Multi-UAV Cooperative Task Assignment Based on Half Random Q-Learning

Unmanned aerial vehicle (UAV) clusters usually face problems such as complex environments, heterogeneous combat subjects, and realistic interference factors in the course of mission assignment. In order to reduce resource consumption and improve the task execution rate, it is very important to develop a reasonable allocation plan for the tasks. Therefore, this paper constructs a heterogeneous UAV multitask assignment model based on several realistic constraints and proposes an improved half-random Q-learning (HR Q-learning) algorithm. The algorithm is based on the Q-learning algorithm under reinforcement learning, and by changing the way the Q-learning algorithm selects the next action in the process of random exploration, the probability of obtaining an invalid action in the random case is reduced, and the exploration efficiency is improved, thus increasing the possibility of obtaining a better assignment scheme, this also ensures symmetry and synergy in the distribution process of the drones. Simulation experiments show that compared with Q-learning algorithm and other heuristic algorithms, HR Q-learning algorithm can improve the performance of task execution, including the ability to improve the rationality of task assignment, increasing the value of gains by 12.12%, this is equivalent to an average of one drone per mission saved, and higher success rate of task execution. This improvement provides a meaningful attempt for UAV task assignment.

Effects of Continuous vs Discrete Frequency Scaling and Core Allocation on Energy Efficiency of Static Schedules for Moldable Tasks

Models for energy-efficient static scheduling of parallelizable tasks with deadlines on frequency-scalable parallel machines comprise moldable vs. malleable tasks and continuous vs. discrete frequency levels, plus preemptive vs. non-preemptive task execution with or without task migration. We investigate the tradeoff between scheduling time and energy efficiency when going from continuous to discrete core allocation and frequency levels on a multicore processor, and from preemptive to non-preemptive task execution. To this end, we present a tool to convert a schedule computed for malleable tasks on machines with continuous frequency scaling [Sanders and Speck, Euro-Par (2012)] into one for moldable tasks on a machine with discrete frequency levels. We compare the energy efficiency of the converted schedule to the energy consumed by a schedule produced by the integrated crown scheduler [Melot et al., ACM TACO (2015)] for moldable tasks and a machine with discrete frequency levels. Our experiments with synthetic and application-based task sets indicate that the converted Sanders Speck schedules, while computed faster, consume more energy on average than crown schedules. Surprisingly, it is not the step from malleable to moldable tasks that is responsible but the step from continuous to discrete frequency levels. One-time frequency scaling during a task’s execution can compensate for most of the energy overhead caused by frequency discretization.

Learning induces coordinated neuronal plasticity of metabolic demands and functional brain networks

The neurobiological basis of learning is reflected in adaptations of brain structure, network organization and energy metabolism. However, it is still unknown how different neuroplastic mechanisms act together and if cognitive advancements relate to general or task-specific changes. To address these questions, we tested how hierarchical network interactions contribute to improvements in the performance of a visuo-spatial processing task by employing simultaneous PET/MR neuroimaging before and after a 4-week learning period. We combined functional PET with metabolic connectivity mapping (MCM) to infer directional interactions across brain regions and subsequently performed simulations to disentangle the role of functional network dynamics and glucose metabolism. As a result, learning altered the top-down regulation of the salience network onto the occipital cortex, with increases in MCM at resting-state and decreases during task execution. Accordingly, a higher divergence between resting-state and task-specific effects was associated with better cognitive performance, indicating that these adaptations are complementary and both required for successful skill learning. Simulations further showed that changes at resting-state were dependent on glucose metabolism, whereas those during task performance were driven by functional connectivity between salience and visual networks. Referring to previous work, we suggest that learning establishes a metabolically expensive skill engram at rest, whose retrieval serves for efficient task execution by minimizing prediction errors between neuronal representations of brain regions on different hierarchical levels.

Development of an Artificial Intelligence System for the Instruction and Control of Cooperating Mobile Robots

