Task Allocation For Multi-robot Systems Research Articles

Swarm based Optimization Algorithms for Task Allocation in Multi Robot Systems: A Comprehensive Review

Multi-robot systems (MRS) have gained significant attention due to their potential applications in various domains such as search and rescue, surveillance, and exploration. An essential aspect of MRS is task allocation, which involves distributing tasks among robots efficiently to achieve collective objectives. Swarm-based optimization algorithms have emerged as effective approaches for task allocation in MRS, leveraging principles inspired by natural swarms to coordinate the actions of multiple robots. This paper provides a comprehensive review of swarm-based optimization algorithms for task allocation in MRS, highlighting their principles, advantages, challenges, and applications. The discussion encompasses key algorithmic approaches, including ant colony optimization, particle swarm optimization, and artificial bee colony optimization, along with recent advancements and future research directions in this field.

Task allocation in multi-robot system using resource sharing with dynamic threshold approach.

Task allocation is a fundamental requirement for multi-robot systems working in dynamic environments. An efficient task allocation algorithm allows the robots to adjust their behavior in response to environmental changes such as fault occurrences, or other robots’ actions to increase overall system performance. To address these challenges, this paper presents a Task Allocation technique based on a threshold level which is an accumulative value aggregated by a centralized unit using the Task-Robot ratio and the number of the available resource in the system. The threshold level serves as a reference for task acceptance and the task acceptance occurs despite resource shortage. The deficient resources for the accepted task are acquired through an auction process using objective minimization. Despite resource shortage, task acceptance occurs. The threshold approach and the objective minimization in the auction process reduce the overall completion time and increase the system’s resource utilization up to 96%, which is demonstrated theoretically and validated through simulations and real experimentation.

Cooperative Task Allocation for Multi-Robot Systems Based on Multi-Objective Ant Colony System

This paper proposes a novel multi-objective ant colony system (MOACS) approach to solve the cooperative task allocation problem of multi-robot systems. The task allocation problem is formulated as a multi-objective multiple traveling salesman problem (MTSP). The objectives are to minimize the total and maximum cost of the robotic vehicles so that the workload of each vehicle could be balanced. The time cost matrices of the salesmen are different and asymmetric due to the different flight speeds of vehicles and executing time of tasks. Based on the single-objective ant colony system (ACS), a novel solution construction method and a novel pheromone update rule are proposed. At each step in the solution construction phase, the ant with minimum cost has the biggest chance to add an unassigned task to balance the workload of each vehicle, while the ant with maximum cost also has a bigger chance than any other ants to add an unassigned task to find better Pareto front. The minimum value of the pheromone is limited in the pheromone update phase, which is helpful in avoiding fast convergence and local optima. Extensive simulation results suggest that the proposed MOACS has better performance and effectiveness than the existing non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO). Hardware-in-the-loop experiments on multiple unmanned aerial vehicles (UAVs) also show that compared with NSGA-II and MOPSO, the maximum and total flight distance of the UAVs with the proposed MOACS are decreased by up to 28.46% and 26.34%, respectively, while the maximum and total time used to finish all tasks are decreased by up to 23.86% and 17.94%.

Dynamic Task Allocation in Multi-Robot System Based on a Team Competition Model.

In recent years, it is a trend to integrate the ideas in game theory into the research of multi-robot system. In this paper, a team-competition model is proposed to solve a dynamic multi-robot task allocation problem. The allocation problem asks how to assign tasks to robots such that the most suitable robot is selected to execute the most appropriate task, which arises in many real-life applications. To be specific, we study multi-round team competitions between two teams, where each team selects one of its players simultaneously in each round and each player can play at most once, which defines an extensive-form game with perfect recall. We also study a common variant where one team always selects its player before the other team in each round. Regarding the robots as the players in the first team and the tasks as the players in the second team, the sub-game perfect strategy of the first team computed via solving the team competition gives us a solution for allocating the tasks to the robots—it specifies how to select the robot (according to some probability distribution if the two teams move simultaneously) to execute the upcoming task in each round, based on the results of the matches in the previous rounds. Throughout this paper, many properties of the sub-game perfect equilibria of the team competition game are proved. We first show that uniformly random strategy is a sub-game perfect equilibrium strategy for both teams when there are no redundant players. Secondly, a team can safely abandon its weak players if it has redundant players and the strength of players is transitive. We then focus on the more interesting case where there are redundant players and the strength of players is not transitive. In this case, we obtain several counterintuitive results. For example, a player might help improve the payoff of its team, even if it is dominated by the entire other team. We also study the extent to which the dominated players can increase the payoff. Very similar results hold for the aforementioned variant where the two teams take actions in turn.

