Distributed Task Allocation Research Articles

Distributed Task Allocation to Enable Collaborative Autonomous Driving With Network Softwarization

The autonomous vehicles (AVs), like that in knight rider, were completely a scientific fiction just a few years ago, but are now already practical with real-world commercial deployments. A salient challenge of AVs, however, is the intensive computing tasks to carry out on board for the real-time traffic detection and driving decision making; this imposes heavy load to AVs due to the limited computing power. To explore more computing power and enable scalable autonomous driving, in this paper, we propose a collaborative task computing scheme for AVs, in which the AVs in proximity dynamically share idle computing power among each other. This, however, raises another fundamental problem on how to incentivize AVs to contribute their computing power and how to fully utilize the pool of group computing power in an optimal way. This paper studies the problem by modeling the issue as a market-based optimal computing resource allocation problem. In specific, we develop a software-defined network (SDN) architecture and consider a star topology where a centered AV outsources its computing tasks to the surrounding AVs for its autonomous driving. A market mechanism is developed in which the surrounding AVs sell their computing power at a cost based on their local idle computing resources. Then, we classify the tasks requested by the centered AV into two types which are task with time to live (TTL) and task without TTL, respectively. With different task types, we define corresponding cost models of the centered AV and formulate them as two minimization problems. The optimal solutions of the problems are achieved to guide the centered AV to wisely allocate computing tasks to surrounding AVs towards minimal cost. Finally, the performance of the proposed scheme is evaluated using simulations, which show that the proposed scheme can result in the guaranteed computing performance yet the lowest costs compared with other conventional schemes.

Distributed Task Allocation of Multiple Robots: A Control Perspective

The problem of dynamic task allocation in a distributed network of redundant robot manipulators for path-tracking with limited communications is investigated in this paper, where ${k}$ fittest ones in a group of ${n}$ redundant robot manipulators with ${n>k}$ are allocated to execute an object tracking task. The problem is essentially challenging in view of the interplay of manipulator kinematics and the dynamic competition for activation among manipulators. To handle such an intricate problem, a distributed coordination control law is developed for the dynamic task allocation among multiple redundant robot manipulators with limited communications and with the aid of a consensus filter. In addition, a theorem and its proof are presented for guaranteeing the convergence and stability of the proposed distributed control law. Finally, an illustrative example is provided and analyzed to substantiate the efficacy of the proposed control law.

Reliable, Distributed Scheduling and Rescheduling for Time-Critical, Multiagent Systems

This paper addresses two main problems with many heuristic task allocation approaches—solution trapping in local minima and static structure. The existing distributed task allocation algorithm known as performance impact (PI) is used as the vehicle for developing solutions to these problems as it has been shown to outperform the state-of-the-art consensus-based bundle algorithm for time-critical problems with tight deadlines, but is both static and suboptimal with a tendency toward trapping in local minima. This paper describes two additional modules that are easily integrated with PI. The first extends the algorithm to permit dynamic online rescheduling in real time, and the second boosts performance by introducing an additional soft-max action-selection procedure that increases the algorithm’s exploratory properties. This paper demonstrates the effectiveness of the dynamic rescheduling module and shows that the average time taken to perform tasks can be reduced by up to 9% when the soft-max module is used. In addition, the solution of some problems that baseline PI cannot handle is enabled by the second module. These developments represent a significant advance in the state of the art for multiagent, time-critical task assignment. Note to Practitioners —This work was motivated by the limitations of current agent-to-task allocation algorithms that do not use a central server for communication. In previously published work, the current state-of-the-art consensus-based bundle algorithm has demonstrated poor performance when applied to model task allocation problems with critical time limits, often failing to assign all of the tasks, especially when the deadlines are tight. The performance impact (PI) algorithm has a much better success rate with these model problems but would be flawed when applied to real missions because it has no mechanism for online replanning when new information becomes available. In addition, it is somewhat restricted in the way it searches for a problem solution, meaning that more efficient plans are often available but are not discovered. This paper tackles both of these shortcomings. The PI algorithm is extended to include a module that permits rescheduling when necessary, and a further module is introduced that widens the scope of the solution search. A third module that is able to offer robust plans, even for large-scaled missions involving many agents and tasks, has also been developed, although it is not discussed here. Implementation and testing of a version of PI that incorporates all three of these modules are the final goal of this research.

