Model Predictive Control for Trajectory Tracking of Unmanned Aerial Vehicles Using ROS -- Design of Fuzzy Logic Controllers to ROS-based UAVs -- Flying Multiple UAVs Using ROS -- SkiROS -- A skill-based robot control architecture on top of ROS -- Control of Mobile Robots using ActionLib -- Parametric Identification of the Dynamics of Mobile Robots and Its Application for the Tuning of Controllers in ROS -- ROSLink: Bridging ROS with the Internet-of-Things for Cloud Robotics -- A ROS Package for Dynamic Bandwidth Management in Multi-Robot Systems -- An autonomous companion UAV for the SpaceBot Cup competition 2015. .

Robot Operating System (ROS) The Complete Reference (Volume 2)

The Book of Why

Human–machine cooperation research for navigation of maritime autonomous surface ships: A review and consideration

Recent applications of autonomous agents and robots, for example, self-driving cars, scenario-based trainers, exploration robots, service robots, have brought attention to crucial trust-related problems associated with the current generation of artificial intelligence (AI) systems. AI systems particularly dominated by the connectionist deep learning neural network approach lack capabilities of explaining their decisions and actions to others, despite their great successes. They are fundamentally non-intuitive black boxes, which renders their decision or actions opaque, making it difficult to trust them in safety-critical applications. The recent stance on the explainability of AI systems has witnessed several works on eXplainable Artificial Intelligence; however, most of the studies have focused on data-driven XAI systems applied in computational sciences. Studies addressing the increasingly pervasive goal-driven agents and robots are still missing. This paper reviews works on explainable goal-driven intelligent agents and robots, focusing on techniques for explaining and communicating agents perceptual functions (for example, senses, vision, etc.) and cognitive reasoning (for example, beliefs, desires, intention, plans, and goals) with humans in the loop. The review highlights key strategies that emphasize transparency and understandability, and continual learning for explainability. Finally, the paper presents requirements for explainability and suggests a roadmap for the possible realization of effective goal-driven explainable agents and robots

Explainable Goal-Driven Agents and Robots -- A Comprehensive Review and New Framework

While AI has benefited humans, it may also harm humans if not appropriately developed. The priority of current HCI work should focus on transiting from conventional human interaction with non-AI computing systems to interaction with AI systems. We conducted a high-level literature review and a holistic analysis of current work in developing AI systems from an HCI perspective. Our review and analysis highlight the new changes introduced by AI technology and the new challenges that HCI professionals face when applying the human-centered AI (HCAI) approach in the development of AI systems. We also identified seven main issues in human interaction with AI systems, which HCI professionals did not encounter when developing non-AI computing systems. To further enable the implementation of the HCAI approach, we identified new HCI opportunities tied to specific HCAI-driven design goals to guide HCI professionals addressing these new issues. Finally, our assessment of current HCI methods shows the limitations of these methods in support of developing HCAI systems. We propose the alternative methods that can help overcome these limitations and effectively help HCI professionals apply the HCAI approach to the development of AI systems. We also offer strategic recommendation for HCI professionals to effectively influence the development of AI systems with the HCAI approach, eventually developing HCAI systems. 

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Transitioning to Human Interaction with AI Systems: New Challenges and Opportunities for HCI Professionals to Enable Human-Centered AI

Intelligent robotic autonomous systems (unmanned aerial/ground/surface/underwater vehicles) are attractive for military application to relieve humans from tedious or dangerous tasks. These systems require awareness of the environment and their own performance to reach a mission goal. This awareness enables them to adapt their operations to handle unexpected changes in the environment and uncertainty in assessments. Components of the autonomous system cannot rely on perfect awareness or actuator execution, and mistakes of one component can affect the entire system. To obtain a robust system, a system-wide approach is needed and a realistic model of all aspects of the system and its environment. In this paper, we present our study on the design and development of a fully functional autonomous system, consisting of sensors, observation processing and behavior analysis, information database, knowledge base, communication, planning processes, and actuators. The system behaves as a teammate of a human operator and can perform tasks independently with minimal interaction. The system keeps the human informed about relevant developments that may require human assistance, and the human can always redirect the system with high-level instructions. The communication behavior is implemented as a Social AI Layer (SAIL). The autonomous system was tested in a simulation environment to support rapid prototyping and evaluation. The simulation is based on the Robotic Operating System (ROS) with fully modelled sensors and actuators and the 3D graphics-enabled physics simulation software Gazebo. In this simulation, various flying and driving autonomous systems can execute their tasks in a realistic 3D environment with scripted or user-controlled threats. The results show the performance of autonomous operation as well as interaction with humans

Intelligent autonomous vehicles with an extendable knowledge base under meaningful human control

In decision support systems, information from many different sources must be integrated and interpreted to aid the process of gaining situational understanding. These systems assist users in making the right decisions, for example when under time pressure. In this work, we discuss a controlled automated support tool for gaining situational understanding, where multiple sources of information are integrated.

Kim Veltman

Papers

Intelligent autonomous vehicles with an extendable knowledge base under meaningful human control

Using Relational Concept Networks for Explainable Decision Support