<p>This thesis focuses on the development of an artificial intelligence system for a heterogeneous ensemble of mobile robots. Many robots in the ensemble may have limited processing, communication, sensing, and/or actuation capabilities. This means that each robot may not be able to execute all tasks that are input to the system. A hierarchical system is proposed to permit robots with superior processing and communication abilities to assign tasks and coordinate the less computationally able robots. The limited processing robots may also utilise the resources of superior robots during task execution. Effective task allocation and coordination should result in efficient execution of a global task. Many existing approaches to robot task allocation assume expert knowledge for task specification. This is not ideal if a non-expert human user wants to modify the task requirements. A novel reduced human user input task allocation and feedback coordination technique for limited capability mobile robots is developed and implemented. Unlike existing approaches, the presented method focuses on expressing tasks and robots in terms of processing, communication, sensing, and actuation physical resources. This has the potential to allow non-expert human users to specify tasks to the team of robots. Fuzzy inference systems are utilised to simplify detailed robot information for comparison with simple human user inputs that represent task resource requirements. Like many existing task allocation methods, a greedy algorithm is employed to select robots. This can result in suboptimal task allocation. In addition to this, the non-expert user’s task specifications might be erroneous in some instances. Hence, a feedback coordination component monitors robot performance during task execution. In this thesis, a customised multi-robot mapping and exploration task is utilised as a model task to test the effectiveness of the developed task allocation and feedback coordination strategy. Extensive simulation experiments with various robot team configurations are executed in environments of varying sizes and obstacle densities to assess the performance of the technique. Task allocation is able to identify suitable robots and is robust to selection weight variation. The task allocation process is subjective to fuzzy membership function parameters which may vary for different This thesis focuses on the development of an artificial intelligence system for a heterogeneous ensemble of mobile robots. Many robots in the ensemble may have limited processing, communication, sensing, and/or actuation capabilities. This means that each robot may not be able to execute all tasks that are input to the system. A hierarchical system is proposed to permit robots with superior processing and communication abilities to assign tasks and coordinate the less computationally able robots. The limited processing robots may also utilise the resources of superior robots during task execution. Effective task allocation and coordination should result in efficient execution of a global task. Many existing approaches to robot task allocation assume expert knowledge for task specification. This is not ideal if a non-expert human user wants to modify the task requirements. A novel reduced human user input task allocation and feedback coordination technique for limited capability mobile robots is developed and implemented. Unlike existing approaches, the presented method focuses on expressing tasks and robots in terms of processing, communication, sensing, and actuation physical resources. This has the potential to allow non-expert human users to specify tasks to the team of robots. Fuzzy inference systems are utilised to simplify detailed robot information for comparison with simple human user inputs that represent task resource requirements. Like many existing task allocation methods, a greedy algorithm is employed to select robots. This can result in suboptimal task allocation. In addition to this, the non-expert user’s task specifications might be erroneous in some instances. Hence, a feedback coordination component monitors robot performance during task execution. In this thesis, a customised multi-robot mapping and exploration task is utilised as a model task to test the effectiveness of the developed task allocation and feedback coordination strategy. Extensive simulation experiments with various robot team configurations are executed in environments of varying sizes and obstacle densities to assess the performance of the technique. Task allocation is able to identify suitable robots and is robust to selection weight variation. The task allocation process is subjective to fuzzy membership function parameters which may vary for different users. Feedback coordination is robust to variation in weights and thresholds for failure detection. This permits the correction of suboptimal allocations arising from greedy task allocation, incorrect initial task specifications or unexpected failures. By being robust within the tested limits, weights and thresholds can be intuitively selected. However, other parameters such as ideal achievement data can be difficult to accurately characterise in some instances. A hierarchical hybrid deliberative-reactive navigation system for memory constrained heterogeneous robots to navigate obstructed environments is developed. Deliberative control is developed using a modified version of the A* algorithm and a rectangular occupancy grid map. A novel two-tiered path planner executes on limited memory mobile robots utilising the memory of a computationally powerful robot to enable navigation beyond localised regions of a large environment. Reactive control is developed using a modified dynamic window approach and a polar histogram technique to remove the need for periodic path planning. A range of simulation experiments in different sized environments is conducted to assess the performance of the two-tiered path planning strategy. The path planner is able to achieve superior or comparable execution times to non-memory constrained path planning when small sized local maps are employed in large global environments. Performance of hybrid deliberative-reactive navigation is assessed in a range of simulated environments and is also validated on a real robot. The developed reactive control system outperforms the dynamic window method.</p>

Development of an Artificial Intelligence System for the Instruction and Control of Cooperating Mobile Robots

