Application of self-organizing techniques for the distribution of heterogeneous multi-tasks in multi-robot systems

This paper focuses on the general problem of coordinating of multi-robot systems, more specifically, it addresses the self-election of heterogeneous and specialized tasks by autonomous robots. In this regard, it has proposed experimenting with two different techniques based chiefly on selforganization and emergence biologically inspired, by applying response threshold models as well as ant colony optimization. Under this approach it can speak of multi-tasks selection instead of multi-tasks allocation, that means, as the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. It has evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results

A Survey and Analysis of Cooperative Multi-Agent Robot Systems: Challenges and Directions

Research in the area of cooperative multi-agent robot systems has received wide attention among researchers in recent years. The main concern is to find the effective coordination among autonomous agents to perform the task in order to achieve a high quality of overall performance. Therefore, this paper reviewed various selected literatures primarily from recent conference proceedings and journals related to cooperation and coordination of multi-agent robot systems (MARS). The problems, issues, and directions of MARS research have been investigated in the literature reviews. Three main elements of MARS which are the type of agents, control architectures, and communications were discussed thoroughly in the beginning of this paper. A series of problems together with the issues were analyzed and reviewed, which included centralized and decentralized control, consensus, containment, formation, task allocation, intelligences, optimization and communications of multi-agent robots. Since the research in the field of multi-agent robot research is expanding, some issues and future challenges in MARS are recalled, discussed and clarified with future directions. Finally, the paper is concluded with some recommendations with respect to multi-agent systems

Research Trends and Outlooks in Assembly Line Balancing Problems

This paper presents the findings from the survey of articles published on the assembly line balancing problems (ALBPs) during 2014-2018. Before proceeding a comprehensive literature review, the ineffectiveness of the previous ALBP classification structures is discussed and a new classification scheme based on the layout configurations of assembly lines is subsequently proposed. The research trend in each layout of assembly lines is highlighted through the graphical presentations. The challenges in the ALBPs are also pinpointed as a technical guideline for future research works

Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team

This work addresses the coordination problem of multiple robots with the goal of finding specific hazardous targets in an unknown area and dealing with them cooperatively. The desired behaviour for the robotic system entails multiple requirements, which may also be conflicting. The paper presents the problem as a constrained bi-objective optimization problem in which mobile robots must perform two specific tasks of exploration and at same time cooperation and coordination for disarming the hazardous targets. These objectives are opposed goals, in which one may be favored, but only at the expense of the other. Therefore, a good trade-off must be found. For this purpose, a nature-inspired approach and an analytical mathematical model to solve this problem considering a single equivalent weighted objective function are presented. The results of proposed coordination model, simulated in a two dimensional terrain, are showed in order to assess the behaviour of the proposed solution to tackle this problem. We have analyzed the performance of the approach and the influence of the weights of the objective function under different conditions: static and dynamic. In this latter situation, the robots may fail under the stringent limited budget of energy or for hazardous events. The paper concludes with a critical discussion of the experimental results

Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-selection of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested in a decentralized solution where the robots themselves autonomously and in an individual manner, are responsible for selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-task distribution problem and we propose a solution using two different approaches by applying Response Threshold Models as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results

A Decentralized Strategy for Swarm Robots to Manage Spatially Distributed Tasks

Large-scale scenarios such as search-and-rescue operations, agriculture, warehouse, surveillance, and construction consist of multiple tasks to be performed at the same time. These tasks have non-trivial spatial distributions. Robot swarms are envisioned to be efficient, robust, and flexible for such applications. We model this system such that each robot can service a single task at a time; each task requires a specific number of robots, which we refer to as \u27quota\u27; task allocation is instantaneous; and tasks do not have inter- dependencies. This work focuses on distributing robots to spatially distributed tasks of known quotas in an efficient manner. Centralized solutions which guarantee optimality in terms of distance travelled by the swarm exist. Although potentially scalable, they require non-trivial coordination; could be computationally expensive; and may have poor response time when the number of robots, tasks and task quotas increase. For a swarm to efficiently complete tasks with a short response time, a decentralized approach provides better parallelism and scalability than a centralized one. In this work, we study the performance of a weight-based approach which is enhanced to include spatial aspects. In our approach, the robots share a common table that reports the task locations and quotas. Each robot, according to its relative position with respect to task locations, modifies weights for each task and randomly chooses a task to serve. Weights increase for tasks that are closer and have high quota as opposed to tasks which are far away and have low quota. Tasks with higher weights have a higher probability of being selected. This results in each robot having its own set of weights for all tasks. We introduce a distance- bias parameter, which determines how sensitive the system is to relative robot-task locations over task quotas. We focus on evaluating the distance covered by the swarm, number of inter- task switches, and time required to completely allocate all tasks and study the performance of our approach in several sets of simulated experiments

Modified Q-Learning Algorithm for Mobile Robot Path Planning Variation using Motivation Model

Path planning is an essential algorithm in autonomous mobile robots, including agricultural robots, to find the shortest path and to avoid collisions with obstacles. Q-Learning algorithm is one of the reinforcement learning methods used for path planning. However, for multi-robot system, this algorithm tends to produce the same path for each robot. This research modifies the Q-Learning algorithm in order to produce path variations by utilizing the motivation model, i.e. achievement motivation, in which different motivation parameters will result in different optimum paths. The Motivated Q-Learning (MQL) algorithm proposed in this study was simulated in an area with three scenarios, i.e. without obstacles, uniform obstacles, and random obstacles. The results showed that, in the determined scenario, the MQL can produce 2 to 4 variations of optimum path without any potential of collisions (Jaccard similarity = 0%), in contrast to the Q-Learning algorithm that can only produce one optimum path variation. This result indicates that MQL can solve multi-robots path planning problems, especially when the number of robots is large, by reducing the possibility of collisions as well as decreasing the problem of queues. However, the average computational time of the MQL is slightly longer than that of the Q-Learning