Task oriented area partitioning and allocation for optimal operation of multiple industrial robots in unstructured environments

© 2014 IEEE. When multiple industrial robots are deployed in field applications such as grit blasting and spray painting of steel bridges, the environments are unstructured for robot operation and the robot positions may not be arranged accurately. Coordination of these multiple robots to maximize productivity through area partitioning and allocation is crucial. This paper presents a novel approach to area partitioning and allocation by utilizing multiobjective optimization and voronoi partitioning. Multiobjective optimization is used to minimize: (1) completion time, (2) proximity of the allocated area to the robot, and (3) the torque experienced by each joint of the robot during task execution. Seed points of the voronoi graph for voronoi partitioning are designed to be the design variables of the multiobjective optimization algorithm. Results of three different simulation scenarios are presented to demonstrate the effectiveness of the proposed approach and the advantage of incorporating robots' torque capacity

A New Method for Improving the Fairness of Multi-Robot Task Allocation by Balancing the Distribution of Tasks

This paper presents an innovative task allocation method for multi-robot systems that aims to optimize task distribution while taking into account various performance metrics such as efficiency, speed, and cost. Contrary to conventional approaches, the proposed method takes a comprehensive approach to initialization by integrating the K-means clustering algorithm, the Hungarian method for solving the assignment problem, and a genetic algorithm specifically adapted for Open Loop Travel Sales Man Problem (OLTSP). This synergistic combination allows for a more robust initialization, effectively grouping similar tasks and robots, and laying a strong foundation for the subsequent optimization process. The suggested method is flexible enough to handle a variety of situations, including Multi-Robot System (MRS) with robots that have unique capabilities and tasks of varying difficulty. The method provides a more adaptable and flexible solution than traditional algorithms, which might not be able to adequately address these variations because of the heterogeneity of the robots and the complexity of the tasks. Additionally, ensuring optimal task allocation is a key component of the suggested method. The method efficiently determines the best task assignments for robots through the use of a systematic optimization approach, thereby reducing the overall cost and time needed to complete all tasks. This contrasts with some existing methods that might not ensure optimality or might have limitations in their ability to handle a variety of scenarios. Extensive simulation experiments and numerical evaluations are carried out to validate the method's efficiency. The extensive validation process verifies the suggested approach's dependability and efficiency, giving confidence in its practical applicability

Decentralised Coordination in RoboCup Rescue

Emergency responders are faced with a number of significant challenges when managing major disasters. First, the number of rescue tasks posed is usually larger than the number of responders (or agents) and the resources available to them. Second, each task is likely to require a different level of effort in order to be completed by its deadline. Third, new tasks may continually appear or disappear from the environment, thus requiring the responders to quickly recompute their allocation of resources. Fourth, forming teams or coalitions of multiple agents from different agencies is vital since no single agency will have all the resources needed to save victims, unblock roads, and extinguish the ?res which might erupt in the disaster space. Given this, coalitions have to be efficiently selected and scheduled to work across the disaster space so as to maximise the number of lives and the portion of the infrastructure saved. In particular, it is important that the selection of such coalitions should be performed in a decentralised fashion in order to avoid a single point of failure in the system. Moreover, it is critical that responders communicate only locally given they are likely to have limited battery power or minimal access to long range communication devices. Against this background, we provide a novel decentralised solution to the coalition formation process that pervades disaster management. More specifically, we model the emergency management scenario defined in the RoboCup Rescue disaster simulation platform as a Coalition Formation with Spatial and Temporal constraints (CFST) problem where agents form coalitions in order to complete tasks, each with different demands. In order to design a decentralised algorithm for CFST we formulate it as a Distributed Constraint Optimisation problem and show how to solve it using the state-of-the-art Max-Sum algorithm that provides a completely decentralised message-passing solution. We then provide a novel algorithm (F-Max-Sum) that avoids sending redundant messages and efficiently adapts to changes in the environment. In empirical evaluations, our algorithm is shown to generate better solutions than other decentralised algorithms used for this problem

Self-organising agent communities for autonomic resource management

The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes

Task Planning on Stochastic Aisle Graphs for Precision Agriculture

This work addresses task planning under uncertainty for precision agriculture applications whereby task costs are uncertain and the gain of completing a task is proportional to resource consumption (such as water consumption in precision irrigation). The goal is to complete all tasks while prioritizing those that are more urgent, and subject to diverse budget thresholds and stochastic costs for tasks. To describe agriculture-related environments that incorporate stochastic costs to complete tasks, a new Stochastic-Vertex-Cost Aisle Graph (SAG) is introduced. Then, a task allocation algorithm, termed Next-Best-Action Planning (NBA-P), is proposed. NBA-P utilizes the underlying structure enabled by SAG, and tackles the task planning problem by simultaneously determining the optimal tasks to perform and an optimal time to exit (i.e. return to a base station), at run-time. The proposed approach is tested with both simulated data and real-world experimental datasets collected in a commercial vineyard, in both single- and multi-robot scenarios. In all cases, NBA-P outperforms other evaluated methods in terms of return per visited vertex, wasted resources resulting from aborted tasks (i.e. when a budget threshold is exceeded), and total visited vertices.Comment: To appear in Robotics and Automation Letter