Accuracy in Parallel Dynamic Task Allocation for Multi-Robot Systems Under Fuzzy Environment

Making the right decision is an essential requirement for the task allocation process in multi-robot systems functioning in dynamic environments. Robots are often forced to make these decisions individually without any communication between them. It may be due to reasons related to uncertainty in environments or related to tasks security, such as military applications. However, robot decisions must be precise in order to increase their efficiency to perform complex tasks. This paper presents a model in which a criterion of accuracy in tasks allocation process in an uncertain environment is defined. In order to increase this precision in such environments, the robots will formulate their observations in terms of the fuzzy linguistic variables. These variables are used by a fuzzy inference system to determine a utility value of a task that most effectively increases accuracy in task allocation. Simulation results on a complex task of goods transportation by mobile robots are presented to demonstrate the effectiveness of this model.

Fuzzy predictor for parallel dynamic task allocation in multi-robot systems

This paper presents a model to decompose complex tasks in the form of elemental tasks executed in parallel by multi robots. In this model, a criterion of accuracy in the parallel dynamic tasks allocation process (APDTA) is defined. Through this APDTA, a predictor based on fuzzy logic called FP-TE is developed to evaluate the importance of elemental tasks in the system. The inputs of this predictor are described as observations acquired from sensor data. The FP-TE output will be used to allow each robot to individually decide what task should be executed. Simulation results on goods transportation by mobile robots are presented to demonstrate the effectiveness of this fuzzy predictor.

Fuzzy predictor for parallel dynamic task allocation in multi-robot systems

Fuzzy predictor for parallel dynamic task allocation in multi-robot systems

An auction-based rescue task allocation approach for heterogeneous multi-robot system

Nowadays, robots are faced with real-time, dynamic, complex and confrontational working environment. It is significant to analyze task allocation in multi-robot systems. In this paper, a dynamic auction approach for differentiated tasks under cost rigidities (DAACR) is proposed, which can obtain optimal results in the task allocation of rescue robots. To verify the feasibility of the proposed approach, we investigate the optimality of the DAACR and compare it with other task allocation approaches based on the Hungarian algorithm. The results show that robots using this algorithm can adapt to a variety of complicated work environments, accomplish more tasks in limited time, reduce the delay of task allocation, and improve the overall utility of multi-robot systems.

Resource-based task allocation for multi-robot systems

This paper proposes a resource-based task allocation algorithm for multi-robot systems. During mission operations, robots continuously consume their resources which must be refilled during their operations. Unlike other existing auction-based algorithms in which robots do not account for their resources in task allocations, the proposed algorithm considers the resources of the robots to generate their costs. In this algorithm, robots calculate the task performance estimation considering all the possibilities of visiting different combinations of refill stations based on their resource levels. This enables the robots to reduce unnecessary wastage of time and resources during the mission. The effectiveness of the proposed algorithm with respect to task completion time, resource consumption, and communication overhead is theoretically analyzed and is also demonstrated from the simulation of the delivery mission.

Gini coefficient-based task allocation for multi-robot systems with limited energy resources

Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy supply of robots usually cannot be guaranteed. If the energy resources of some robots are consumed too fast, the number of the future tasks of the coalition will be affected. This paper will develop a novel task allocation method based on Gini coefficient to make full use of limited energy resources of multi-robot system to maximize the number of tasks. At the same time, considering resources consumption, we incorporate the market-based allocation mechanism into our Gini coefficient-based method and propose a hybrid method, which can flexibly optimize the task completion number and the resource consumption according to the application contexts. Experiments show that the multi-robot system with limited energy resources can accomplish more tasks by the proposed Gini coefficient-based method, and the hybrid method can be dynamically adaptive to changes of the work environment and realize the dual optimization goals.

Immigrants Based Adaptive Genetic Algorithms for Task Allocation in Multi-Robot Systems

Optimal task allocation among the suitably formed robot groups is one of the key issues to be investigated for the smooth operations of multi-robot systems. Considering the complete execution of available tasks, the problem of assigning available resources (robot features) to the tasks is computationally complex, which may further increase if the number of tasks increases. Popularly this problem is known as multi-robot coalition formation (MRCF) problem. Genetic algorithms (GAs) have been found to be quite efficient in solving such complex computational problems. There are several GA-based approaches to solve MRCF problems but none of them have considered the dynamic GA variants. This paper considers immigrants-based GAs viz. random immigrants genetic algorithm (RIGA) and elitism based immigrants genetic algorithm (EIGA) for optimal task allocation in MRCF problem. Further, it reports a novel use of these algorithms making them adaptive with certain modifications in their traditional attributes by adaptively choosing the parameters of genetic operators and terms them as adaptive RIGA (aRIGA) and adaptive EIGA (aEIGA). Extensive simulation experiments are conducted for a comparative performance evaluation with respect to standard genetic algorithm (SGA) using three popular performance metrics. A statistical analysis with the analysis of variance has also been performed. It is demonstrated that RIGA and EIGA produce better solutions than SGA for both fixed and adaptive genetic operators. Among them, EIGA and aEIGA outperform RIGA and aRIGA, respectively.

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

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

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.

