Experimental Validation of the Reliability-Aware Multi-UAV Coverage Path Planning Problem

Unmanned aerial vehicles (UAVs) have become crucial for various applications, necessitating reliable and time-constrained performance. Multi-UAV solutions offer advantages but require effective coordination. Traditional coverage path planning methods overlook uncertainties and individual UAV failures. To address this, reliability-aware multi-UAV coverage path planning methods optimise task allocation to maximise mission completion probabilities given a failure model. This paper presents an experimental validation of the reliability-aware approach, specifically an approach using a Greedy Genetic Algorithm (GGA). We evaluate the GGA performance in real-world environments, comparing mission reliability to computed reliability and comparing it against a traditional multi-UAV methods. The experimental validation demonstrates the practical viability and effectiveness of the reliability-aware approach, showing significant improvement in mission reliability despite the inevitable mismatch between real and assumed failure models

Agent as Cerebrum, Controller as Cerebellum: Implementing an Embodied LMM-based Agent on Drones

In this study, we present a novel paradigm for industrial robotic embodied agents, encapsulating an 'agent as cerebrum, controller as cerebellum' architecture. Our approach harnesses the power of Large Multimodal Models (LMMs) within an agent framework known as AeroAgent, tailored for drone technology in industrial settings. To facilitate seamless integration with robotic systems, we introduce ROSchain, a bespoke linkage framework connecting LMM-based agents to the Robot Operating System (ROS). We report findings from extensive empirical research, including simulated experiments on the Airgen and real-world case study, particularly in individual search and rescue operations. The results demonstrate AeroAgent's superior performance in comparison to existing Deep Reinforcement Learning (DRL)-based agents, highlighting the advantages of the embodied LMM in complex, real-world scenarios.Comment: 17 pages, 12 figure

Advancing automation and robotics technology for the Space Station Freedom and for the US economy

The progress made by levels 1, 2, and 3 of the Office of Space Station in developing and applying advanced automation and robotics technology is described. Emphasis is placed upon the Space Station Freedom Program responses to specific recommendations made in the Advanced Technology Advisory Committee (ATAC) progress report 10, the flight telerobotic servicer, and the Advanced Development Program. Assessments are presented for these and other areas as they apply to the advancement of automation and robotics technology for the Space Station Freedom

Human-Robot Team Task Scheduling for Planetary Surface Missions

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77042/1/AIAA-2007-2972-351.pd

A cost-effective intelligent robotic system with dual-arm dexterous coordination and real-time vision

Dexterous coordination of manipulators based on the use of redundant degrees of freedom, multiple sensors, and built-in robot intelligence represents a critical breakthrough in development of advanced manufacturing technology. A cost-effective approach for achieving this new generation of robotics has been made possible by the unprecedented growth of the latest microcomputer and network systems. The resulting flexible automation offers the opportunity to improve the product quality, increase the reliability of the manufacturing process, and augment the production procedures for optimizing the utilization of the robotic system. Moreover, the Advanced Robotic System (ARS) is modular in design and can be upgraded by closely following technological advancements as they occur in various fields. This approach to manufacturing automation enhances the financial justification and ensures the long-term profitability and most efficient implementation of robotic technology. The new system also addresses a broad spectrum of manufacturing demand and has the potential to address both complex jobs as well as highly labor-intensive tasks. The ARS prototype employs the decomposed optimization technique in spatial planning. This technique is implemented to the framework of the sensor-actuator network to establish the general-purpose geometric reasoning system. The development computer system is a multiple microcomputer network system, which provides the architecture for executing the modular network computing algorithms. The knowledge-based approach used in both the robot vision subsystem and the manipulation control subsystems results in the real-time image processing vision-based capability. The vision-based task environment analysis capability and the responsive motion capability are under the command of the local intelligence centers. An array of ultrasonic, proximity, and optoelectronic sensors is used for path planning. The ARS currently has 18 degrees of freedom made up by two articulated arms, one movable robot head, and two charged coupled device (CCD) cameras for producing the stereoscopic views, and articulated cylindrical-type lower body, and an optional mobile base. A functional prototype is demonstrated

Advancing automation and robotics technology for the space station Freedom and for the US economy

In April 1985, as required by Public Law 98-371, the NASA Advanced Technology Advisory Committee (ATAC) reported to Congress the results of its studies on advanced automation and robotics technology for use on the Freedom space station. This material was documented in the initial report (NASA Technical Memorandum 87566). A further requirement of the law was that ATAC follow NASA's progress in this area and report to Congress semiannually. This report is the eighth in a series of progress updates and covers the period between October 1, 1988, and March 31, 1989. NASA has accepted the basic recommendations of ATAC for its Space Station Freedom efforts. ATAC and NASA agree that the thrust of Congress is to build an advanced automation and robotics technology base that will support an evolutionary Space Station Freedom program and serve as a highly visible stimulator, affecting the U.S. long-term economy. The progress report identifies the work of NASA and the Freedom study contractors. It also describes research in progress, and it makes assessments of the advancement of automation and robotics technology on the Freedom space station

Fault Adaptive Workload Allocation for Complex Manufacturing Systems

This research proposes novel fault adaptive workload allocation (FAWA) strategies for the health management of complex manufacturing systems. The primary goal of these strategies is to minimize maintenance costs and maximize production by strategically controlling when and where failures occur through condition-based workload allocation. For complex systems that are capable of performing tasks a variety of different ways, such as an industrial robot arm that can move between locations using different joint angle configurations and path trajectories, each option, i.e. mission plan, will result in different degradation rates and life-expectancies. Consequently, this can make it difficult to predict when a machine will require maintenance, as it will depend not only on the type and quality of the machine, but the actual tasks and mission plans it is performing. Furthermore, effective maintenance planning becomes increasingly challenging when dealing with complex systems, such as manufacturing production lines, that have multiple machines all performing different tasks, as the different degradation rates of each task will likely cause sporadic failures, leading to excessive work stoppages and lost production. In response, this work proposes novel strategies for optimizing maintenance schedules through fault adaptive workload allocation (FAWA). This work will show how we can alternate between multiple mission plans and task assignments to control degradation across multiple components, guiding failures to occur at optimal times and locations. We will present two unique strategies for degradation control. The first strategy attempts to synchronize maintenance by utilizing multiple mission plans and task assignments, such that the healthiest components do the most work, whenever possible, in order to compensate for the more degraded components. This promotes balanced degradation and synchronized failures across all components, allowing the number of work stoppages to be minimized. The second strategy involves desynchronizing maintenance by alternating between mission plans and task assignments where the healthiest components do either the most work or the least work in order to maintain an optimal difference between component degradation rates, such that overlapping failures are minimized. In this work, FAWA is applied to several case studies involving two types of manufacturing systems: industrial robot arms and 3D printers