Multi-robot Task Allocation Algorithm Research Articles

Swiss Round Selection Algorithm for Multi-Robot Task Scheduling

Efficient and stable control and task assignment optimization in electronic commerce logistics and warehousing systems involving multiple robots executing multiple tasks is highly challenging. Hence, this paper proposes a Swiss round selection algorithm for multi-robot task allocation to address the challenges mentioned. Firstly, based on the shipping process of electronic commerce logistics and warehousing systems, the tasks are divided into packaging and sorting stages, and a grid model for the electronic commerce warehousing system is established. Secondly, by increasing the probabilities of crossover and mutation in the population and adopting a full crossover and full mutation approach, the search scope of the population is expanded. Then, a Swiss round selection mechanism with burst probability is proposed, which ensures the smooth inheritance of high-quality individuals while improving the diversity of the population. Finally, 12 comparative experiments are designed with different numbers of robots and tasks. The experimental results demonstrate that the Swiss round selection algorithm outperforms the genetic algorithm in terms of maximum task completion time and convergence time to reach the optimal value. Thus, the effectiveness of the Swiss round selection algorithm in solving the multi-robot task allocation problem is verified.

An effective collaboration evolutionary algorithm for multi-robot task allocation and scheduling in a smart farm

The rapid development of intelligent and unmanned technologies has created new opportunities and challenges for smart farms. Agricultural production is increasingly incorporating intelligent robots for various farming activities. This paper investigates the task allocation and scheduling for multiple agricultural robots operating in a smart farm. For addressing this problem, a collaborative discrete bee colony algorithm (CDABC) is introduced to provide a viable solution, aiming to optimize the system efficiency in smart farms. To enhance the global search ability, several solution neighborhoods are designed based on a single robot and multi robots, and a problem-oriented dynamic neighborhood strategy list is given for the collaborative employment bees stage. Additionally, the neighborhoods based on the critical robot are constructed to enhance the local exploitation capability. A novel collaborative following bee strategy is also integrated to help individuals trapped in the local optimum move to more ideal areas in the scout bee stage. Finally, comprehensive design experiments are carried out to determine optimal parameter configurations and demonstrate that CDABC has the better performance than the state-of-the-art algorithms.

An efficient two-stage evolutionary algorithm for multi-robot task allocation in nuclear accident rescue scenario

With the growing maturity of multi-robot system technology, its applications have expanded across various domains. This paper addresses the critical issue of task allocation in nuclear accident rescue scenario, which plays a pivotal role in the success of such operations. The problem is formulated as a multi-objective optimization problem, taking into account three key indicators: execution time, radiation accumulation, and waiting cost. To effectively tackle this problem, an two-stage evolutionary algorithm is proposed. Firstly, a solution encoding method and a crossover mutation method is devised tailored to the problem’s characteristics. Secondly, a two-stage search strategy is designed. In the first stage, a fixed population size and shift-based density estimation method are used to quickly converge the solution set to the Pareto front. The latter stage uses an infinite size population to find as many Pareto solutions as possible. Finally, a local search strategy is introduced to improve the quality of solution set. In the experimental section, our proposed method is compared with five state-of-the-art algorithms on nine instances of varying scales. Across five evaluation metrics, the proposed algorithm demonstrates competitive performance on all instances. These results underscore the efficacy and competitiveness of our approach in tackling the task allocation problem in multi-robot systems within nuclear accident rescue.

A novel multimodal multi-objective optimization algorithm for multi-robot task allocation

Multi-robot task allocation (MRTA) is widely used in various fields and plays an important role in some complex task environments due to its ability to distribute parallel processing tasks. However, the multi-robot cooperative system is susceptible to actual environments or preferences of decision-makers. Therefore, providing enough solutions/schemes in the MRTA is important. To improve the reliability and feasibility of obtained solution set, an improved multimodal multi-objective differential evolution algorithm hybrid with a simulated annealing algorithm (IMMODE-SA) is proposed to solve MRTA problems in this study. In the proposed IMMODE-SA, a novel population initialization method is used to improve the population quality, and a redundant solution deletion method is employed to delete redundant solutions during the search process. Moreover, a simulated annealing algorithm is utilized to improve the exploitation capability in the last generation of evolutionary process. To verify the performance of the proposed algorithm, extensive simulation experiments are conducted on three MRTA instances. Experimental results show that the proposed algorithm performs better than other competitors on MRTA instances in terms of Hypervolume (HV). Also, the validity of the proposed algorithm is demonstrated via three experiments and experimental analysis results indicate that the IMMODE-SA can provide more equivalent optimal schemes to decision makers. Finally, it is crucial to solve MRTA problems with time window constraints.

