Reconfiguration Tasks Research Articles

On Lyapunov stability of artificial potential function-based low-thrust constellation reconfiguration control

The configuration control of constellations which consist of numerous satellites presents significant challenges. Such space systems demand the capability for autonomous reconfiguration to accomplish specific missions. Individual satellites within constellations are primarily equipped with low-thrust systems, rendering traditional impulse control methods for satellite formation and mission planning ineffective. To address this issue, this paper proposes a low-thrust constellations reconfiguration control method utilizing an artificial potential function. Relying exclusively on neighboring state information, satellites can dynamically modulate their control efforts in real-time. The stability of the proposed reconfiguration control method is established under modelable, bounded perturbation using Lyapunov theory. Furthermore, a repulsive potential function is formulated to ensure collision avoidance among satellites. Numerical results indicate that the proposed method is stable, computationally efficient, and guarantees the safe distance during maneuvers. Consequently, this method is ideally suited for reconfiguration tasks across a spectrum of large space systems, including mega-constellations.

A Novel Combination of Genetic Algorithm, Particle Swarm Optimization, and Teaching-Learning-Based Optimization for Distribution Network Reconfiguration in Case of Faults

Reconfiguring distribution networks involves modifying their topological structure by managing switch states. This process is crucial in smart grids, as it can isolate faults, minimize power loss, and enhance system stability. However, in existing research, the reconfiguration task is often treated as a problem of either single- or multi-objective optimization and frequently overlooks the issue's multimodality. As a result, the solutions derived may be inadequate or unfeasible when facing environmental changes. In this study, the objective function of minimizing power loss considers the case of faults in the distribution grid. Coordinating the initial population division of the Genetic Algorithm (GA) with the Particle Swarm Optimization (PSO) and the Teaching and Learning-Based Optimization (TLBO) algorithms accelerates the process of finding the optimal solution, resulting in faster and more reliable results. The proposed method was tested on the IEEE-33 bus test system and was compared with other methods, demonstrating reliable results and superior efficiency.

PLC orchestration automation to enhance human–machine integration in adaptive manufacturing systems

Current approaches to manufacturing must evolve to respond to increasing demands for short product life cycles and customised products. Adaptive manufacturing systems integrate advanced technologies, automation, and data-driven methodologies to develop adaptable, efficient, and responsive production processes. Central to this concept is the emphasis on human involvement and fostering synergy between human operators and the manufacturing system. Significant changes to the system’s controller are required to achieve adaptivity, with programmable logic controllers (PLCs) being a common controller type. After the necessary changes to the configuration of the manufacturing system, the PLC should be reconfigured to orchestrate the new required behaviour. Automated reconfiguration is vital to rapidly responding to change, but some changes cannot be entirely achieved without human input in collaboration with automated methods. Conventional practices in PLC programming include manual, repetitive coding practices subject to errors. As a result, to ensure operational safety, the changes must be tested before being deployed to operations, ensuring it is error-free. This paper presents a methodology to automatically reconfigure the simulation environment and controller in response to a new product request. We automate the PLC code generation and testing practices to support and free up the operators when performing repetitive manufacturing reconfiguration tasks. The methodology is based on human learning, software automation, customised program development, knowledge graphs, and Graph Neural Networks (GNNs). The presented solution is a generic, vendor-agnostic, and interoperable solution that facilitates information exchange among multiple heterogeneous environments. Lastly, we have validated the methodology as a proof of concept at an adaptive assembly cell at the University of Nottingham in the United Kingdom.

Implementation of a Cascade Fault Tolerant Control and Fault Diagnosis Design for a Modular Power Supply

The main objective of this research work was to develop reliable and intelligent power sources for the future. To achieve this objective, a modular stand-alone solar energy-based direct current (DC) power supply was designed and implemented. The converter topology used is a two-stage interleaved boost converter, which is monitored in closed loop. The diagnosis method is based on analytic redundancy relations (ARRs) deduced from the bond graph (BG) model, which can be used to detect the failures of power switches, sensors, and discrete components such as the output capacitor. The proposed supervision scheme including a passive fault-tolerant cascade proportional integral sliding mode control (PI-SMC) for the two-stage boost converter connected to a solar panel is suitable for real applications. Most model-based diagnosis approaches for power converters typically deal with open circuit and short circuit faults, but the proposed method offers the advantage of detecting the failures of other vital components. Practical experiments on a newly designed and constructed prototype, along with simulations under PSIM software, confirm the efficiency of the control scheme and the successful recovery of a faulty stage by manual isolation. In future work, the automation of this reconfiguration task could be based on the successful simulation results of the diagnosis method.