Multi-Auv Distributed Task Allocation Based on the Differential Evolution Quantum Bee Colony Optimization Algorithm

Abstract The multi-autonomous underwater vehicle (AUV) distributed task allocation model of a contract net, which introduces an equilibrium coefficient, has been established to solve the multi-AUV distributed task allocation problem. A differential evolution quantum artificial bee colony (DEQABC) optimization algorithm is proposed to solve the multi-AUV optimal task allocation scheme. The algorithm is based on the quantum artificial bee colony algorithm, and it takes advantage of the characteristics of the differential evolution algorithm. This algorithm can remember the individual optimal solution in the population evolution and internal information sharing in groups and obtain the optimal solution through competition and cooperation among individuals in a population. Finally, a simulation experiment was performed to evaluate the distributed task allocation performance of the differential evolution quantum bee colony optimization algorithm. The simulation results demonstrate that the DEQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The DEQABC algorithm can effectively improve AUV distributed multi-tasking performance.

Distributed task allocation in multi-agent environments using cellular learning automata

People’s safety is threatened by the repetition of critical events. Many people lose their lives due to unprofessional rescue operation as well as time pressure of the rescue operation. A key problem in urban search and rescue teams, considering the severe turbulence and complexity of the environments which are hit by a crisis, is the coordination between the team members. In order to solve this problem, an effective plan would be the provision of measures where human works with intelligent assistant agents to assign the tasks in any way. Dynamic tasks are identified by the human agent of the rescue team in the crisis environment and are characterized by spatial–temporal characteristics assigned to the appropriate rescue team by the intelligent assistant agents who apply intelligent decision-making techniques. The objective of this study is to propose a new approach for allocating spatial–temporal tasks in multi-agent systems through cellular learning automata as the decision-making technique. Results obtained here indicate that this proposed model can significantly improve the rescue time and space. Rescue teams could cover all critical areas by going through the minimum distance to make maximum use of time.

Cooperative task allocation in heterogeneous wireless sensor networks

In a wireless sensor network, sensor nodes are strictly energy and capacity constrained, which makes it necessary for them to collaboratively execute a complex task. Thus, task allocation becomes a fundamental and crucial issue in wireless sensor networks. Most previous studies developed centralized methods to solve this problem. In addition, a common assumption is that all the sensor nodes are homogeneous, which is unfavorable in many real applications. In this article, a distributed task allocation strategy which can handle the problem in a heterogeneous wireless sensor network is proposed. The task is propagated from nodes to nodes and each node matches its own capacity with the required capacities until all the demanded capacities of the task are obtained. Building on this, an enhanced task allocation strategy based on self-organization is developed. By utilizing previous assigning information, the nodes with proper capacities will be selected as candidate nodes, then the paths to these nodes will be ...

Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System

This paper considers the problem of maximizing the number of task allocations in a distributed multirobot system under strict time constraints, where other optimization objectives need also be considered. It builds upon existing distributed task allocation algorithms, extending them with a novel method for maximizing the number of task assignments. The fundamental idea is that a task assignment to a robot has a high cost if its reassignment to another robot creates a feasible time slot for unallocated tasks. Multiple reassignments among networked robots may be required to create a feasible time slot and an upper limit to this number of reassignments can be adjusted according to performance requirements. A simulated rescue scenario with task deadlines and fuel limits is used to demonstrate the performance of the proposed method compared with existing methods, the consensus-based bundle algorithm and the performance impact (PI) algorithm. Starting from existing (PI-generated) solutions, results show up to a 20% increase in task allocations using the proposed method.

