Multi-Robot Multi-Station Cooperative Spot Welding Task Allocation Based on Stepwise Optimization: An Industrial Case Study

Multi-robot systems comprising of heterogeneous autonomous vehicles on land, air, water are being increasingly used to assist or replace humans in different hazardous missions. Two crucial aspects in such multi-robot systems are to: a) explore an initially unknown region of interest to discover tasks, and, b) allocate and share the discovered tasks between the robots in a coordinated manner using a multi-robot task allocation (MRTA) algorithm. In this paper, we describe results from our research on multi-robot terrain coverage and MRTA algorithms within an autonomous landmine detection scenario, done as part of the COMRADES project. Each robot is equipped with a different type of landmine detection sensor and different sensors, even of the same type, can have different degrees of accuracy. The landmine detection-related operations performed by each robot are abstracted as tasks and multiple robots are required to complete a single task. First, we describe a distributed and robust terrain coverage algorithm that employs Voronoi partitions to divide the area of interest among the robots and then uses a single-robot coverage algorithm to explore each partition for potential landmines. Then, we describe MRTA algorithms that use the location information of discovered potential landmines and employ either a greedy strategy, or, an opportunistic strategy to allocate tasks among the robots while attempting to minimize the time (energy) expended by the robots to perform the tasks. We report experimental results of our algorithms using accurately-simulated Corobot robots within the Webots simulator performing a multi-robot, landmine detection operation.

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.

In this paper, we consider the task allocation problem for autonomous agents where the distances between any two agents should always be less than a predetermined value. To easily tackle the problem, we introduce a novel concept referred to as a virtual task that helps to decouple the task allocation from the path planning in the joint task allocation and path planning problem. An overall task allocation procedure with two steps is proposed. After the virtual tasks are generated with preliminary clustering in the first step, tasks are clustered into groups and allocated to agents in the second step. To reduce the computational complexity of optimal clustering and task allocation, we propose a suboptimal method called the cluster-level traveling salesman problem (TSP). Numerical results show that the cluster-level TSP suffers only little performance degradation compared to the optimal method, even though its computational costs are dramatically reduced.

Task allocation is one of the research focuses of multi-robot system. On the base of presenting the utility values matrix of robots relative to tasks and analyzing the characteristics of multi-robot task allocation, we build the multi-robot task allocation model based on robotic utility value. In order to prevent the basic particle swarm optimization (PSO) algorithm from converging on local optimum, this paper proposes a modified particle swarm optimization (MPSO) algorithm by introducing the linear decrease mechanism of inertia weight and the concept of adjustment operator and adjustment sequence. With the evolution of velocity in the MPSO algorithm, particle not only studies from the historical optimum individual of itself and population, but also studies from the other stochastic individuals with some probability. Finally, the MPSO algorithm is used to solve the task allocation problem of RoboCup 2D soccer robot system, the efficiency of this modified algorithm is proved through simulation results.

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.

Many application domains require search and retrieval, which is also known in the robotic domain as foraging. For example, in a search and rescue domain, a disaster area needs to be explored and transportation of survivors to a safe area needs to be arranged. Performing such a search and retrieval task by more than one robot increases performance if they are able to distribute their workload efficiently and evenly. In this work, we study the Multi-Robot Task Allocation (MRTA) problem in the search and retrieval domain, where a team of robots is required to cooperatively search for targets of interest in an environment and also retrieve them back to a home base. In comparison with typical foraging tasks, we look at a more general search and retrieval task in which the targets are distinguished with various types, and task allocation also requires taking into account temporal constraints on the team goal. As usual, robots have no prior knowledge about the location of targets in the environment but in addition they need to deliver targets to the home base in a specific order according to their types, which significantly increases the complexity of a foraging problem. We first use a graph-based model to analyse the search and retrieval problem and the dynamics of exploration and retrieval within a cooperative team. We then proceed to present an extended auction-based approach, as well as a prediction approach. The essential difference between these two approaches is that the task allocation and execution procedures in the auction approach are running in parallel, whereas a robot in the prediction approach only needs to choose a task to perform when it has no thing to do. The auction approach uses a winner determination mechanism to allocate tasks to each robot, whereas the robots in the prediction approach implicitly coordinate their activities by team reasoning that leads to consensuses about task allocation. We use the Blocks World for Teams (BW4T) simulator to evaluate the two approaches in our experimental study.

Multirobot systems (MRS) hold the promise of improved performance and increased fault tolerance for large-scale problems. One of the most important aspects in the design of MRS is the allocation of tasks among the robots in a productive and efficient manner. Optimal solutions to multirobot task allocation (MRTA) can be found through an exhaustive search. Since there are ways in which m tasks can be assigned to n robots, an exhaustive search is often not possible. Task allocation methodologies must ensure that not only the global mission is achieved, but also the tasks are well distributed among the robots. This paper presents task allocation methodologies for MRS by considering their capability in terms of time and space. A two-phase solution methodology is used to solve the MRTA problem wherein the task capacity of the robots is determined during the first phase and the task allocation optimization is done during the second phase using linear programming (LP).

In order to minimize the processing time for multiple robots to finish lots of tasks, an optimized multiple robots task allocation and path planning approach is proposed. In this approach, the optimized task sequence is first schemed on the assumption that all the tasks are processed by one robot. While scheming the task sequence. A star algorithm is employed to plan paths and calculate the path cost. Then, according to the number of the robots, the task sequence has been randomly divided into several task segments that will be assigned to robots. At last, the task number of each task segments are averaged according to the time each robot used, so that the optimized task allocation scheme is got. In this approach, not only multiple robot task allocation, but also the optimized path planning is realized to ensure that the robots can finish all the tasks in the shortest time. The experiments and simulation are provided to validate the effectiveness of the approach. The results show that the proposed approach can scheme optimized multiple robots task allocation and path planning scheme.