ЭФФЕКТИВНОСТЬ ВЫПОЛНЕНИЯ КОЛЛЕКТИВНОЙ МИССИИ МНОГОАГЕНТНОЙ ГЕТЕРОГЕННОЙ ГРУППОЙ

The article deals with study оf the criteria, parameters and performance indicators for the implementationof the collective mission of extended space monitoring by a group that includes controlobjects with different functionality. The most urgent military-technical task of recent times – monitoringof extended spaces- is considered as an example of a mission. A feature of the article is the assumptionabout of hazardous zones in the monitoring area. Any counteraction from potentially criticalobjects may significantly limit the capabilities and even disable the monitoring tools. This provisoaffects for implementation of the mission by the group and leads to a revision of the group collectivestrategies and individual strategies for monitoring tools, their movement routes, and decision-makingalgorithms and etc. Monitoring tools are modeled as intelligent agents trained by the "collectivism"paradigm. It provides a common resource of situational awareness of the group, the organization of negotiations between agents and mutual assistance to each other in the event of problem situations.A general approach has been developed to evaluate the effectiveness of solving collective problemsby a heterogeneous group of agents, taking into account the objective function, resource intensity ofthe entire mission and organizing active interaction between agents. It is proposed that the missionefficiency characteristic expressed as a weighted sum of the normalized indicators of the parametersof the agents' functions with the weights "significance of the function for the mission" and "value ofthe object". It is shown that the loss of efficiency in the performance of a collective mission in theevent of problematic situations with an agent (active enemy action, breakdown, lack of the necessaryfunctionality, resources, etc.) can be compensated by the required functionality of other agents of thegroup with the appropriate reconfiguration of tasks.

Human Factors Assessment of a Novel Pediatric Lower-Limb Exoskeleton

While several lower-limb exoskeletons have been designed for adult patients, there remains a lack of pediatric-oriented devices. This paper presented a human factor assessment of an adjustable pediatric lower-limb exoskeleton for childhood gait assistance. The hip and knee exoskeleton uses an adjustable frame for compatibility with children 6–11 years old. This assessment evaluates the device’s comfort and ease of use through timed donning, doffing, and reconfiguration tasks. The able-bodied study participants donned the device in 6 min and 8 s, doffed it in 2 min and 29 s, and reconfigured it in 8 min and 23 s. The results of the timed trials suggest that the exoskeleton can be easily donned, doffed, and reconfigured to match the anthropometrics of pediatric users. A 6-min unpowered walking experiment was conducted while the child participant wore the exoskeletal device. Inspection of both the device and participant yielded no evidence of damage to either the device or wearer. Participant feedback on the device was positive with a system usability scale rating of 80/100. While minor improvements can be made to the adjustability indicators and padding placement, the results indicate the exoskeleton is suitable for further experimental evaluation through assistive control assessments.

A Distributed Task Rescheduling Method for UAV Swarms Using Local Task Reordering and Deadlock-Free Task Exchange

Distributed task scheduling is an ongoing concern in the field of multi-vehicles, especially in recent years; UAV swarm performing complex tasks endows it with new characteristics, such as self-organization, scalability, reconfigurability, etc. This requires the swarm to have distributed rescheduling capability to dynamically include as many unassigned tasks or new tasks as possible, while satisfying tight time constraints. As one of the most advanced rescheduling methods, the Performance Impact (PI)-MaxAss algorithm provides an important reference for this paper. However, its task exchange-based strategy faces the deadlock problem, and the task rescheduling method should not be limited to this. To this end, a new distributed rescheduling method is proposed for UAV swarms, which combines the local task reordering strategy and the improved task exchange strategy. On the one hand, based on the analysis of the fact that the scheduler is unreasonable for individuals, this paper proposes a local task reordering strategy denoted as PI-Reorder, which simply adds the reordering strategy to the recursive inclusion phase of the PI-MinAvg algorithm, so that unassigned tasks or new tasks can be included without relying on the task exchange. On the other hand, from the phenomenon that two or more vehicles occasionally get caught in an infinite cycle of exchanging the same tasks, the deadlock problem of PI-MaxAss is analyzed, which is then solved by introducing a deadlock-free task exchange strategy, where some defined counters are used to detect and isolate the deadlocks. Then, a rescue scenario is used to demonstrate the performance of the proposed methods, PI-Hybrid compared with PI-MaxAss. Monte Carlo simulation results show that, compared with PI-MaxAss, this method can not only increase the number of allocations to varying degrees, but also reduce the average waiting time, while ensuring deadlock avoidance. The methods can be used not only for the secondary optimization of the existing task exchange scheduling algorithms to escape local optima, but also for task reconfiguration of swarm tasks after adding or removing tasks.

