262,510 research outputs found
Cooperative Carrying Control for Mobile Robots in Indoor Scenario
openIn recent years, there has been a growing interest in designing multi-robot systems to provide cost-effective, fault-tolerant and reliable solutions to a variety of automated applications. In particular, from an industrial perspective, cooperative carrying techniques based on Reinforcement Learning (RL) gained a strong interest. Compared to a single robot system, this approach improves the system’s robustness and manipulation dexterity in the transportation of large objects. However, in the current state of the art, the environments’ dynamism and re-training procedure represent a considerable limitation for most of the existing cooperative carrying RL-based solutions.
In this thesis, we employ the Value Propagation Networks (VPN) algorithm for cooperative multi-robot transport scenarios. We extend and test the Delta-Q cooperation metric to V-value-based agents, and we investigate path generation algorithms and trajectory tracking controllers for differential drive robots. Moreover, we explore localization algorithms in order to take advantage of range sensors and mitigate the drift errors of wheel odometry, and we conduct experiments to derive key performance indicators of range sensors' precision. Lastly, we perform realistic industrial indoor simulations using Robot Operating System (ROS) and Gazebo 3D visualization tool, including physical objects and 6G communication constraints.
Our results showed that the proposed VPN-based algorithm outperforms the current state-of-the-art since the trajectory planning and dynamic obstacle avoidance are performed in real-time, without re-training the model, and under constant 6G network coverage.In recent years, there has been a growing interest in designing multi-robot systems to provide cost-effective, fault-tolerant and reliable solutions to a variety of automated applications. In particular, from an industrial perspective, cooperative carrying techniques based on Reinforcement Learning (RL) gained a strong interest. Compared to a single robot system, this approach improves the system’s robustness and manipulation dexterity in the transportation of large objects. However, in the current state of the art, the environments’ dynamism and re-training procedure represent a considerable limitation for most of the existing cooperative carrying RL-based solutions.
In this thesis, we employ the Value Propagation Networks (VPN) algorithm for cooperative multi-robot transport scenarios. We extend and test the Delta-Q cooperation metric to V-value-based agents, and we investigate path generation algorithms and trajectory tracking controllers for differential drive robots. Moreover, we explore localization algorithms in order to take advantage of range sensors and mitigate the drift errors of wheel odometry, and we conduct experiments to derive key performance indicators of range sensors' precision. Lastly, we perform realistic industrial indoor simulations using Robot Operating System (ROS) and Gazebo 3D visualization tool, including physical objects and 6G communication constraints.
Our results showed that the proposed VPN-based algorithm outperforms the current state-of-the-art since the trajectory planning and dynamic obstacle avoidance are performed in real-time, without re-training the model, and under constant 6G network coverage
Leveraging Semantic Web Service Descriptions for Validation by Automated Functional Testing
Recent years have seen the utilisation of Semantic Web Service descriptions for automating a wide range of service-related activities, with a primary focus on service discovery, composition, execution and mediation. An important area which so far has received less attention is service validation, whereby advertised services are proven to conform to required behavioural specifications. This paper proposes a method for validation of service-oriented systems through automated functional testing. The method leverages ontology-based and rule-based descriptions of service inputs, outputs, preconditions and effects (IOPE) for constructing a stateful EFSM specification. The specification is subsequently utilised for functional testing and validation using the proven Stream X-machine (SXM) testing methodology. Complete functional test sets are generated automatically at an abstract level and are then applied to concrete Web services, using test drivers created from the Web service descriptions. The testing method comes with completeness guarantees and provides a strong method for validating the behaviour of Web services
Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme
Behavior planning and decision-making are some of the biggest challenges for
highly automated systems. A fully automated vehicle (AV) is confronted with
numerous tactical and strategical choices. Most state-of-the-art AV platforms
implement tactical and strategical behavior generation using finite state
machines. However, these usually result in poor explainability, maintainability
and scalability. Research in robotics has raised many architectures to mitigate
these problems, most interestingly behavior-based systems and hybrid
derivatives. Inspired by these approaches, we propose a hierarchical
behavior-based architecture for tactical and strategical behavior generation in
automated driving. It is a generalizing and scalable decision-making framework,
utilizing modular behavior blocks to compose more complex behaviors in a
bottom-up approach. The system is capable of combining a variety of scenario-
and methodology-specific solutions, like POMDPs, RRT* or learning-based
behavior, into one understandable and traceable architecture. We extend the
hierarchical behavior-based arbitration concept to address scenarios where
multiple behavior options are applicable but have no clear priority against
each other. Then, we formulate the behavior generation stack for automated
driving in urban and highway environments, incorporating parking and emergency
behaviors as well. Finally, we illustrate our design in an explanatory
evaluation
Automatic Software Repair: a Bibliography
This article presents a survey on automatic software repair. Automatic
software repair consists of automatically finding a solution to software bugs
without human intervention. This article considers all kinds of repairs. First,
it discusses behavioral repair where test suites, contracts, models, and
crashing inputs are taken as oracle. Second, it discusses state repair, also
known as runtime repair or runtime recovery, with techniques such as checkpoint
and restart, reconfiguration, and invariant restoration. The uniqueness of this
article is that it spans the research communities that contribute to this body
of knowledge: software engineering, dependability, operating systems,
programming languages, and security. It provides a novel and structured
overview of the diversity of bug oracles and repair operators used in the
literature
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