Modeling and simulation of dynamic ant colony’s labor division for task allocation of UAV swarm

The problem of unmanned aerial vehicle (UAV) task allocation not only has the intrinsic attribute of complexity, such as highly nonlinear, dynamic, highly adversarial and multi-modal, but also has a better practicability in various multi-agent systems, which makes it more and more attractive recently. In this paper, based on the classic fixed response threshold model (FRTM), under the idea of “problem centered + evolutionary solution” and by a bottom-up way, the new dynamic environmental stimulus, response threshold and transition probability are designed, and a dynamic ant colony’s labor division (DACLD) model is proposed. DACLD allows a swarm of agents with a relatively low-level of intelligence to perform complex tasks, and has the characteristic of distributed framework, multi-tasks with execution order, multi-state, adaptive response threshold and multi-individual response. With the proposed model, numerical simulations are performed to illustrate the effectiveness of the distributed task allocation scheme in two situations of UAV swarm combat (dynamic task allocation with a certain number of enemy targets and task re-allocation due to unexpected threats). Results show that our model can get both the heterogeneous UAVs’ real-time positions and states at the same time, and has high degree of self-organization, flexibility and real-time response to dynamic environments.

Research on dynamic task allocation for multiple unmanned aerial vehicles

To solve the distributed task allocation problems of search and rescue missions for multiple unmanned aerial vehicles (UAVs), this paper establishes a dynamic task allocation model under three conditions: 1) when new targets are detected, 2) when UAVs break down and 3) when unexpected threats suddenly occur. A distributed immune multi-agent algorithm (DIMAA) based on an immune multi-agent network framework is then proposed. The technologies employed by the proposed algorithm include a multi-agent system (MAS) with immune memory, neighbourhood clonal selection, neighbourhood suppression, neighbourhood crossover and self-learning operators. The DIMAA algorithm simplifies the decision-making process among agents. The simulation results show that this algorithm not only obtains the global optimum solution, but also reduces the communication load between agents.

Distributed task allocation for multi-robot systems in search and rescue scenario

Distributed task allocation for multi-robot systems in search and rescue scenario

Distributed task allocation modeling based on agent topology and protocol for collaborative system

Task allocation plays an important role in collaborative system. This paper focuses on interaction topology and protocol of Multi-agent system (MAS) for task allocation, particularly for UUV collaborative system. The corresponding relationship between UUV formation system and MAS was systematically analyzed and developed from the viewpoint of structural similarity, in which UUV is regarded as the agent member of MAS network to realize the control for task allocation, and then Extended Contract Net Protocol is present based on peer-to-peer topology to complete interactive communication, which mainly focuses on network communication to improve efficiency and quality of task completed. The experiment results demonstrate the proposed distributed task allocation model has better performance on the performance indicators, which can be a good tool to solve the task allocation in the UUV formation system.

A Heuristic Distributed Task Allocation Method for Multivehicle Multitask Problems and Its Application to Search and Rescue Scenario

Using distributed task allocation methods for cooperating multivehicle systems is becoming increasingly attractive. However, most effort is placed on various specific experimental work and little has been done to systematically analyze the problem of interest and the existing methods. In this paper, a general scenario description and a system configuration are first presented according to search and rescue scenario. The objective of the problem is then analyzed together with its mathematical formulation extracted from the scenario. Considering the requirement of distributed computing, this paper then proposes a novel heuristic distributed task allocation method for multivehicle multitask assignment problems. The proposed method is simple and effective. It directly aims at optimizing the mathematical objective defined for the problem. A new concept of significance is defined for every task and is measured by the contribution to the local cost generated by a vehicle, which underlies the key idea of the algorithm. The whole algorithm iterates between a task inclusion phase, and a consensus and task removal phase, running concurrently on all the vehicles where local communication exists between them. The former phase is used to include tasks into a vehicle's task list for optimizing the overall objective, while the latter is to reach consensus on the significance value of tasks for each vehicle and to remove the tasks that have been assigned to other vehicles. Numerical simulations demonstrate that the proposed method is able to provide a conflict-free solution and can achieve outstanding performance in comparison with the consensus-based bundle algorithm.