Memetic algorithms for optimal task allocation in multi-robot systems for inspection problems with cooperative tasks

Multi-robot task allocation means to distribute and schedule a set of tasks to be accomplished by a group of robots to minimize cost while satisfying operational constraints. It can be challenging to solve a large number of tasks and becomes even more challenging when tightly coupled multi-robot tasks are also taken into account. For example, it is more complex to solve problems that include tasks that have to be carried out jointly by two robots due to the resulting temporal and spatial constraints. Additionally, the complexity of task allocation increases exponentially with rising task variety. This paper focuses on multi-robot task allocation in inspection problems with both single- and two-robot tasks. A novel memetic algorithm is proposed combining a genetic algorithm with two local search schemes. Using permutation representation, eight approaches based on four basic coding strategies are designed for multi-robot task allocation of inspection problems with two-robot tasks. The performance of the memetic algorithm is evaluated in case studies on inspecting a large storage tank area of a petroleum refinery.

A comprehensive taxonomy for multi-robot task allocation

Task allocation is an important aspect of many multi-robot systems. The features and complexity of multi-robot task allocation (MRTA) problems are dictated by the requirements of the particular domain under consideration. These problems can range from those involving instantaneous distribution of simple, independent tasks among members of a homogenous team, to those requiring the time-extended scheduling of complex interrelated multi-step tasks for members of a heterogenous team related by several constraints. The existing widely used taxonomy for task allocation in multi-robot systems was designed for problems with independent tasks and does not deal with problems with interrelated utilities and constraints. While that taxonomy was a ground-breaking contribution to the MRTA literature, a survey of recent work in MRTA reveals that it is no longer a sufficient taxonomy, due to the increasing importance of interrelated utilities and constraints in realistic MRTA problems under consideration. Thus, in this paper, we present a new, comprehensive taxonomy, iTax, that explicitly takes into consideration the issues of interrelated utilities and constraints. Our taxonomy maps categories of MRTA problems to existing mathematical models from combinatorial optimization and operations research, and hence draws important parallels between robotics and these fields.

Social-welfare based task allocation for multi-robot systems with resource constraints

This paper aims to propose a distributed task allocation algorithm for a team of robots that have constraints on energy resources and operate in an unknown dynamic environment. The objective of the allocation is to maximize task completion ratio while minimizing resource usage. The approach we propose is inspired by the social welfare in economics that helps extend the combined operational lifetime of the team by balancing resource consumptions among robots. This social welfare based task allocation method positions a robot team appropriately in preparedness for dynamic future events and enables to achieve the objectives of the system flexibly depending on the application context. Our simulation-based experiments show that the proposed algorithm outperforms a typical market-based approach in various scenarios.

Distributed Task Allocation in Multi-Robot Systems Using Argumentation-Based Negotiation

The continuous growing of application’s complexity and increased interest in automated negotiation brought recently researcher’s attention to persuasive negotiation (PN) and argumentation-based negotiation (ABN). The Market-based approach has gained popularity in the last decade due to its flexibility, speed and robustness. Contract Net protocol inspired algorithms have been proved suitable for allocating weakly coupled tasks in robot teams, but there are still some challenges when addressing complex application in uncertain environments. In this context the purpose of the paper is to present a method to allocate tasks in multi-robot systems through the use of augmentation- based negotiation.

Integrated Multi-Robotic Assembly Planning for Improved Productivity

In product assembly, optimized sequence is a prerequisite for automated systems. The assembly process can be optimized through appropriate selection and allocation of the given tasks in a multi-device framework. These two discrete tasks need to be integrated to produce the optimum result and a cost effective system to cope with the needs of the system, the present work attempts to generate an automatic assembly sequence and seeks for optimal allocation of tasks amongst the available robots. Further, an effective task allocation approach considers the capabilities of the deployable robots. This paper presents an integrated approach for assembly sequence generation and task allocation for multi-robot systems by considering their capability in terms of time and space. An example of a 21 part drive assembly is given to illustrate the concept and procedure of the proposed methodology.

Balancing task allocation in multi-robot systems using K-means clustering and auction based mechanisms

This paper aims to solve the balanced multi-robot task allocation problem. Multi-robot systems are becoming more and more significant in industrial, commercial and scientific applications. Effectively allocating tasks to multi-robots i.e. utilizing all robots in a cost effective manner becomes a tedious process. The current attempts made by the researchers concentrate only on minimizing the distance between the robots and the tasks, and not much importance is given to the balancing of work loads among robots. It is also found from the literature that the multi-robot system is analogous to Multiple Travelling Salesman Problem (MTSP). This paper attempts to develop mechanism to address the above two issues with objective of minimizing the distance travelled by ‘m’ robots and balancing the work load between ‘m’ robots equally. The proposed approach has two fold, first develops a mathematical model for balanced multi-robot task allocation problem, and secondly proposes a methodology to solve the model in three stages. Stage I groups the ‘N’ tasks into ‘n’ clusters of tasks using K-means clustering technique with the objective of minimizing the distance between the tasks, stage II calculates the travel cost of robot and clusters combination, stage III allocates the robot to the clusters in order to utilise all robot in a cost effective manner.