Many systems or applications have been developed for distributed environments with the goal of attaining multiple objectives in the face of environmental challenges such as high dynamics/hostility or severe resource constraints (e.g., energy or communications bandwidth). Often the multiple objectives are conflicting with each other, requiring optimal tradeoff analyses between the objectives. This paper is mainly concerned with how to model multiple objectives of a system and how to optimize their performance. We first conduct a comprehensive survey of the state-of-the-art modeling and solution techniques to solve multi-objective optimization problems. In addition, we discuss pros and cons of each modeling and optimization technique for in-depth understanding. Further, we classify existing approaches based on the types of objectives and investigate main problem domains, critical tradeoffs, and key techniques used in each class. We discuss the overall trends of the existing techniques in terms of application domains, objectives, and techniques. Further, we discuss challenging issues based on the inherent nature of multi-objective optimization problems. Finally, we suggest future work directions in terms of what critical design factors should be considered to design and analyze a system with multiple objectives.

A Survey on Modeling and Optimizing Multi-Objective Systems

Solving task allocation problem in multi Unmanned Aerial Vehicles systems using Swarm intelligence

This paper considers the problem of maximizing the number of task allocations in a distributed multirobot system under strict time constraints, where other optimization objectives need also be considered. It builds upon existing distributed task allocation algorithms, extending them with a novel method for maximizing the number of task assignments. The fundamental idea is that a task assignment to a robot has a high cost if its reassignment to another robot creates a feasible time slot for unallocated tasks. Multiple reassignments among networked robots may be required to create a feasible time slot and an upper limit to this number of reassignments can be adjusted according to performance requirements. A simulated rescue scenario with task deadlines and fuel limits is used to demonstrate the performance of the proposed method compared with existing methods, the consensus-based bundle algorithm and the performance impact (PI) algorithm. Starting from existing (PI-generated) solutions, results show up to a 20% increase in task allocations using the proposed method.

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Distributed Task Rescheduling With Time Constraints for the Optimization of Total Task Allocations in a Multirobot System

This letter presents a new Multi-Objective Particle Swarm Optimization (MOPSO) approach to a Cooperative Multi-Robot Task Allocation (CMRTA) problem, where the robots have to minimize the total team cost and, additionally, balance their workloads. We formulate the CMRTA problem as a more complex variant of multiple Travelling Salesman Problems (mTSP) and, in particular, address how to minimize the total travel distance of the entire robot team, as well as how to minimize the highest travel distance of an individual robot. The proposed approach extends the standard single-objective Particle Swarm Optimization (PSO) to cope with the multiple objectives, and its novel feature lies in a Pareto front refinement strategy and a probability-based leader selection strategy. To validate the proposed approach, we first use three benchmark functions to evaluate the performance of finding the true Pareto fronts in comparison with four existing well-known algorithms in continuous spaces. Afterwards, we use six datasets to investigate the task allocation mechanisms in dealing with the CMRTA problem in discrete spaces.

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Particle Swarm Optimization for Cooperative Multi-Robot Task Allocation: A Multi-Objective Approach

For a team of heterogeneous robots executing multiple tasks, we propose a novel algorithm to optimally allocate tasks to robots while accounting for their different capabilities. Motivated by the need that robot teams have in many real-world applications of remaining operational for long periods of time, we allow each robot to choose tasks taking into account the energy consumed by executing them, besides the global specifications on the task allocation. The tasks are encoded as constraints in an energy minimization problem solved at each point in time by each robot. The prioritization of a task over others—effectively signifying the allocation of the task to that particular robot—occurs via the introduction of slack variables in the task constraints. Moreover, the suitabilities of certain robots towards certain tasks are also taken into account to generate a task allocation algorithm for a team of robots with heterogeneous capabilities. The efficacy of the developed approach is demonstrated both in simulation and on a team of real robots.

An Optimal Task Allocation Strategy for Heterogeneous Multi-Robot Systems

Multi-objective optimization for dynamic task allocation in a multi-robot system

In this paper, we examine the use of ensemble methods in a multirobot task allocation environment. The aim is to enable a robot that needs to estimate the required resources to complete a task, to utilize information coming from other robots of the same type. To our knowledge, it is the first attempt made, to use such methods, to combine data of the same type, coming from data sets of different agents, to form a prediction. Knowledge exchange is not continuous, but only ad hoc. To merge data, we use ensemble models. This keeps communication needs to a minimum, as only the models themselves—and no actual data— need to be exchanged. To further reduce communication costs, the number of robots that contribute information is being limited. Finally, we make an attempt to see how well the concept we use would perform in other domains. This is to examine whether the approach could yield the same results in other domains, or it is limited to the task allocation problem, as formulated in Tolmidis and Petrou (Eng. Appl. Artif. Intell., 2013;26(5–6):1458–1468) . For this, we selected two additional, different, publicly available data sets.

Avraam Th. Tolmidis

Papers

Multi-objective optimization for dynamic task allocation in a multi-robot system

Ensemble Methods for Cooperative Robotic Learning