Multi-robot Task Allocation Research Articles

Task assignment strategy for multi-robot based on improved Grey Wolf Optimizer

Multi-robot task allocation (MRTA) is the basis of a multi-robot system to perform tasks automatically, which directly affects the execution efficiency of the whole system. A distributed cooperative task allocation strategy based on the algorithm of the improved Grey Wolf Optimizer (IGWO) was proposed to quickly and effectively plan the cooperative task path with a large number of working task points. The MRTA problem was transformed into multiple traveling salesman problems (MTSPs), and the task target points were clustered by the K-means clustering algorithm and divided into several traveling salesman problems (TSPs). The Grey Wolf Optimizer (GWO) was improved by the Kent chaotic algorithm to initialize the population and enhance the diversity of the population. Furthermore, an adaptive adjustment strategy of the control parameter $$\vec {a}$$ was proposed to balance exploration and exploitation. The individual speed and position updates in PSO were introduced to enable the gray wolf individual to preserve its optimal location information and accelerate the convergence speed. The IGWO was used to solve the optimal solutions to multiple TSP problems. Finally, the optimal solution space was integrated to get the optimal solution of MTSP, and 16 international classical test functions simulated the IGWO. The results showed that the IGWO algorithm has faster convergence speed and higher accuracy. The task allocation strategy is reasonable, with roughly equal path length, small planning cost, fast convergence speed, and excellent stability.

Development of a task-oriented, auction-based task allocation framework for a heterogeneous multirobot system

A multirobot system has cooperative team of robots designed to enhance efficiency of its operations. One of the critically investigated problems of multirobot system is the multirobot task allocation (MRTA) issue. The main objective of MRTA is to assign tasks to the most suitable robot based on its functions and capability as well as availability. In this paper, a task-oriented, auction-based task allocation framework is presented and tested through simulations and real-world experiments. The developed framework consists of a novel heuristic-based task allocation algorithm and communication module. It is implemented in a multirobot system, allowing tasks to be dynamically assigned to the robots as they achieve given tasks. The implemented framework shows robustness in its flexibility to the task and environment requirements such as resource and energy requirements and size of the environment. The framework involved a task allocation algorithm, which consists of bid generation and bid selection process, and a TCP/IP-based client-server communication module. The results from both simulations and real-world experiments matched, producing optimum results in task allocation.

Dynamic Multi-Objective Auction-Based (DYMO-Auction) Task Allocation

In this paper, we address the problem of online dynamic multi-robot task allocation (MRTA) problem. In the existing literature, several works investigated this problem as a multi-objective optimization (MOO) problem and proposed different approaches to solve it including heuristic methods. Existing works attempted to find Pareto-optimal solutions to the MOO problem. However, to the best of authors’ knowledge, none of the existing works used the task quality as an objective to optimize. In this paper, we address this gap, and we propose a new method, distributed multi-objective task allocation approach (DYMO-Auction), that considers tasks’ quality requirement, along with travel distance and load balancing. A robot is capable of performing the same task with different levels of perfection, and a task needs to be performed with a level of perfection. We call this level of perfection quality level. We designed a new utility function to consider four competing metrics, namely the cost, energy, distance, type of tasks. It assigns the tasks dynamically as they emerge without global information and selects the auctioneer randomly for each new task to avoid the single point of failure. Extensive simulation experiments using a 3D Webots simulator are conducted to evaluate the performance of the proposed DYMO-Auction. DYMO-Auction is compared with the sequential single-item approach (SSI), which requires global information and offline calculations, and with Fuzzy Logic Multiple Traveling Salesman Problem (FL-MTSP) approach. The results demonstrate a proper matching with SSI in terms of quality satisfaction and load balancing. However, DYMO-Auction demands 20% more travel distance. We experimented with DYMO-Auction using real Turtlebot2 robots. The results of simulation experiments and prototype experiments follow the same trend. This demonstrates the usefulness and practicality of the proposed method in real-world scenarios.