Distributed Task Allocation of Mobile Robotic Sensor Networks with Guaranteed Connectivity

Robotic sensor network (RSN) contains mobile sensors and robots providing feasible solution for many multi-agent applications. One of the most critical issues in RSN and its application is how to effectively assign tasks. This paper presents a novel connectivity preserving hybrid task allocation strategy to answer the question particularly for RSN. Firstly, we model the task allocation in RSN to distinguish the discovering and allocating processes. Secondly, a fully distributed simple Task-oriented Unoccupied Neighbor Algorithm, named TUNA, is developed to allocate tasks with only partial view of the network topology. A connectivity controller is finally developed and integrated into the strategy to guarantee the global connectivity of entire RSN, which is critical to most RSN applications. The correctness, efficiency and scalability of TUNA are proved with both theoretical analysis and experimental simulations. The evaluation results show that TUNA can effectively assign tasks to mobile robots with the requirements of only a few messages and small movements of mobile agents.

A Distributed Task Allocation Algorithm for a Multi-Robot System in Healthcare Facilities

Various ambient assisted living (AAL) technologies have been proposed for improving the living conditions of elderly people. One of them is to introduce robots to reduce dependency on support staff. The tasks commonly encountered in a healthcare facility such as a care home for elderly people are heterogeneous and are of different priorities. A care home environment is also dynamic and new emergency priority tasks, which if not attended shortly may result in fatal situations, may randomly appear. Therefore, it is better to use a multi-robot system (MRS) consisting of heterogeneous robots than designing a single robot capable of doing all tasks. An efficient task allocation algorithm capable of handling the dynamic nature of the environment, the heterogeneity of robots and tasks, and the prioritisation of tasks is required to reap the benefits of introducing an MRS. This paper proposes Consensus Based Parallel Auction and Execution (CBPAE), a distributed algorithm for task allocation in a system of multiple heterogeneous autonomous robots deployed in a healthcare facility, based on auction and consensus principles. Unlike many of the existing market based task allocation algorithms, which use a time extended allocation of tasks before the actual execution is initialised, the proposed algorithm uses a parallel auction and execution framework, and is thus suitable for highly dynamic real world environments. The robots continuously resolve any conflicts in the bids on tasks using inter-robot communication and a consensus process in each robot before a task is assigned to a robot. We demonstrate the effectiveness of the CBPAE by comparing its simulation results with those of an existing market based distributed multi-robot task allocation algorithm and through experiments on real robots.

An Overlay-Based Data Mining Architecture Tolerant to Physical Network Disruptions

Management scheme for highly scalable big data mining has not been well studied in spite of the fact that big data mining provides many valuable and important information for us. An overlay-based parallel data mining architecture, which executes fully distributed data management and processing by employing the overlay network, can achieve high scalability. However, the overlay-based parallel mining architecture is not capable of providing data mining services in case of the physical network disruption that is caused by router/communication line breakdowns because numerous nodes are removed from the overlay network. To cope with this issue, this paper proposes an overlay network construction scheme based on node location in physical network, and a distributed task allocation scheme using overlay network technology. The numerical analysis indicates that the proposed schemes considerably outperform the conventional schemes in terms of service availability against physical network disruption.

Resource Welfare Based Task Allocation for UAV Team with Resource Constraints

This paper addresses a task allocation problem for a team of UAVs that cooperatively performs a search and attack mission in an unknown region. The UAVs are heterogeneous carrying different types and amounts of munition resources, and limited in communications and sensing capabilities. The environment is highly uncertain and dynamic where no prior information is available and dynamic events such as UAV failures unpredictably occur. The objective of the mission is to maximize total reward obtained by destroying targets within a given mission horizon. A group of UAVs may need to be formed to attack a target because individual UAVs may not have sufficient resources for the execution of attack tasks. Instantaneous task allocation approaches that seek for optimal solution for current tasks cannot effectively account for the unpredictability of future tasks in the uncertain dynamic environment. In this paper, we propose a distributed task allocation scheme based on resource welfare of which concept is adopted from economics. The approach we present enables the UAV team to effectively utilize resources by balancing resource depletions and consequently be capable of smoothly responding to dynamic events by retaining more UAVs available. Simulation experiments were conducted in various conditions to evaluate the performance of the proposed approach in comparison with the instantaneous task allocation method. The results show that our approach improves the performance by up to 29.3 % with respect to the instantaneous task allocation method.