E2DA: Energy Efficient Data Aggregation and End-to-End Security in 3D Reconfigurable WSN

This paper deals with energy consumption and security limitations in the reconfigurable WSN’s in order to improve the network lifetime with end-to-end data privacy. The network consists set of nodes placed in distributed environment such that each node performs a reconfiguration task to support users requirements. We proposed novel solutions that improved network lifetime through efficient reconfigurable routing and less network traffic. We proposed an in-network task to eliminate duplicate packets at the node level using hashing distance computation (HDC). Further, we introduced power-efficient reconfigurable cell-by-cell golden sector-based emperor penguin colony (CbC GSEPC) for trust-based routing. In terms of data confidentiality in the energy-constrained environment, a lightweight key expandable cryptography method was proposed for end-to-end confidentiality. Additionally, a reading-based dual validation (RbDV) audits the information at sink level for intrusion detection and isolates suspicion nodes. The proposed and existing works simulated with NS-3.26 and the results show that the proposed work’s average energy consumption is 5.82% lower than the existing 2D-WSN while offering end-to-end confidentiality and node reconfiguration opportunity.

Fast Search and Efficient Placement Algorithm for Reconfigurable Tasks on Modern Heterogeneous FPGAs

To date, only a tiny fraction of reconfigurable task placement algorithms is targeted at modern heterogeneous field-programmable gate array (FPGA) architecture, and they often focus on determining the final placement location and pursuing placement quality. Hence, their real-time performance is poor because feasible location searching and placement speed are rarely taken into consideration. In this article, we propose a fast search strategy based on characteristic target gene sequence (CTGS) and an efficient placement algorithm called prioritization-based minimum cost and marginal compact (P2MC). CTGS ascertains tasks’ feasible locations quickly by regarding the relatively few heterogeneous resources on FPGAs as search targets. P2MC first introduces prioritization heuristics based on task characteristics (PHTC) to presort tasks in order to improve the placement success rate and then select the final location according to the principle of minimum cost and marginal compact (2MC) so as to reduce the fragmentation of free space. The proposed algorithms are verified and evaluated on Xilinx’s mainstream FPGA families Virtex-5/6/7. Results show that CTGS can accelerate the search speed of tasks’ feasible locations by about four to five times, and P2MC can further balance placement speed and success rate. Compared with state-of-the-art heterogeneous task placement algorithms, P2MC can either increase both placement speed and success rate (by about 29% and 4.5%, respectively) or significantly increase the placement speed (by 20 times) at the expense of a bit of placement success rate (by only 5.8%).

Reconfiguration of response rule is more difficult than that of task goal: Behavior and electrophysiological evidence

Previous studies have investigated the neural mechanisms underlying cognitive control by using the task-switching paradigm, but differentiation from response rule switching (RR-switch) remains poorly explored. In this study, a partial voluntary task-switching (VTS) paradigm was used to explore the electrophysiological differences between task switching (T-switch) and RR-switching. Participants were sequentially presented with Arabic numerals colored red or green. If the color in the current trial was the same as that in the previous trial, the participants had to perform the same task following the same response rule. Otherwise, they had to voluntarily switch tasks (e.g., from parity task to magnitude task) or switch response rules (e.g., from “pressing F for odd and J for even number” to “pressing J for odd and F for even number”). The behavioral results indicated that RR-switch was infrequently selected, and the performance was less efficient than that of the T-switch. Event-related potential results showed that both T- and RR-switches elicited a larger switch-positivity in the P2 and P3 time windows than that in the repeat condition. Switch-positivity was larger for RR-switch than for T-switch over the frontal sites, suggesting that more attention and cognitive resources were required to update information for the RR-switch than for the T-switch. These findings suggest that in the VTS, the hierarchical relationship between task goals and response rules is relatively loose, resulting in the neural disassociation of task reconfiguration and response change.

