Effective task allocation frameworks for large-scale multiple agent systems.

This research aims to develop innovative and transformative decision-making frameworks that enable a large-scale multi-robot system, called robotic swarm, to autonomously address multi-robot task allocation problem: given a set of complicated tasks, requiring cooperation, how to partition themselves into subgroups (or called coalitions) and assign the subgroups to each task while maximising the system performance. The frameworks should be executable based on local information in a decentralised manner, operable for a wide range of the system size (i.e., scalable), predictable in terms of collective behaviours, adaptable to dynamic environments, operable asynchronously, and preferably able to accommodate heterogeneous agents. Firstly, for homogeneous robots, this thesis proposes two frameworks based on biological inspiration and game theories, respectively. The former, called LICA-MC (Markov-Chan-based approach under Local Information Consistency Assumption), is inspired by fish in nature: despite insufficient awareness of the entire group, they are well-coordinated by sensing social distances from neighbours. Analogously, each agent in the framework relies only on local information and requires its local consistency over neighbouring agents to adaptively generate the stochastic policy. This feature offers various advantages such as less inter-agent communication, a shorter timescale for using new information, and the potential to accommodate asynchronous behaviours of agents. We prove that the agents can converge to a desired collective status without resorting to any global information, while maintaining scalability, flexibility, and long-term system efficiency. Numerical experiments show that the framework is robust in a realistic environment where information sharing over agents is partially and temporarily disconnected. Furthermore, we explicitly present the design requirements to have all these advantages, and implementation examples concerning travelling costs minimisation, over-congestion avoidance, and quorum models, respectively. The game-theoretical framework, called GRAPE (GRoup Agent Partitioning and placing Event), regards each robot as a self-interested player attempting to join the most preferred coalition according to its individual preferences regarding the size of each coalition. We prove that selfish agents who have social inhibition can always converge to a Nash stable partition (i.e., a social agreement) within polynomial time under the proposed framework. The framework is executable based on local interactions with neighbour agents under a strongly-connected communication network and even in asynchronous environments. This study analyses an outcome’s minimum-guaranteed suboptimality, and additionally shows that at least 50% is guaranteed if social utilities are non-decreasing functions with respect to the number of co-working agents. Numerical experiments confirm that the framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation where some of the agents temporarily halt operation during a mission. The two proposed frameworks are compared in the domain of division of labour. Empirical results show that LICA-MC provides excellent scalability with respect to the number of agents, whereas GRAPE has polynomial complexity but is more efficient in terms of convergence time (especially when accommodating a moderate number of robots) and total travelling costs. It also turns out that GRAPE is sensitive to traffic congestion, meanwhile LICA-MC suffers from slower robot speed. We discuss other implicit advantages of the frameworks such as mission suitability and additionally-builtin decision-making functions. Importantly, it is found that GRAPE has the potential to accommodate heterogeneous agents to some extent, which is not the case for LICA-MC. Accordingly, this study attempts to extend GRAPE to incorporate the heterogeneity of agents. Particularly, we consider the case where each task has its minimum workload requirement to be fulfilled by multiple agents and the agents have different work capacities and costs depending on the tasks. The objective is to find an assignment that minimises the total cost of assigned agents while satisfying the requirements. GRAPE cannot be directly used because of the heterogeneity, so we adopt tabu-learning heuristics where an agent penalises its previously chosen coalition whenever it changes decision: this variant is called T-GRAPE. We prove that, by doing so, a Nash stable partition is always guaranteed to be determined in a decentralised manner. Experi-mental results present the properties of the proposed approach regarding suboptimality and algorithmic complexity. Finally, the thesis addresses a more complex decision-making problem involving team formation, team-to-task assignment, agent-to-working-position selection, fair resource allocation concerning tasks’ minimum requirements for completion, and trajectory optimisation with collision avoidance. We propose an integrated framework that decouples the original problem into three subproblems (i.e., coalition formation, position allocation, and path planning) and deals with them sequentially by three respective modules. The coalition formation module based on T-GRAPE deals with a max-min problem, balancing the work resources of agents in proportion to the task’s requirements. We show that, given reasonable assumptions, the position allocation subproblem can be solved efficiently in terms of computational complexity. For the path planning, we utilise an MPC-SCP (Model Predictive Control and Sequential Convex Programming) approach that enables the agents to produce collision-free trajectories. As a proof of concept, we implement the framework into a cooperative stand-in jamming mission scenario using multiple UAVs. Numerical experiments suggest that the framework could be computationally feasible, fault-tolerant, and near-optimal. Comparison of the proposed frameworks for multi-robot task allocation is discussed in the last chapter regarding the desired features described at first (i.e., decentralisation, scalability, predictability, flexibility, asynchronisation, heterogeneity), along with future work and possible applications in other domains.PhD in Aerospac