Particle Swarm Optimization for Cooperative Multi-Robot Task Allocation: A Multi-Objective Approach

This letter presents a new Multi-Objective Particle Swarm Optimization (MOPSO) approach to a Cooperative Multi-Robot Task Allocation (CMRTA) problem, where the robots have to minimize the total team cost and, additionally, balance their workloads. We formulate the CMRTA problem as a more complex variant of multiple Travelling Salesman Problems (mTSP) and, in particular, address how to minimize the total travel distance of the entire robot team, as well as how to minimize the highest travel distance of an individual robot. The proposed approach extends the standard single-objective Particle Swarm Optimization (PSO) to cope with the multiple objectives, and its novel feature lies in a Pareto front refinement strategy and a probability-based leader selection strategy. To validate the proposed approach, we first use three benchmark functions to evaluate the performance of finding the true Pareto fronts in comparison with four existing well-known algorithms in continuous spaces. Afterwards, we use six datasets to investigate the task allocation mechanisms in dealing with the CMRTA problem in discrete spaces.

Yard crane and AGV scheduling in automated container terminal: A multi-robot task allocation framework

The efficiency of automated container terminals primarily depends on the synchronization of automated-guided vehicles (AGVs) and automated cranes. Accordingly, we study the integrated rail-mounted yard crane and AGV scheduling problem as a multi-robot coordination and scheduling problem in this paper. Based on a discretized virtualized network, we propose a multi-commodity network flow model with two sets of flow balance constraints for cranes and AGVs. In addition, two side constraints are introduced to deal with inter-robot constraints to reflect the complex interactions among terminal agents accurately. The Alternating Direction Method of Multipliers (ADMM) method is adopted in this study as a market-driven approach to dualize the hard side constraints; therefore, the original problem is decomposed into a set of crane-specific and vehicle-specific subtasks. The cost-effective solutions can be obtained by iteratively adjusting both the primal and dual costs of each subtask. We also compare the computational performance of the proposed solution framework with that of the resource-constrained project scheduling problem (RCPSP) model using commercial solvers. Comparison results indicate that our proposed approach could efficiently find solutions within 2% optimality gaps. Illustrative and real-world instances show that the proposed approach effectively serves the accurate coordination of AGVs and cranes in automated terminals.

Correlation clustering-based multi-robot task allocation

Robots currently available in the market are somewhat limited in their capabilities, which makes it difficult for a single robot to handle many real-world tasks that are inherently complex. Thus, it becomes necessary to engage multiple robots which might form coalitions to complete such complex tasks. In this paper, we consider the well-known NP-hard problem of forming coalitions with the goal of instantaneous allocation (IA) of a group of robots to a set of tasks for optimal completion of the tasks. With the goal of bringing similar robots together to form coalitions, we use a correlation clustering technique, which uses a Linear Programming-based graph partitioning approach along with a region growing strategy. Our proposed approach is shown to gracefully handle both cost and utility-based objective functions for task allocation. We demonstrate that the resulting algorithm is fast and efficient in allocating (near) optimal robot coalitions to tasks. Furthermore, it outperforms two existing approaches in terms of execution times while yielding similar new-optimal solutions.

Towards Multi Robot Task Allocation and Navigation using Deep Reinforcement Learning

Developing algorithms for multi robot systems to reach target positions and navigate safely in the environment is an open field of research. Most systems treat Multi Robot Task Allocation (MRTA) and Multi Robot Path Planning (MRPP) as two separate steps each with its own set of algorithms in which the MRTA algorithm assigns each robot to a task and the MRPP algorithm guides each robot through the environment towards the assigned goal position while avoiding both static and dynamic obstacles. In this paper, we present a method that combines both steps by using a deep reinforcement learning model. The model consists of a decentralized sensor level policy which outputs the robot’s velocity to guide it through the environment towards the selected goal position and avoiding collisions. The model was trained in a simulation environment and all the robots are homogenous differential drive robots. The objective is to ensure that each robot reaches a unique goal position with the number of goal positions is equal to the number of robots. The results of training the policy in an environment is presented with both static and dynamic obstacles with four robots and four goal positions.

