scholarly journals A Damage-Tolerant Task Assignment Algorithm for UAV Swarm in Confrontational Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Chao Chen ◽  
Weidong Bao ◽  
Tong Men ◽  
Wen Zhou ◽  
Daqian Liu ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Success Probability ◽  
Task Assignment ◽  
Human Beings ◽  
Assignment Algorithm ◽  
Uav Swarm ◽  
Task Success ◽  
Damage Tolerant ◽  
First Time ◽  
Reliability And Availability

As Unmanned Aerial Vehicles (UAVs) are widely used in many applications, a lot of military missions in confrontational environments are being undertaken by UAV swarm rather than human beings due to its advantages. In confrontational environments, the reliability and availability of UAV swarm would be the major concern because of UAVs’ vulnerability, so damage-tolerant task assigning algorithms are of great importance. In this paper, we come up with a novel damage-tolerant framework for assigning real-time tasks to UAVs with dynamical states in confrontational environments. Different from existing scheduling methods, we not only assign tasks but also back up copies of tasks to UAVs when needed, to promote reliability. Meanwhile, we adopt an overlapping mechanism, including Backup-Primary overlapping and Backup-Backup overlapping, in assignment to save the limited swarm resources. On the basis of the damage-tolerant and overlapping mechanism, for the first time, we propose a new damage-tolerant task assignment algorithm named DTTA, aiming at promoting the task success probability. Extensive experiments are conducted based on random synthetic workloads to compare DTTA with three baseline algorithms. The experimental results indicate that DTTA can efficiently promote the probability of tasks’ success without affecting the effectiveness of swarms in confrontational environments.

scholarly journals A Two-Layer Task Assignment Algorithm for UAV Swarm Based on Feature Weight Clustering

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaowei Fu ◽  
Peng Feng ◽  
Bin Li ◽  
Xiaoguang Gao
Keyword(s):  
Large Scale ◽  
Task Assignment ◽  
Small Scale ◽  
Resource Assignment ◽  
Assignment Algorithm ◽  
Uav Swarm ◽  
Feature Weight ◽  
Bundle Algorithm ◽  
Aerial Vehicle ◽  
Simulation Results

For the large-scale operations of unmanned aerial vehicle (UAV) swarm and the large number of UAVs, this paper proposes a two-layer task and resource assignment algorithm based on feature weight clustering. According to the numbers and types of task resources of each UAV and the distances between different UAVs, the UAV swarm is divided into multiple UAV clusters, and the large-scale allocation problem is transformed into several related small-scale problems. A two-layer task assignment algorithm based on the consensus-based bundle algorithm (CBBA) is proposed, and this algorithm uses different consensus rules between clusters and within clusters, which ensures that the UAV swarm gets a conflict-free task assignment solution in real time. The simulation results show that the algorithm can assign tasks effectively and efficiently when the number of UAVs and targets is large.

scholarly journals Toward delay-tolerant multiple-unmanned aerial vehicle scheduling system using Multi-strategy Coevolution algorithm

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881523 ◽  
Author(s):  
Yohanes Khosiawan ◽  
Sebastian Scherer ◽  
Izabela Nielsen
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Unmanned Aerial Vehicle ◽  
Task Assignment ◽  
Computation Time ◽  
Task Sequence ◽  
Swarm Optimization ◽  
Assignment Algorithm ◽  
Aerial Vehicle ◽  
Delay Tolerant

Autonomous bridge inspection operations using unmanned aerial vehicles take multiple task assignments and constraints into account. To efficiently execute the operations, a schedule is required. Generating a cost optimum schedule of multiple-unmanned aerial vehicle operations is known to be Non-deterministic Polynomial-time (NP)-hard. This study approaches such a problem with heuristic-based algorithms to get a high-quality feasible solution in a short computation time. A constructive heuristic called Retractable Chain Task Assignment algorithm is presented to build an evaluable schedule from a task sequence. The task sequence representation is used during the search to perform seamless operations. Retractable Chain Task Assignment algorithm calculates and incorporates slack time to the schedule according to the properties of the task. The slack time acts as a cushion which makes the schedule delay-tolerant. This algorithm is incorporated with a metaheuristic algorithm called Multi-strategy Coevolution to search the solution space. The proposed algorithm is verified through numerical simulations, which take inputs from real flight test data. The obtained solutions are evaluated based on the makespan, battery consumption, computation time, and the robustness level of the schedules. The performance of Multi-strategy Coevolution is compared to Differential Evolution, Particle Swarm Optimization, and Differential Evolution–Fused Particle Swarm Optimization. The simulation results show that Multi-strategy Coevolution gives better objective values than the other algorithms.