Local information-based control for probabilistic swarm distribution guidance

This paper proposes a closed-loop decentralised framework for swarm distribution guidance, which disperses homogeneous agents over bins to achieve a desired density distribution by using feedback gains from the current swarm status. The key difference from existing works is that the proposed framework utilises only local information, not global information ,to generate the feedback gains for stochastic policies. Dependency on local information entails various advantages including reduced inter-agent communication, a shorter timescale for obtaining new information, asynchronous implementation, and deployability without a priori mission knowledge. Our theoretical analysis shows that, even utilising only local information, the proposed framework guarantees convergence of the agents to the desired status, while maintaining the advantages of existing closed-loop approaches. Also, the analysis explicitly provides the design requirements to achieve all the advantages of the proposed framework. We provide implementation examples and report the results of empirical tests. The test results confirm the effectiveness of the proposed framework and also validate the robustness enhancement in a scenario of partial disconnection of the communication network

Anonymous hedonic game for task allocation in a large-scale multiple agent system

This paper proposes a novel game-theoretical autonomous decision-making framework to address a task allocation problem for a swarm of multiple agents. We consider cooperation of self-interested agents, and show that our proposed decentralized algorithm guarantees convergence of agents with social inhibition to a Nash stable partition (i.e., social agreement) within polynomial time. The algorithm is simple and executable based on local interactions with neighbor agents under a strongly connected communication network and even in asynchronous environments. We analytically present a mathematical formulation for computing the lower bound of suboptimality of the outcome, and additionally show that at least 50% of suboptimality can be guaranteed if social utilities are nondecreasing functions with respect to the number of coworking agents. The results of numerical experiments confirm that the proposed framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation

Cooperative control for a flight array of UAVs and an application in radar jamming

This paper proposes a flight array system and an integrated approach to cope with its operational issues raised in mission-planning level (i.e., task allocation) and control level (i.e., control allocation). The proposed flight array system consists of multiple ducted-fan UAVs that can assemble with each other to fly together, as well as dissemble themselves to fly individually for accomplishing a given mission. To address the task allocation problem, a game-theoretical framework is developed. This framework enables agents to converge into an agreed task allocation in a decentralised and scalable manner, while guaranteeing a certain level of global optimality. In addition, this paper suggests a cooperative control scheme based on sliding mode control and weighted pseudo-inverse techniques so that the system’s non-linearity and control allocation issue are effectively handled. As a proof-of-concept, a prototype simulation program is developed and validated in a cooperative jamming mission. The numerical simulations manifest the feasibility of effectiveness of the proposed approach

An integrated decision-making framework of a heterogeneous aerial robotic swarm for cooperative tasks with minimum requirements

Given a cooperative mission consisting of multiple tasks spatially distributed, an aerial robotic swarm’s decision-making issues include team formation, team-to-task assignment, agent-to-work-position assignment and trajectory optimisation with collision avoidance. The problem becomes even more complicated when involving heterogeneous agents, tasks’ minimum requirements and fair allocation. This paper formulates all the combined issues as an optimisation problem and then proposes an integrated framework that addresses the problem in a decentralised fashion. We approximate and decouple the complex original problem into three subproblems (i.e. coalition formation, position allocation and path planning), which are sequentially addressed by three different proposed modules. The coalition formation module based on game theories deals with a max-min problem, the objective of which is to partition the agents into disjoint task-specific teams in a way that balances the agents’ work resources in proportion to the task’s minimum workload requirements. For agents assigned to the same task, given reasonable assumptions, the position allocation subproblem can be efficiently addressed in terms of computational complexity. For the trajectory optimisation, we utilise a Model Predictive Control and Sequential Convex Programming algorithm, which reduces the size of the problem so that the agents can generate collision-free trajectories on a real-time basis. As a proof of concept, we implement the framework into an unmanned aerial vehicle swarm’s cooperative stand-in jamming mission scenario and show its feasibility, fault tolerance and near-optimality based on numerical experiment

Game-theoretical approach to heterogeneous multi-robot task assignment problem with minimum workload requirements

This paper addresses a multi-robot task assignment problem with heterogeneous agents and tasks. Each task has a different type of minimum workload requirement to be accomplished by multiple agents, and the agents have different work capacities and costs depending on the tasks. The objective is to find an assignment that minimises the total cost of assigned agents while satisfying the requirements of the tasks. We formulate this problem as the minimisation version of the generalised assignment problem with minimum requirements (MinGAP-MR). We propose a distributed game-theoretical approach in which each selfish player (i.e., robot) wants to join a task-specific coalition that minimises its own cost as possible. We adopt tabu-learning heuristics where a player penalises its previously chosen coalition, and thereby a Nash-stable partition is always guaranteed to be determined. Experimental results present the properties of our proposed approach in terms of suboptimality and algorithmic complexity

Omnipotent Virtual Giant for Remote Human–Swarm Interaction

This paper proposes an intuitive human-swarm interaction framework inspired by our childhood memory in which we interacted with living ants by changing their positions and environments as if we were omnipotent relative to the ants. In virtual reality, analogously, we can be a super-powered virtual giant who can supervise a swarm of robots in a vast and remote environment by flying over or resizing the world, and coordinate them by picking and placing a robot or creating virtual walls. This work implements this idea by using Virtual Reality along with Leap Motion, which is then validated by proof-of-concept experiments using real and virtual mobile robots in mixed reality. We conduct a usability analysis to quantify the effectiveness of the overall system as well as the individual interfaces proposed in this work. The results reveal that the proposed method is intuitive and feasible for interaction with swarm robots, but may require appropriate training for the new end-user interface device