A Distributed Approach to the Multi-Robot Task Allocation Problem Using the Consensus-Based Bundle Algorithm and Ant Colony System

We propose a distributed approach to solve the multi-robot task allocation problem. This problem consists of two distinct sets: robots and tasks. The objective is to assign tasks to robots while optimizing a given criterion. This problem is known to be $\mathcal {NP}$ -hard even with small numbers of robots and tasks. The field of survivors’ search and rescue is adopted: i.e. some Unmanned Aerial Vehicles are used to rescue a number of survivors. We choose this problem, given its importance in everyday life: (a) survivors are the tasks; (b) Unmanned Aerial Vehicles are the robots; and (c) the objective is to rescue the maximum number of survivors while minimizing the makespan (time elapsed between rescuing the first and last survivors) and traveled distances. The approach is composed of two phases: inclusion and consensus. During the inclusion phase, each Unmanned Aerial Vehicle builds a bundle of survivors using the Ant Colony System. During the consensus phase, Unmanned Aerial Vehicles resolve conflicts in their bundles of survivors (i.e. a survivor is being chosen by more than two Unmanned Aerial Vehicles), using an adequate coordination mechanism. The approach is implemented using Java programming language and JADE multi-agent Framework. The performance of our approach is compared to five state-of-the-art multi-robot task allocation solutions. Simulation results show that the proposed approach outperforms these solutions, in terms of: (i) makespans; (ii) traveled distances; and (iii) exchanged messages.

A Multi-Level Architecture for Solving the Multi-Robot Task Allocation Problem Using a Market-Based Approach

A Multi-Level Architecture for Solving the Multi-Robot Task Allocation Problem Using a Market-Based Approach

Towards a Distributed Solution to the Multi-Robot Task Allocation Problem with Energetic and Spatiotemporal Constraints

The multi-robot task allocation problem consists of two distinct sets: a set of tasks (requiring resources) and a set of robots (offering resources); then tasks are allocated to robots; while optimizing a certain objective function, subject to some constraints: e.g. allocate the maximum number of tasks, minimize the distances travelled by robots, etc. In this paper, we propose some objective functions, and our main contribution is the introduction of energetic constraints on multi-robot task allocation problems. In addition, we propose an allocation method (based on parallel distributed guided genetic algorithms) and compare it to two state-of-the-art algorithms. Performed simulations and obtained results show the effectiveness and scalability of our solution, even with a large number of robots and tasks.

Coalition Formation for Multi-Robot Task Allocation via Correlation Clustering

The complexity of a vast number of real world tasks provides a great challenge for the currently available robots due to their limited capabilities. Thus, multiple robots would need to form coalitions for the completion of such tasks. In this paper, we examine the multi-robot coalition formation problem for task allocation where a group of robots needs to be allocated to a set of tasks. Our approach for this problem is to use a correlation clustering technique enabling similar robots to form coalitions. The algorithm presented in this paper is fast and scales better in comparison to two existing algorithms.

Multi-Robot Dynamic Task Allocation for Exploration and Destruction

Environmental exploration is one of the common tasks in the robotic domain which is also known as foraging. In comparison with the typical foraging tasks, our work focuses on the Multi-Robot Task Allocation (MRTA) problem in the exploration and destruction domain, where a team of robots is required to cooperatively search for targets hidden in the environment and attempt to destroy them. As usual, robots have the prior knowledge about the suspicious locations they need to explore but they don’t know the distribution of interested targets. So the destruction task is dynamically generated along with the execution of exploration task. Each robot has different strike ability and each target has uncertain anti-strike ability, which means either the robot or target is likely to be damaged in the destruction task according to that whose ability is higher. The above setting significantly increases the complexity of exploration and destruction problem. The auction-based approach, vacancy chain approach and a deep Q-learning approach based on strategy-level selection are employed in this paper to deal with this problem. A new simulation system based on Robot Operating System and Gazebo is specially built for this MRTA problem. Subsequently, extensive simulation results are provided to show that all proposed approaches are able to solve the MRTA problem in exploration and destruction domain. In addition, experimental results are further analyzed to show that each method has its own advantages and disadvantages.

Submodular Optimization for Coupled Task Allocation and Intermittent Deployment Problems

In this paper, we demonstrate a formulation for optimizing coupled submodular maximization problems with provable sub-optimality bounds. In robotics applications, it is quite common that optimization problems are coupled with one another and therefore cannot be solved independently. Specifically, we consider two problems coupled if the outcome of the first problem affects the solution of a second problem that operates over a longer time scale. For example, in our motivating problem of environmental monitoring, we posit that multi-robot task allocation will potentially impact environmental dynamics and thus influence the quality of future monitoring, here modeled as a multi-robot intermittent deployment problem. The general theoretical approach for solving this type of coupled problem is demonstrated through this motivating example. Specifically, we propose a method for solving coupled problems modeled by submodular set functions with matroid constraints. A greedy algorithm for solving this class of problem is presented, along with sub-optimality guarantees. Finally, practical optimality ratios are shown through Monte Carlo simulations to demonstrate that the proposed algorithm can generate near-optimal solutions with high efficiency.