Education as Arikara Spiritual Renewal and Cultural Evolution

2014 ◽  
Vol 54 (3) ◽  
pp. 303-322 ◽  
Author(s):  
KuuNUx TeeRIt Kroupa
Keyword(s):  
Deep Understanding ◽  
Cultural Development ◽  
Human Beings ◽  
Old People ◽  
New Beginning ◽  
The People ◽  
History Of ◽  
The Creation ◽  
First Time ◽  
The Universe

In May 2009, the Arikara returned to the land of their ancestors along the Missouri River in South Dakota. For the first time in more than a half century, a Medicine Lodge was built for ceremony. The lodge has returned from its dormant state to regain its permanent place in Arikara culture. This event will be remembered as a significant moment in the history of the Arikara because it symbolizes a new beginning and hope for the people. Following this historic event, Arikara spiritual leader Jasper Young Bear offered to share his experience and deep insight into Arikara thought: You have to know that the universe is the Creator's dream, the Creator's mind, everything from the stars all the way to the deepest part of the ocean, to the most microscopic particle of the creation, to the creation itself, on a macro level, on a micro level. You have to understand all of those aspects to understand what the lodge represents. The lodge is a fractal, a symbolic representation of the universe itself. How do we as human beings try to make sense of that? That understanding, of how the power in the universe flows, was gifted to us through millennia of prayer and cultural development… It is important for us to internalize our stories, internalize the star knowledge, internalize those things and make that your way, make that your belief, because we're going to play it out inside the lodge. It only lives by us guys interacting with it and praying with it and bringing it to life… We're going to play out the wise sayings of the old people… So you see that it's an Arikara worldview. A learning process of how the universe functions is what you're actually experiencing [inside the Medicine Lodge]. What the old people were describing was the functioning of how we believed the universe behaves. And we had a deep, deep understanding of what that meant and how it was for us. So that's what you're actually seeing in the Medicine Lodge.

scholarly journals Cognitive Dissonance Theory

Psychology ◽  
2014 ◽  
Author(s):  
David C. Vaidis ◽  
Alexandre Bran
Keyword(s):  
Cognitive Dissonance ◽  
Human Beings ◽  
General Psychology ◽  
Social Situations ◽  
Cognitive Dissonance Theory ◽  
Historical Sources ◽  
Scientific Publications ◽  
Very Large Array ◽  
Dissonance Theory ◽  
First Time

Appearing for the first time in the mid-20th century, the term “cognitive dissonance” appears nowadays about eight hundred times in PsycINFO and the original book has been cited more than forty-five thousand times in scientific publications: that is more than twice a day for about sixty years. The theory of cognitive dissonance was molded by Leon Festinger at the beginning of the 1950s. It suggests that inconsistencies among cognitions (i.e., knowledge, opinion, or belief about the environment, oneself, or one’s behavior) generate an uncomfortable motivating feeling (i.e., the cognitive dissonance state). According to the theory, people feel uncomfortable when they experience cognitive dissonance and thus are motivated to retrieve an acceptable state. The magnitude of existing dissonance depends on the importance of the involved cognitions. Experiencing a higher level of dissonance causes pressure and motivation to reduce the dissonance. Findings from several studies show that dissonance occurs when people do not act in accordance with their attitude (e.g., writing supportive arguments in favor of a topic that they do not agree upon; performing a task they disapprove). Festinger 1957 (cited under Core Historical Sources) considers three ways to cope with cognitive dissonance: (a) changing one or several involved elements in the dissonance relationship (e.g., moving an opinion to fit a behavior), (b) adding new elements to reduce the inconsistency (e.g., adopting opinions that fit a behavior), and (c) reducing the importance of the involved elements. Early theorists in this field suggested improvement to the cognitive dissonance theory by adding restrictions for the emergence of the phenomena. Three major developments have to be considered: the commitment purpose and freedom, the consequence of the act purpose, and the self-involvement. Since the 2010s, the theory has been refined with new integrative models and methodological breakthrough. Mostly studied in human beings, several studies shift paradigms to other animals such as nonhuman primates, rats, and birds. The cognitive dissonance theory has been applied to a very large array of social situations and leads to original experimental designs. It is arguably one of the most influential theories in social psychology, general psychology, and cross-discipline sciences more generally.