Control of an Orbital Manipulator with Reaction Wheels for On-orbit Servicing

In on-orbit servicing missions, a spacecraft equipped with a manipulator arm grasps a client satellite. Afterwards, the momentum-dumping phase follows and the manipulator can be utilized for servicing tasks. Once the momentum has been dumped, a desired manipulator configuration and base attitude may be needed for initializing a servicing phase such as berthing. The proposed approach prioritizes the attitude maintenance of the servicer spacecraft, and reconfigures the manipulator arm with the grasped client satellite in the nullspace of the servicer's attitude. The approach utilizes the redundancy in rotational degrees of freedom, provided by reaction wheels, to decouple the manipulator reconfiguration task from the attitude of the servicer-base. The proposed controller shows convergence of the attitude and joint-level task, independent of kinematic singularities of the manipulator arm. Additionally, the problem of speed saturation of the reaction wheels is tackled by increasing the damping torques as a function of the wheel speed. Simulation results verify the effectiveness of the proposed control approach.

Enhancing learning capabilities of movement primitives under distributed probabilistic framework for flexible assembly tasks

This paper presents a novel probabilistic distributed framework based on movement primitives for flexible robot assembly. Since the modern advanced industrial cell usually deals with various scenarios that are not fixed via-point trajectories but highly reconfigurable tasks, the industrial robots used in these applications must be capable of adapting and learning new in-demand skills without programming experts. Therefore, we propose a probabilistic framework that could accommodate various learning abilities trained with different movement-primitive datasets, separately. Derived from the Bayesian Committee Machine, this framework could infer new adapting trajectories with weighted contributions of each training dataset. To verify the feasibility of our proposed imitation learning framework, the simulation comparison with the state-of-the-art movement learning framework task-parametrised GMM is conducted. Several key aspects, such as generalisation capability, learning accuracy and computation expense, are discussed and compared. Moreover, two real-world experiments, i.e. riveting picking and nutplate picking, are further tested with the YuMi collaborative robot to verify the application feasibility in industrial assembly manufacturing.

Exploring the Link between Novel Task Proceduralization and Motor Simulation.

Our ability to generate efficient behavior from novel instructions is critical for our adaptation to changing environments. Despite the absence of previous experience, novel instructed content is quickly encoded into an action-based or procedural format, facilitating automatic task processing. In the current work, we investigated the link between proceduralization and motor simulation, specifically, whether the covert activation of the task-relevant responses is used during the assembly of action-based instructions representations. Across three online experiments, we used a concurrent finger-tapping task to block motor simulation during the encoding of novel stimulus-response (S-R) associations. The overlap between the mappings and the motor task at the response level was manipulated. We predicted a greater impairment at mapping implementation in the overlapping condition, where the mappings’ relevant response representations were already loaded by the motor demands, and thus, could not be used in the upcoming task simulation. This hypothesis was robustly supported by the three datasets. Nonetheless, the overlapping effect was not modulated by further manipulations of proceduralization-related variables (preparation demands in Exp.2, mapping novelty in Exp.3). Importantly, a fourth control experiment ruled out that our results were driven by alternative accounts as fatigue or negative priming. Overall, we provided strong evidence towards the involvement of motor simulation during anticipatory task reconfiguration. However, this involvement was rather general, and not restricted to novelty scenarios. Finally, these findings can be also integrated into broader models of anticipatory task control, stressing the role of the motor system during preparation.

A formal analysis of performance-security tradeoffs under frequent task reconfigurations

Reconfiguration can be used to improve system performance, increase fault-tolerance, recover from failures, prevent and recover from cyberattacks. The process of using reconfiguration to increase a system’s resilience to cyberattacks is called Moving Target Defense (MTD). While system reconfiguration has many advantages, it brings potential disadvantages such as performance and availability degradation when a system is undergoing reconfiguration. This paper analyzes the use of MTD to reduce the frequency of cyberattacks and develops a closed-form analytic model that captures the tradeoffs between resilience to cyberattacks and system performance. The state-of-the art in MTD discusses how proposed methods operate and uses experimentation to demonstrate their use. This paper fills a research gap in terms of developing formal models that analyze tradeoffs between performance and resilience to cyberattacks. More specifically, the main contributions of this paper include: (1) A closed-form analytic model for computing the probability that an attacker is successful in completing the reconnaissance phase of the attack and the computation of the task’s execution time when regular reconfigurations are used. (2) An optimization model for determining the optimal task reconfiguration frequency in a way that takes into account given performance-security tradeoffs. (3) A closed-form analytic model for computing the probability that an attacker is successful in completing the reconnaissance phase of the attack and the computation of the task’s execution time when irregular reconfigurations are used. (4) Derivation of the maximum probability that an attacker fails in completing the reconnaissance phase under irregular reconfiguration intervals.