From selfish auctioning to incentivized marketing

Auction and market-based mechanisms are among the most popular methods for distributed task allocation in multi-robot systems. Most of these mechanisms were designed in a heuristic way and analysis of the quality of the resulting assignment solution is rare. This paper presents a new market-based multi-robot task allocation algorithm that produces optimal assignments. Rather than adopting a buyer's "selfish" bidding perspective as in previous auction/market-based approaches, the proposed method approaches auctioning from a merchant's point of view, producing a pricing policy that responds to cliques of customers and their preferences. The algorithm uses price escalation to clear a market of all its items, producing a state of equilibrium that satisfies both the merchant and customers. This effectively assigns all robots to their tasks. The proposed method can be used as a general assignment algorithm as it has a time complexity ( $$O(n^3 \text {lg} n)$$ O ( n 3 lg n ) ) close to the fastest state-of-the-art algorithms ( $$O(n^3)$$ O ( n 3 ) ) but is extremely easy to implement. As in previous research, the economic model reflects the distributed nature of markets inherently: in this paper it leads directly to a decentralized method ideally suited for distributed multi-robot systems.

A Distributed Task Allocation Strategy for Collaborative Applications in Cluster-Based Wireless Sensor Networks

Balancing energy consumption and prolonging network lifetime have been hot topics in energy limited wireless sensor networks (WSNs). It is generally known that multiple sensor nodes collaborating with each other to perform tasks are an important means of energy balancing. In addition, a reasonable task allocation strategy is also an efficient method for task performing and energy saving. Furthermore, in order to save energy consumption, many WSNs are organized into clusters. Therefore, in this paper, we propose a distributed task allocation strategy for collaborative applications (DTAC) in cluster-based WSNs. In DTAC, the service abilities of sensor nodes are first evaluated and then the sensor nodes are classified into different ranking domains. Based on the service abilities of cluster members, the service ability of each cluster is calculated. A complex task can be first allocated to several clusters, and then scheduled on collaborative cluster members. Simulation results demonstrate that, compared with current related works, DTAC is much more efficient for complex task processing in terms of reducing energy consumption, shortening execution time, and balancing network loads.

탐지 및 공격 임무를 수행하는 로봇팀의 효율적 자원관리를 통한 작업할당방식

In this paper, we address a task allocation problem for a robot team that performs a search and attack mission. The robots are limited in sensing and communication capabilities, and carry different types of resources that are used to attack a target. The environment is uncertain and dynamic where no prior information about targets is given and dynamic events unpredictably happen. The goal of robot team is to collect total utilities as much as possible by destroying targets in a mission horizon. To solve the problem, we propose a distributed task allocation framework incorporated with effective resource management based on resource welfare. The framework we propose enables the robot team to retain more robots available by balancing resources among robots, and respond smoothly to dynamic events, which results in system performance improvement.

Dynamic Mission Planning for Communication Control in Multiple Unmanned Aircraft Teams

A multi-UAV system relies on communications to operate. Failure to communicate remotely sensed mission data to the base may render the system ineffective, and the inability to exchange command and control messages can lead to system failures. This paper describes a unique method to control network communications through distributed task allocation to engage under-utilized UAVs to serve as communication relays and to ensure that the network supports mission tasks. This work builds upon a distributed algorithm previously developed by the authors, CBBA with Relays, which uses task assignment information, including task location and proposed execution time, to predict the network topology and plan support using relays. By explicitly coupling task assignment and relay creation processes, the team is able to optimize the use of agents to address the needs of dynamic complex missions. In this work, the algorithm is extended to explicitly consider realistic network communication dynamics, including path loss, stochastic fading, and information routing. Simulation and flight test results validate the proposed approach, demonstrating that the algorithm ensures both data-rate and interconnectivity bit-error-rate requirements during task execution.