<p>This thesis focuses on the development of an artificial intelligence system for a heterogeneous ensemble of mobile robots. Many robots in the ensemble may have limited processing, communication, sensing, and/or actuation capabilities. This means that each robot may not be able to execute all tasks that are input to the system. A hierarchical system is proposed to permit robots with superior processing and communication abilities to assign tasks and coordinate the less computationally able robots. The limited processing robots may also utilise the resources of superior robots during task execution. Effective task allocation and coordination should result in efficient execution of a global task. Many existing approaches to robot task allocation assume expert knowledge for task specification. This is not ideal if a non-expert human user wants to modify the task requirements. A novel reduced human user input task allocation and feedback coordination technique for limited capability mobile robots is developed and implemented. Unlike existing approaches, the presented method focuses on expressing tasks and robots in terms of processing, communication, sensing, and actuation physical resources. This has the potential to allow non-expert human users to specify tasks to the team of robots. Fuzzy inference systems are utilised to simplify detailed robot information for comparison with simple human user inputs that represent task resource requirements. Like many existing task allocation methods, a greedy algorithm is employed to select robots. This can result in suboptimal task allocation. In addition to this, the non-expert user’s task specifications might be erroneous in some instances. Hence, a feedback coordination component monitors robot performance during task execution. In this thesis, a customised multi-robot mapping and exploration task is utilised as a model task to test the effectiveness of the developed task allocation and feedback coordination strategy. Extensive simulation experiments with various robot team configurations are executed in environments of varying sizes and obstacle densities to assess the performance of the technique. Task allocation is able to identify suitable robots and is robust to selection weight variation. The task allocation process is subjective to fuzzy membership function parameters which may vary for different This thesis focuses on the development of an artificial intelligence system for a heterogeneous ensemble of mobile robots. Many robots in the ensemble may have limited processing, communication, sensing, and/or actuation capabilities. This means that each robot may not be able to execute all tasks that are input to the system. A hierarchical system is proposed to permit robots with superior processing and communication abilities to assign tasks and coordinate the less computationally able robots. The limited processing robots may also utilise the resources of superior robots during task execution. Effective task allocation and coordination should result in efficient execution of a global task. Many existing approaches to robot task allocation assume expert knowledge for task specification. This is not ideal if a non-expert human user wants to modify the task requirements. A novel reduced human user input task allocation and feedback coordination technique for limited capability mobile robots is developed and implemented. Unlike existing approaches, the presented method focuses on expressing tasks and robots in terms of processing, communication, sensing, and actuation physical resources. This has the potential to allow non-expert human users to specify tasks to the team of robots. Fuzzy inference systems are utilised to simplify detailed robot information for comparison with simple human user inputs that represent task resource requirements. Like many existing task allocation methods, a greedy algorithm is employed to select robots. This can result in suboptimal task allocation. In addition to this, the non-expert user’s task specifications might be erroneous in some instances. Hence, a feedback coordination component monitors robot performance during task execution. In this thesis, a customised multi-robot mapping and exploration task is utilised as a model task to test the effectiveness of the developed task allocation and feedback coordination strategy. Extensive simulation experiments with various robot team configurations are executed in environments of varying sizes and obstacle densities to assess the performance of the technique. Task allocation is able to identify suitable robots and is robust to selection weight variation. The task allocation process is subjective to fuzzy membership function parameters which may vary for different users. Feedback coordination is robust to variation in weights and thresholds for failure detection. This permits the correction of suboptimal allocations arising from greedy task allocation, incorrect initial task specifications or unexpected failures. By being robust within the tested limits, weights and thresholds can be intuitively selected. However, other parameters such as ideal achievement data can be difficult to accurately characterise in some instances. A hierarchical hybrid deliberative-reactive navigation system for memory constrained heterogeneous robots to navigate obstructed environments is developed. Deliberative control is developed using a modified version of the A* algorithm and a rectangular occupancy grid map. A novel two-tiered path planner executes on limited memory mobile robots utilising the memory of a computationally powerful robot to enable navigation beyond localised regions of a large environment. Reactive control is developed using a modified dynamic window approach and a polar histogram technique to remove the need for periodic path planning. A range of simulation experiments in different sized environments is conducted to assess the performance of the two-tiered path planning strategy. The path planner is able to achieve superior or comparable execution times to non-memory constrained path planning when small sized local maps are employed in large global environments. Performance of hybrid deliberative-reactive navigation is assessed in a range of simulated environments and is also validated on a real robot. The developed reactive control system outperforms the dynamic window method.</p>

Hybrid Multiagent Collaboration for Time-Critical Tasks: A Mathematical Model and Heuristic Approach

Principal–assistant agent teams are often employed to solve tasks in multiagent collaboration systems. Assistant agents attached to the principal agents are more flexible for task execution and can assist them to complete tasks with complex constraints. However, how to employ principal–assistant agent teams to execute time-critical tasks considering the dependency between agents and the constraints among tasks is still a challenge so far. In this paper, we investigate the principal–assistant collaboration problem with deadlines, which is to allocate tasks to suitable principal–assistant teams and construct routes satisfying the temporal constraints. Two cases are considered in this paper, including single principal–assistant teams and multiple principal–assistant teams. The former is formally formulated in an arc-based integer linear programming model. We develop a hybrid combination algorithm for adapting larger scales, the idea of which is to find an optimal combination of partial routes generated by heuristic methods. The latter is defined in a path-based integer linear programming model, and a branch-and-price-based (BP-based) algorithm is proposed that introduces the number of assistant-accessible tasks surrounding a task to guide the route construction. Experimental results validate that the hybrid combination algorithm and the BP-based algorithm are superior to the benchmarks in terms of the number of served tasks and the running time.

Computation Offloading Optimization in Mobile Edge Computing Based on HIBSA

Multiaccess edge computation (MEC) is a hotspot in 5G network. The problem of task offloading is one of the core problems in MEC. In this paper, a novel computation offloading model which partitions tasks into subtasksis proposed. This model takes communication and computing resources, energy consumption of intelligent mobile devices, and weight of tasks into account. We then transform the model into a multiobjective optimization problem based on Pareto that balances the task weight and time efficiency of the offloaded tasks. In addition, an algorithm based on hybrid immune and bat scheduling algorithm (HIBSA) is further designed to tackle the proposed multiobjective optimization problem. The experimental results show that HIBSA can meet the requirements of both the task execution deadline and the weight of the offloaded tasks.