scholarly journals Report on first certificate course on applied physiology of pain, analgesia, anesthesia, and euthanasia for laboratory animals held in Sri Lanka

2018 ◽  
Vol 42 (2) ◽  
pp. 263-266
Author(s):  
Mangala Gunatilake
Keyword(s):  
Sri Lanka ◽  
Laboratory Animal ◽  
Pain Perception ◽  
Anesthesia Machine ◽  
Laboratory Animals ◽  
Comparative Biology ◽  
Human Beings ◽  
Unpleasant Sensation ◽  
Applied Physiology ◽  
First Time

Similar to human beings, pain is an unpleasant sensation experienced by animals as well. There is no exception when the animals are subjected to experimental procedures. Our duty as researchers/scientists is to prevent or minimize the pain in animals so as to lessen their suffering and distress during experimental procedures. The basics of the physiology of pain and pain perception, analgesia, anesthesia, and euthanasia of laboratory animals were included to complete the program, before the practical part was attempted and before advanced topics, such as comparison of anesthetic combinations, were discussed. Therefore, this course was organized in Sri Lanka for the first time in collaboration with the Comparative Biology Centre of Newcastle University, UK. During this course, we were able to demonstrate how an anesthesia machine could be used in laboratory animal anesthesia for the first time in the country. None of the animal houses in the country were equipped with an anesthesia machine at the time of conducting the course.

A Dynamic-Programming-Styled Algorithm for a Class of Multi-Agent Optimal Task Assignment

2001 ◽  
Author(s):  
Guang Yang ◽  
Vikram Kapila ◽  
Ravi Vaidyanathan
Keyword(s):  
Dynamic Programming ◽  
Task Assignment ◽  
Research Activity ◽  
Spacecraft Formation ◽  
Assignment Algorithm ◽  
Decentralized Decision Making ◽  
Current Research Activity ◽  
Multi Agent ◽  
Global Optimal ◽  
Multiple Spacecraft

Abstract In this paper, we use a dynamic programming formulation to address a class of multi-agent task assignment problems that arise in the study of fuel optimal control of multiple agents. The fuel optimal multi-agent control is highly relevant to multiple spacecraft formation reconfiguration, an area of intense current research activity. Based on the recurrence relation derived from the celebrated principle of optimality, we develop an algorithm with a distributed computational architecture for the global optimal task assignment. In addition, we propose a communication protocol to facilitate decentralized decision making among agents. Illustrative studies are included to demonstrate the efficacy of the proposed multi-agent optimal task assignment algorithm.

Modelling a Deep Learning-Based Wireless Sensor Network Task Assignment Algorithm

2020 ◽  
pp. 84-109
Author(s):  
Titus Issac ◽  
Salaja Silas ◽  
Elijah Blessing Rajsingh
Keyword(s):  
Deep Learning ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Task Assignment ◽  
Human Interaction ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Feed Forward Neural Network ◽  
Functional Capabilities ◽  
Assignment Algorithm

The 21st century is witnessing the emergence of a wide variety of wireless sensor network (WSN) applications ranging from simple environmental monitoring to complex satellite monitoring applications. The advent of complex WSN applications has led to a massive transition in the development, functioning, and capabilities of wireless sensor nodes. The contemporary nodes have multi-functional capabilities enabling the heterogeneous WSN applications. The future of WSN task assignment envisions WSN to be heterogeneous network with minimal human interaction. This led to the investigative model of a deep learning-based task assignment algorithm. The algorithm employs a multilayer feed forward neural network (MLFFNN) trained by particle swarm optimization (PSO) for solving task assignment problem in a dynamic centralized heterogeneous WSN. The analyses include the study of hidden layers and effectiveness of the task assignment algorithms. The chapter would be highly beneficial to a wide range of audiences employing the machine and deep learning in WSN.

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