. Additionally, the planning handles nonreversible, as well as reversible, assembly tasks through backward assembly planning. In order to decrease the planning efficiency, the system avoids the analysis of decompositions that do not correspond to feasible assembly tasks. This is achieved by grouping and merging those parts that can not be decomposable at the current stage of backward assembly planning due to the requirement of special processes and the constraint of interconnection feasibility. The invention includes methods of evaluating assembly cost in terms of the number of fixtures (or holding devices) and reorientations required for assembly, through the analysis of stability, directionality, and manipulability. All these factors are used in defining cost and heuristic functions for an AO* search for an optimal plan

Using EPECs to model bilevel games in restructured electricity markets with locational prices

CWPE0619 (EPRG0602) Xinmin Hu and Daniel Ralph (Feb 2006) Using EPECs to model bilevel games in restructured electricity markets with locational prices We study a bilevel noncooperative game-theoretic model of electricity markets with locational marginal prices. Each player faces a bilevel optimization problem that we remodel as a mathematical program with equilibrium constraints, MPEC. This gives an EPEC, equilibrium problem with equilibrium constraints. We establish sufficient conditions for existence of pure strategy Nash equilibria for this class of bilevel games and give some applications. We show by examples the effect of network transmission limits, i.e. congestion, on existence of equilibria. Then we study, for more general EPECs, the weaker pure strategy concepts of local Nash and Nash stationary equilibria. We model the latter via complementarity problems, CPs. Finally, we present numerical examples of methods that attempt to find local Nash or Nash stationary equilibria of randomly generated electricity market games. The CP solver PATH is found to be rather effective in this context

Machine learning : techniques and foundations

The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples

Statistical models for genome sequence mapping

In this work we present a mapper, an algorithm to find short DNA sequences in large reference texts. Our algorithm uses the standard seed-and-extend approach, utilized by most modern mappers, combined with a novel genome annotation called neighborhood annotation. The neighborhood annotation is a data structure that contains information of similarity between sequences of the same reference. Based on this annotation, we build a statistical model to aid the processes of seeding and mapping quality estimation. Overall, our algorithm achieves higher sensitivity and more accurate estimation of mapping reliability with simulated Illumina reads, at the same speed compared to the state-of-the art algorithms. The C source code of the algorithm implementation is available at http://github.com/ezorita/mapper.En este proyecto presentamos un algoritmo de mapping. Los mappers son algoritmos utilizados para encontrar secuencias cortas de ADN en textos de referencia mucho más largos. Nuestro algoritmo utiliza la técnica estándar de seed-and-extend, utilizada por la mayoria de mappers actuales, combinada con una nueva anotación del genoma: el neighborhood annotation. Esta anotación es una estructura de datos que almacena información sobre las similitudes entre las secuencias del texto de referencia. Basandonos en esta estructura, hemos diseñado un modelo estadístico que utilizamos para favorecer los procesos de seeding y de estimación de la calidad de mapping. Finalmente, hemos implementado y testeado el rendimiento de nuestro algoritmo en secuencias simuladas de Illumina. Los resultados obtenidos muestran una mejor sensitividad y estimaciones más precisas de la fiabilidad de mapping, a la misma velocidad que los mappers del estado del arte. El código fuente de la implementación en C está disponible en open-source en http://github.com/ezorita/mapper.En aquest projecte hi presentem un algoritme de mapping. Els mappers són algoritmes que s'utilitzen per trobar seqüències curtes d'ADN en textos de referència molt grans. El nostre algoritme utilitza la tècnica estàndard de seed-and-extend, utilitzada per la majoria de mappers actuals, combinada amb una nova anotació del genoma que hem anomenat neighborhood annotation. Aquesta anotació consisteix en una estructura de dades que emmagatzema informació sobre les similaritats entre les seqüències del text de referència. Basant-nos en aquesta estructura, hem dissenyat un model estadístic que utilitzem per assistir els processos de seeding i d'estimació de la qualitat de mapping. Finalment, hem implementat i mesurat el rendiment del nostre algoritme en seqüenciacions simulades d'Illumina. Els resultats obtinguts determinen millor sensitivitat i estimacions més acurades de la fiabilitat de mapping, a la mateixa velocitat que els mappers de l'estat de l'art. El codi font de la implementació en C està disponible en open-source al web http://github.com/ezorita/mapper

Source-code Summarization of Java Methods Using Control-Flow Graphs

Source-code summarization aims to generate natural-language summaries for software artifacts (e.g., method and class). % Researchers have been exploring source-code summarization as one research area in software engineering. Various research works showed the use of text-retrieval-based techniques, heuristic-based techniques, and data-driven techniques for source-code summarization. In data-driven techniques, researchers used a sequence of source-code tokens and other representations of source code (e.g., application programming interface (API) sequences and abstract syntax tree (AST)) as an input to source-code summarization models. According to the current published literature in source-code summarization, researchers have not explored the use of a sequence extracted from control-flow graph that shows a contextual relationship between program instructions based on control-flow relationships for source-code summarization models. In this work, we employ control-flow graph representations to increase the prediction accuracy of a bi-directional long-short term memory (LSTM) source-code summarization model in terms of describing the functionality of Java methods. We use an attention-based bi-directional LSTM sequence-to-sequence model to show the use of linearized control-flow graph sequences alongside a sequence of source-code tokens. We compared our model with the current state-of-the-art and with or without a linearized control-flow graph. We created a source-code summarization dataset to train and evaluate our approach and conducted expert and automatic evaluations. In the expert evaluation, the participants gave rating for summaries generated by each model in terms of correctly describing the functionality of a Java method. Our models outperformed the state-of-the-art in terms of the mean average-rating. Also, the expert evaluation showed us the model benefit from the structural information. In the automatic evaluation, we found that the use of control-flow graphs does not increase the prediction accuracy of a bi-directional LSTM model in terms of BLEU score compared to a bi-directional LSTM model that does not use control-flow graphs. However, we found our source-code summarization approach that uses a control-flow graph as an additional representation better than encoding AST in graph neural networks. Overall, we improved the state-of-the-art for method summarization with our models that take sequence of method tokens with and without a control-flow graph