Adaptive Risk-Based Replanning For Human-Aware Multi-Robot Task Allocation With Local Perception

In this letter, we propose an adaptive risk-based replanning strategy in the context of multirobot task allocation for dealing with limitations of local perception and unpredicted human behavior. Our replanning method is based on the variations of social risk and human motion prediction uncertainty. The performance of our method is studied through an extensive suite of experiments of increasing complexity. Results obtained using both a high-fidelity simulator and real robots confirm that this strategy outperforms a nonadaptive replanning strategy in all cases with respect to the chosen social metrics. First, the overall performance of the team depends on its replanning strategy, and second on the available information about the humans. Although an adaptive replanning strategy with global perception leads to the best performance, it is computationally expensive and infeasible in some real applications. Local perception shows comparable results as long as updates of relevant human poses affecting a task's risk are available within the execution time of that task. Conversely, the nonadaptive replanning strategy is shown to have degraded results with global perception as decisions in this case can be based on outdated information that lead to invalid plans.

An Efficient Scheduling Algorithm for Multi-Robot Task Allocation in Assembling Aircraft Structures

Efficient utilization of cooperating robots in the assembly of aircraft structures relies on balancing the workload of the robots and ensuring collision-free scheduling. We cast this problem as that of allocating a large number of repetitive assembly tasks, such as drilling holes and installing fasteners, among multiple robots. Such task allocation is often formulated as a traveling salesman problem, which is NP-hard, implying that computing an exactly optimal solution is computationally prohibitive for real-world applications. The problem complexity is further exacerbated by intermittent robot failures necessitating real-time task reallocation. In this letter, we present an efficient method that exploits workpart geometry and problem structure to initially generate balanced and conflict-free robot schedules under nominal conditions. Subsequently, we deal with the failures by allowing the robots to first complete their nominal schedules and then employing a market-based optimizer to allocate the leftover tasks. Results show an improvement of 11.5% in schedule efficiency as compared to an optimized greedy multi-agent scheduler on a four robot system, which is especially promising for aircraft assembly processes that take many hours to complete. Moreover, the computation times are similar and small, typically hundreds of milliseconds.

ACD3GPSO: automatic clustering-based algorithm for multi-robot task allocation using dynamic distributed double-guided particle swarm optimization

PurposeThe multi-robot task allocation (MRTA) problem is a challenging issue in the robotics area with plentiful practical applications. Expanding the number of tasks and robots increases the size of the state space significantly and influences the performance of the MRTA. As this process requires high computational time, this paper aims to describe a technique that minimizes the size of the explored state space, by partitioning the tasks into clusters. In this paper, the authors address the problem of MRTA by putting forward a new automatic clustering algorithm of the robots' tasks based on a dynamic-distributed double-guided particle swarm optimization, namely, ACD3GPSO.Design/methodology/approachThis approach is made out of two phases: phase I groups the tasks into clusters using the ACD3GPSO algorithm and phase II allocates the robots to the clusters. Four factors are introduced in ACD3GPSO for better results. First, ACD3GPSO uses the k-means algorithm as a means to improve the initial generation of particles. The second factor is the distribution using the multi-agent approach to reduce the run time. The third one is the diversification introduced by two local optimum detectors LODpBest and LODgBest. The last one is based on the concept of templates and guidance probability Pguid.FindingsComputational experiments were carried out to prove the effectiveness of this approach. It is compared against two state-of-the-art solutions of the MRTA and against two evolutionary methods under five different numerical simulations. The simulation results confirm that the proposed method is highly competitive in terms of the clustering time, clustering cost and MRTA time.Practical implicationsThe proposed algorithm is quite useful for real-world applications, especially the scenarios involving a high number of robots and tasks.Originality/valueIn this methodology, owing to the ACD3GPSO algorithm, task allocation's run time has diminished. Therefore, the proposed method can be considered as a vital alternative in the field of MRTA with growing numbers of both robots and tasks. In PSO, stagnation and local optima issues are avoided by adding assorted variety to the population, without losing its fast convergence.