Evolutionary algorithms (EA) have been used to solve path planning of mobile robots successfully. However, accuracy and convergence are always topics. The cloud model which transforms qualitative concept into quantitative description, and vice versa, is adept in uncertainty modeling. It can be used in EA for evolving in a uniform and natural way. An EA embedded with cloud model (EACM) for path planning of mobile robots was proposed in this paper. The algorithm adopted floating-point coding and realized fast converging. Simulation results verified the efficiency of our algorithm.

With the advent of Industry 4.0, the seamless integration of industrial systems and unmanned technologies has significantly accelerated the development of smart industries. However, the research on task allocation for railway maintenance robots remains limited, particularly with respect to optimizing costs and efficiency within smart railway systems. To address this gap, the present study explores multi-robot task allocation for automated orbital bolt maintenance, aiming to enhance operational efficiency by minimizing both makespan and total travel distance for all robots. To achieve this, an improved hybrid algorithm combining NSGA-II and MOPSO is proposed. Initially, a dynamic task planning method, tailored to the specific conditions of railway operations, is developed. This method uses the coordinates of track bolts to extract environmental features, enabling the dynamic partitioning of task areas. Subsequently, a multi-elite archive strategy is introduced, along with an adaptive mechanism for adjusting crossover and mutation probabilities. This ensures the preservation and maintenance of multiple solutions across various Pareto fronts, effectively mitigating the premature convergence commonly observed in traditional NSGA-II algorithms. Moreover, the integration of the MOPSO algorithm strikes a balance between local and global search capabilities, thereby enhancing both optimization efficiency and solution quality. Finally, a series of experiments, conducted with varying task sizes and robot quantities during the railway maintenance window, validate the effectiveness and improved performance of the proposed algorithm in addressing the multi-robot task allocation problem.

Multirobot task allocation is a hot issue in the field of robot research. A new emotional model is used with the self-interested robot, which gives a new way to measure self-interested robots' individual cooperative willingness in the problem of multirobot task allocation. Emotional cooperation factor is introduced into self-interested robot; it is updated based on emotional attenuation and external stimuli. Then a multirobot pursuit task allocation algorithm is proposed, which is based on emotional cooperation factor. Combined with the two-step auction algorithm recruiting team leaders and team collaborators, set up pursuit teams, and finally use certain strategies to complete the pursuit task. In order to verify the effectiveness of this algorithm, some comparing experiments have been done with the instantaneous greedy optimal auction algorithm; the results of experiments show that the total pursuit time and total team revenue can be optimized by using this algorithm.

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.

Robotic Mobile Fulfillment System (RMFS) is a new type of parts-to-picker order picking system and has become the development trend of e-commerce logistics distribution centers. There are usually a large number of tasks need to be allocated to many robots and the picking time for e-commerce orders is usually very tight, which puts forward higher requirements for the efficiency of multirobot task allocation (MRTA) in e-commerce RMFS. Current researches on MRTA in RMFS seldom consider task correlation and the balance among picking stations. In this paper, a task time cost model considering task correlation is built according to the characteristics of the picking process. Then, a multirobot task allocation model minimizing the overall picking time is established considering both the picking time balance of picking stations and the load balance of robots. Finally, a four-stage balanced heuristic auction algorithm is designed to solve the task allocation model and the tasks with execution sequence for each robot are obtained. By comparing with the traditional task time cost model and the algorithm without considering the balance among picking stations, it is found that the proposed model and algorithm can significantly shorten the overall picking time.

This paper presents a Multi-Robot Task Allocation (MRTA) system, implemented on a RoboCup Small Size League team, where robots participate of auctions for the available roles, such as attacker or defender, and use Heuristically Accelerated Reinforcement Learning to evaluate their aptitude to perform these roles, given the situation of the team, in real-time. The performance of the task allocation mechanism is evaluated and compared in different implementation variants, and results show that the proposed MRTA system significantly increases the team performance, when compared to pre-programmed team behavior algorithms.

When planning the soccer robot path at present, a two-dimensional map is used mainly to optimize the path of the soccer robot's operating field. However, because the two-dimensional robot is selected, which can only utilize the data of plane about a specific environment, the data information of the mobile soccer robot cannot be gathered, impacting greatly the completion of the plan of the robot path. The path planning of the soccer robot is conducted using the quantum genetic algorithm so that the problem can be dealt with. On the premise that there is a lack of full consideration of the accurate motion path between the two points, the inertia weight is dynamically adjusted to overcome the disadvantage of premature convergence of the traditional quantum group algorithm, which can make the weight of the quantum genetic algorithm controllable with adaptability so that the problem of the extremely slow convergence speed of a single quantum in the quantum group and the high group dispersion can be solved. Furthermore, the selected quantum genetic algorithm can realize the real-time update of the soccer robot's position, effectively increase the trajectory change of the particle swarm movement, and to a certain extent effectively increase the search ability and convergence effect of the particle swarm in the global scope, so as to ensure that this algorithm can be more effective than the traditional path planning algorithm in terms of global search ability and convergence speed in the path planning of the soccer robot and has a higher use value.