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

Abstract

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.

A review of cooperative path planning of an unmanned aerial vehicle group

“…The objective is to find the appropriate allocation mappings for global reward value maximization. Jang et al (2019) proposed an integrated decision-making framework of a heterogeneous UAV group to execute cooperative tasks in a 2D dynamic environment. The goal was to find an assignment set that can maximize the minimum value of the requirement satisfaction index of tasks.…”

“…As the literature review reflects, only a few studies considered the CPP of heterogeneous UAVs (Maza and Ollero, 2007;Chen J et al, 2013;Liu W et al, 2013;Balampanis et al, 2017;Quintin et al, 2017;Zhen et al, 2018;Cho et al, 2019;Jang et al, 2019;Yan et al, 2019a). Due to the differences in performance, functions, and roles of heterogeneous UAVs, their cooperation generally involves more problems, such as coalition formation and role assignment (Jang et al, 2019), which are closely related to their cooperative path planning. Also, UAVs may cooperate with other categories of vehicles, such as ground vehicles and underwater vehicles, to achieve multi-domain coordination in complex tasks (Sujit et al, 2009;Quintin et al, 2017;Ding et al, 2019b).…”

PDDL Planning with Natural Language-Based Scene Understanding for UAV-UGV Cooperation

“…Wurm et al [21] integrated a temporal planning approach with a PDDL planner for heterogeneous teams of robots. Jang et al [22] solved the decision-making issues of aerial robots using an integrated decision-making framework. Kingry et al [23] represented the environment in a scalar field and created a time-optimized mission plan for UGVs using a cascaded heuristic optimization algorithm.…”

Omnipotent Virtual Giant for Remote Human-Swarm Interaction

“…path planning, collision avoidance, low-level control, etc.) [20], remote inspection [21], manipulation [22], and inter-agent communication. They behave autonomously based on their local information and interaction with their neighbouring robots.…”