Robots in the Huddle: Upfront Computation to Reduce Global Communication at Run Time in Multirobot Task Allocation

In this article, we study multirobot task allocation problems where task costs vary. The variation may be, for example, due to the revelation of new information or other dynamic circumstances. As robots update their cost estimates, typically they will update task assignments to reflect the new information using additional communication and computation. In dynamic settings, the robots are continually repairing the optimality of the system's task assignments, which can incur substantial communication and computation. We investigate how one can reduce communication and centralized computation expense during execution by using a prior model of how costs may change and performing upfront computation of possible robot–task assignments. First, we develop an algorithm that partitions a team of robots into several independent subteams that are able to maintain global optimality by communicating entirely amongst themselves. Second, we propose a method for computing the worst-case cost suboptimality if robots persist with the initial assignment and perform no further communication and computation. Finally, we introduce an algorithm to assess whether cost changes affect the optimality of the current assignment through a succession of local communication exchanges. Experimental results show that the proposed methods are helpful in reducing the degree of centralization needed by a multirobot system (e.g., the third method gave at least 45% reduction of global communication across all scenarios studied). The methods are valuable in transitioning multirobot techniques, which have met with success in structured applications (such as factories and warehouses) to the broader, wilder world.

Distributed task allocation method based on self-awareness of autonomous robots

Multi-robot task allocation problem is an important research field in multi-robot systems. The multi-robot task allocation problem combined with affective computing is an interesting frontier problem. This paper proposes a distributed affective robot pursuit task allocation algorithm based on self-awareness, which combines cognitive intelligence with emotional intelligence to establish self-awareness for affective robots. This paper defines the cognitive mechanism, environmental detection mechanism and self-decision mechanism of affective robots, so that the self-aware affective robots can make decisions autonomously by grasping the information of itself and the environment. The experimental results show better efficiency of this task allocation algorithm at each task scale, and the allocation efficiency is gradually improved as the number of robot increases.

FA–QABC–MRTA: a solution for solving the multi-robot task allocation problem

The problem of task allocation in a multi-robot system is the situation where we have a set of tasks and a number of robots; then each task is assigned to the appropriate robots with the aim of optimizing some criteria subject to constraints, e.g., allocate the maximum number of tasks. We propose an effective solution to address this problem. It implements a two-stage methodology: first, a global allocation based of the well-known firefly algorithm, and then, a local allocation combining advantages of quantum genetic algorithms and artificial bee colony optimization. We compared our proposed solution to one solution from the state of the art. The simulation results show that our scheme significantly performs better than this solution. Our solution allocated $$100\%$$ of the tasks (in every configuration tried in the experiments) and enhanced the allocation time by $$75\%$$ .

A distributed method for dynamic multi-robot task allocation problems with critical time constraints

This paper considers the task allocation problems in a distributed multi-robot system under critical time constraints. Considering the requirement of distributed computing, many existing distributed heuristic task allocation approaches tend to trap in local optimal and cannot obtain high-quality solutions. For a dynamic task allocation problem in a multi-robot system, not only the task information and the robot state may be subject to changes, but also the network status. That is, robots that each robot can communicate with may change over time, and sometimes there may even be no robots that it can communicate with. To solve these problems, a dynamic grouping allocation method is proposed. It builds upon the state-of-the-art consensus-based auction algorithms, extending them in both task inclusion phase and consensus phase. First, a cluster-first strategy and a task inclusion procedure that can be easily applied to the task inclusion phase of the algorithms are proposed, so that the solution quality of each iteration of the algorithms are significantly improved with a reasonable amount of computation. In addition, to increase the exploration capabilities, a proportional selection method is used in the task inclusion procedure when it is likely to trap in a local optimal. Second, the block-information-sharing strategy is used to avoid the possible conflicts that dynamic changes may bring. Numerical simulations demonstrate that the proposed method can provide conflict-free solutions in dynamic environments and can achieve outstanding performance in comparison with the state-of-the-art algorithms.