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Earth observation resources are becoming increasingly indispensable in disaster relief, damage assessment, and other related domains. Many unpredictable factors, such as changes in observation task requirements, bad weather, and resource malfunctions, may cause the scheduled observation scheme to become infeasible. In these cases, it is crucial to promptly reformulate high-quality observation schemes while exerting minimal negative effects on the previously scheduled tasks. Accordingly, in this study, a bottom-up distributed coordination framework together with an improved contract net is proposed, aiming to facilitate dynamic task replanning for heterogeneous Earth observation resources. This hierarchical framework consists of three levels: 1) neighboring resource coordination; 2) single planning center coordination; and 3) multiple planning center coordination. The observation tasks affected by unpredicted factors are managed along with a bottom-up route from resources to planning centers. This bottom-up distributed coordination framework transfers part of the computing load to various nodes of the observation systems to plan tasks more efficiently and robustly. To support the prompt replanning of multiple tasks to proper Earth observation resources in dynamic environments, we propose a multiround combinatorial allocation (MCA) method. Moreover, a new float interval-based local search algorithm is proposed to quickly obtain a promising replanning scheme. The simulation results demonstrate that the MCA method can achieve a better task completion rate for large-scale tasks with satisfactory time efficiency. In addition, this method can efficiently obtain replanning schemes based on original schemes in dynamic environments. [ABSTRACT FROM AUTHOR]

With the increasing applications of wheeled mobile manipulators (WMMs), consisting of a mobile platform (MP) and a manipulator, in diverse fields, new challenges have arisen in achieving multiple tasks such as obstacle avoidance in a constrained environment during the end-effector (EE) operation. A WMM is usually redundant due to the combination of the MP and the manipulator, making multitask control possible via employing its null space. Dual-user/two-handed teleoperation of a WMM is desirable for tasks where it is important to simultaneously control the poses of both the MP and the EE. The existing teleoperation approaches for WMMs are mostly executed at the kinematic level, without considering the nonlinear rigid-body dynamics of the WMMs. In this article, a task-priority-based dual-user teleoperation framework for a WMM is implemented to perform tasks in a constrained environment. It can simultaneously manipulate the MP and the EE, the overground obstacles are avoided by telecontrolling the MP using the WMM’s null space. Any residual redundancy can be further employed for other tasks such as singularity avoidance. The stability of the entire teleoperation design is rigorously proved even with arbitrary time delays. Experiments with a dual-user teleoperation system, consisting of two local robots and an omnidirectional WMM, are conducted to verify the proposed approach’s feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

This article proposes a decentralized collaboration scheme for the motion planning of multiagent systems under coupled linear temporal logic task specifications. In order to alleviate the massive computational complexity in centralized methods, coupled edges are introduced to decouple the product automata, and then the path of each agent is synthesized according to local messages. Furthermore, in order to achieve the real-time message exchange, the tableau and gossip protocol are employed during online communication, resulting in a distributed collaboration scheme. Finally, based on the resultant decoupled product automata, a united agent model is designed to deal with partial node failures, yielding a more robust collaboration scheme. Simulations are conducted to demonstrate the effectiveness and superiority of the proposed methods. [ABSTRACT FROM AUTHOR]

This article considers heterogeneous, cold standby systems performing missions with the fixed amount of work when a failure of an operating element results in a mission failure. A system is operating in a random environment modeled by the Poisson process of shocks. Each shock decreases the remaining lifetime of an operating element and, therefore, its preventive replacement (PR) is scheduled on experiencing the predetermined number of shocks. An important feature of the discussed model is that the failure can also occur during these PRs (task transfers) with two elements involved. The duration of the task transfer depends on the time from the start of a mission. The recursive equations for obtaining the mission success probability are derived and the corresponding numerical algorithm is developed. The number of shocks triggering elements’ replacements is obtained as a solution of the formulated optimization problem. The numerical example with the detailed analysis for a set of virtual machines operating in a cloud computing environment is presented. [ABSTRACT FROM AUTHOR]

Previous crowdsourcing studies often adopted the individual-oriented approach that outsources a task to an individual worker or team formation-based approach that outsources a task to an artificially formed team of workers. Nowadays, workers are often naturally organized into groups through social networks. To address such common issue of grouped workers in real crowdsourcing systems, this article explores a novel crowdsourcing paradigm in which the task allocation targets are naturally existing worker groups but not individual workers or artificially formed teams as before. Because a natural group might not possess all required skills and needs to coordinate with other groups in the social network contexts for performing a complex task, a concept of contextual crowdsourcing value is presented to measure a group’s capacity to complete a task by coordinating with its contextual groups, which determines the priority that the group is assigned the task; then, the task allocation algorithms, including the allocations of groups and the workers actually participating in executing the task, are designed. The experiments on a real-world dataset show that our presented group-oriented approach can nearly always achieve better synergy performance, consistency performance, conflict performance, adaptability, and effectiveness on reducing costs, as compared with previous benchmark individual-oriented and team formation approaches. [ABSTRACT FROM AUTHOR]

This paper deals with real-time scheduling of homogeneous multicore platforms powered by a battery to be periodically recharged. A system is composed of reconfigurable real-time dependent and periodic tasks to be assigned to different cores interconnected by a network-on-chip (NoC). The system is subject to reconfigurations, which are automatic operations allowing the addition and/or removal of tasks as well as their exchanged messages on the NoC. Consequently, any reconfiguration can violate real-time and energy constraints on cores as well as the NoC when the energy is unavailable until the next recharge. A novel periodic task model based on elastic coefficients and a new scheduling strategy are proposed to compute useful temporal parameters allowing for tasks and messages to meet the related constraints while controlling the communication cost on the NoC. This strategy is compared with an integer linear programming-based optimal solution, and a tool named OptimalMappingTasks is developed to run different simulations that prove the originality of this paper’s contribution. [ABSTRACT FROM AUTHOR]