Formally-based Model-Driven Development of Collaborative Robotic Applications

The development of Human Robot Collaborative (HRC) systems faces many challenges. First, HRC systems should be adaptable and re-configurable to support fast production changes. However, in the development of HRC applications safety considerations are of paramount importance, as much as classical activities such as task programming and deployment. Hence, the reconfiguration and reprogramming of executing tasks might be necessary also to fulfill the desired safety requirements. Model-based software engineering is a suitable means for agile task programming and reconfiguration. We propose a model-based design-to-deployment toolchain that simplifies the routine of updating or modifying tasks. This toolchain relies on (i) UML profiles for quick model design, (ii) formal verification for exhaustive search for unsafe situations (caused by intended or unintended human behavior) within the model, and (iii) trans-coding tools for automating the development process. The toolchain has been evaluated on a few realistic case studies. In this paper, we show a couple of them to illustrate the applicability of the approach.

Efficient On-Chip Training of Optical Neural Networks Using Genetic Algorithm

Recent advances in silicon photonic chips have made huge progress in optical computing owing to their flexibility in the reconfiguration of various tasks. Its deployment of neural networks serves a...

On Mapping of Reconfigurable Hierarchical Tasks to MP-SoC-Oriented Architectures Under Real-Time and Energy Constraints

Mapping complex hierarchical reconfigurable tasks on distributed architectures are classically an NP-hard problem. The current paper introduces a new software specification of these tasks with two-dimensional hierarchy levels DAGs. Each DAG is composed of multi-hierarchical subDAGs to specify graphs of complex software task graphs. We also propose a heuristic algorithm for mapping the new reconfigurable hierarchical model on multi-core MP-SOC architectures. In the proposed software model, an application is seen as a DAG of tasks each of which is considered as a subDAG composed of elementary functions. The proposed algorithm acts in three steps: First, we map the subDAGs at the deepest level of hierarchy into processing units (cluster of processors). Then we map each task on a particular processor. Finally we map elementary functions on processor cores. Experimental results of the proposed algorithms show an increase of performance in terms of execution time and energy consumption. Our algorithm is also efficient in terms of communication cost approved by simulation results applied to large number of tasks.

Safety control system technologies for UAVs: review and prospect

Unmanned aerial vehicles (UAVs), considered as platforms for AI technology, play an important role in national security and economic development. With the growing degree of task complexity and the development of anti-UAV technologies, the UAV-incident rate is increasing significantly. Therefore, it is highly desirable to improve UAV safety through anti-disturbance and resilience control. This paper reviews existing studies upon disturbance estimation and fault diagnosis, anti-disturbance control, fault-tolerant control, and task reconfiguration. In addition, technical challenges and potential solutions are presented, including an overall architecture of a UAV safety-control system. In terms of research focus, particular attention must be paid to UAV-characteristics recognition, causality and trace-to-the-source analysis, quantitative analysis of ability, intelligent detection in a local loop, safety control in an overall loop, and task-control-optimization design. In the future, advanced control algorithms must be transferred to the software, chips, and systems of safety control, allowing the proposed control scheme to satisfy major practical needs and applications.

A Distributed Reconfiguration Planning Algorithm for Modular Robots

Self-reconfigurable modular robots are usually composed of multiple modules with uniform docking interfaces that can be transformed into different configurations by themselves. The reconfiguration planning problem is finding what sequence of reconfiguration actions are required for one arrangement of modules to transform into another. We present a novel reconfiguration planning algorithm for the SMORES form of modular robots. The algorithm compares the initial configuration with the goal configuration efficiently. The reconfiguration actions can be executed in a distributed manner so that each module can efficiently finish its reconfiguration task which results in a global reconfiguration for the system. In the end, the algorithm is demonstrated on the SMORES-EP self-reconfigurable modular robot hardware and some reconfiguration task examples are provided.

An Optimal Planning Framework to Deploy Self-Reconfigurable Modular Robots

Self-reconfiguration is a hard problem due to the high dimensionality of self-reconfigurable modular systems. Searchbased approaches offer complete and optimal solutions. However, naive search algorithms cannot directly solve self-reconfiguration tasks in reasonable time. In this letter, a transition model, a search heuristic, pruning strategies, and offline computations are proposed to advance performance of search-based self-reconfiguration methods. The targeted reconfiguration tasks include the reconfiguration of any distribution of modules, whether it is a complex structures or disconnected modules distributed in space. Hypothesized strategies are tested with conventional search methods to understand their contributions on complexity, optimality (minimum number of steps), completeness, and time efficiency. The whole framework is designed keeping hardware restrictions in mind and deployed on real Roombots hardware.