Energy Harvesting and Task-Aware Multi-Robot Task Allocation in Robotic Wireless Sensor Networks

In this work, we investigate an energy-aware multi-robot task-allocation (MRTA) problem in a cluster of the robot network that consists of a base station and several clusters of energy-harvesting (EH) robots. It is assumed that there are M+1 robots in the cluster and M tasks exist in each round. In the cluster, a robot is elected as the cluster head, which assigns one task to each robot in that round. Its responsibility (or task) is to collect the resultant data from the remaining M robots to aggregate and transmit directly to the BS. This paper aims to allocate the M tasks to the remaining M robots optimally or near optimally by considering the distance to be traveled by each node, the energy required for executing each task, the battery level at each node, and the energy-harvesting capabilities of the nodes. Then, this work presents three algorithms: Classical MRTA Approach, Task-aware MRTA Approach, EH and Task-aware MRTA Approach. The performances of the proposed MRTA algorithms are evaluated under both independent and identically distributed (i.i.d.) and Markovian energy-harvesting processes for different scenarios with five robots and 10 robots (with the same number of tasks). EH and Task-aware MRTA Approach shows the best performance among all MRTA approaches by keeping up to 100% more energy in the battery than the Classical MRTA Approach and keeping up to 20% more energy in the battery than the Task-aware MRTA Approach.

Multi robot cooperative rescue based on two-stage task allocation algorithm

Task allocation is an important issue in the decision-making of multi robot fire rescue in underground garage. In this paper, a multi robot and multi-objective optimization model is constructed. According to the real-time requirements of fire rescue in the underground garage scene, a two-stage multi robot task allocation algorithm based on ant colony and contract net protocol is proposed, which solves the global deficiency of the allocation result based on contract net protocol and the defect that ant colony optimization can not respond to environmental changes in real time. The comparative test results in dynamic environment show that the two-stage task allocation algorithm is similar to ant colony optimization in global cost, and its real-time performance is better than ant colony optimization.

A Task Allocation Approach of Multi-Heterogeneous Robot System for Elderly Care

Roboticized nursing technology is a significant means to implement efficient elderly care and improve their welfare. Introducing multi-heterogeneous robot systems (MHRS) and sensor networks into a smart home is a promising approach to improve the safety and acceptability of elderly care services in daily life. Among them, the energy consumption and task planning of MHRS determine nursing safety, which is particularly important in the real nursing process. Therefore, we established a novel smart home for elderly care based on seven heterogeneous nursing robots, and proposed a multi-robot task allocation (MRTA) algorithm, considering execution time and energy consumption. The whole system efficiency makes up for the functional limitations and service continuity of traditional MHRS. To realize efficiently conducted multitasks, we established an architecture with centralized task allocation center, robot alliance layer and distributed execution layer for the MHRS. The self-organizing architecture contributes to overall task allocation, communication and adaptive cooperative control between different robots. Then, to clearly describe the continuous nursing process with multiple simultaneous demands and emergency tasks, we modeled the whole nursing process with continuity, multi-priority, and interpretability. A novel MRTA algorithm with a dynamic bidding mechanism was proposed. Comprehensive experiments showed that the proposed algorithm could effectively solve the three key problems of multi-priority tasks, multi-robot safe and adaptive cooperation, and emergency task call in the scene of elderly care. The proposed architecture regarding the smart home could be applied in nursing centers, hospitals, and other places for elderly care.

Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms

The paper proposes the formulation of a single-task robot (ST), single-robot task (SR), time-extended assignment (TA), multi-robot task allocation (MRTA) problem with multiple, nonlinear criteria using discrete variables that drastically reduce the computation burden. Obtaining an allocation is addressed by a Branch and Bound (B&B) algorithm in low scale problems and by a genetic algorithm (GA) specifically developed for the proposed formulation in larger scale problems. The GA crossover and mutation strategies design ensure that the descendant allocations of each generation will maintain a certain level of feasibility, reducing greatly the range of possible descendants, and accelerating their convergence to a sub-optimal allocation. The proposed MRTA algorithms are simulated and analyzed in the context of a thermosolar power plant, for which the spatially distributed Direct Normal Irradiance (DNI) is estimated using a heterogeneous fleet composed of both aerial and ground unmanned vehicles. Three optimization criteria are simultaneously considered: distance traveled, time required to complete the task and energetic feasibility. Even though this paper uses a thermosolar power plant as a case study, the proposed algorithms can be applied to any MRTA problem that uses a multi-criteria and nonlinear cost function in an equivalent way. The performance and response of the proposed algorithms are compared for four different scenarios. The results show that the B&B algorithm can find the global optimal solution in a reasonable time for a case with four robots and six tasks. For larger problems, the genetic algorithm approaches the global optimal solution in much less computation time. Moreover, the trade-off between computation time and accuracy can be easily carried out by tuning the parameters of the genetic algorithm according to the available computational power.

A multi-robot task allocation algorithm based on universal gravity rules

A multi-robot task allocation algorithm based on universal gravity rules

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.

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.

A Resource-Oriented, Decentralized Auction Algorithm for Multirobot Task Allocation

This paper proposes a resource-oriented, decentralized auction algorithm (RODAA) for multirobot task allocation considering multiple resources of the robots and limited robot communication range. The resources that this paper focuses on are the expendable supplies that a robot consumes and recharges while performing tasks, such as energy. In the proposed algorithm, each robot generates its cost for the task in a probabilistic manner considering multiple paths that visit none or different combinations of refill stations for performing the task based on the robot's residual resources. For robust and time-efficient task allocation with limited robot communication range in a dynamic network, a multihop-based auction algorithm is proposed. This paper also introduces a solar panel cleaning mission as a new application for multirobot systems and the proposed algorithm is implemented in the simulation of the mission. The simulation results demonstrate that the proposed algorithm is capable of completing the panel cleaning mission faster than other auction-based task allocation algorithms and has lower overall resource consumption.

A Spatial Queuing-Based Algorithm for Multi-Robot Task Allocation

Multi-robot task allocation (MRTA) is an important area of research in autonomous multi-robot systems. The main problem in MRTA is to allocate a set of tasks to a set of robots so that the tasks can be completed by the robots while ensuring that a certain metric, such as the time required to complete all tasks, or the distance traveled, or the energy expended by the robots is reduced. We consider a scenario where tasks can appear dynamically and a task needs to be performed by multiple robots to be completed. We propose a new algorithm called SQ-MRTA (Spatial Queueing-MRTA) that uses a spatial queue-based model to allocate tasks between robots in a distributed manner. We have implemented the SQ-MRTA algorithm on accurately simulated models of Corobot robots within the Webots simulator for different numbers of robots and tasks and compared its performance with other state-of-the-art MRTA algorithms. Our results show that the SQ-MRTA algorithm is able to scale up with the number of tasks and robots in the environment, and it either outperforms or performs comparably with respect to other distributed MRTA algorithms.

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.

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 Novel Multi-robot Task Allocation Algorithm under Heterogeneous Capabilities Condition

A Novel Multi-robot Task Allocation Algorithm under Heterogeneous Capabilities Condition

Multi-robot task allocation through vacancy chain scheduling

Modeling the effects of robot interaction in multi-robot systems, i.e., the group dynamics, is difficult due to the complexity of such interactions. This article formalizes the concept of group dynamics in the framework of scheduling and presents a proof that multi-robot task allocation (MRTA), in systems with significant performance effects from group dynamics, is an N P -complete problem. As a way of dealing with this complexity we have developed vacancy chain scheduling (VCS), a new formal model of MRTA inspired by a resource distribution process commonly found in nature. VCS is also the foundation of a new MRTA algorithm which relies on optimal allocation patterns to emerge from the stigmergic effects of robot interactions. We present experimental evidence of the validity of the VCS model from high-fidelity simulations. The experimental results validate the VCS model by reliably producing the predicted allocation patterns in both homogeneous and heterogeneous groups of robots. The evidence also supports our claim that VCS is a feasible solution for a restricted class of MRTA problems.