Abstract
Interest is growing in the use of autonomous swarms of drones in various mission-physical applications such as surveillance, intelligent monitoring, and rescue operations. Swarm systems should fulfill safety and efficiency constraints in order to guarantee dependable operations. To maximize motion safety, we should design the swarm system in such a way that drones do not collide with each other and/or other objects in the operating environment. On other hand, to ensure that the drones have sufficient resources to complete the required task reliably, we should also achieve efficiency while implementing the mission, by minimizing the travelling distance of the drones. In this paper, we propose a novel integrated approach that maximizes motion safety and efficiency while planning and controlling the operation of the swarm of drones. To achieve this goal, we propose a novel parallel evolutionary-based swarm mission planning algorithm. The evolutionary computing allows us to plan and optimize the routes of the drones at the run-time to maximize safety while minimizing travelling distance as the efficiency objective. In order to fulfill the defined constraints efficiently, our solution promotes a holistic approach that considers the whole design process from the definition of formal requirements through the software development. The results of benchmarking demonstrate that our approach improves the route efficiency by up to 10% route efficiency without any crashes in controlling swarms compared to state-of-the-art solutions.
Highlights
A swarm of drones is a group of autonomously functioning drones providing some services in a coordinated manner
We start by explicitly defining the conditions that should be verified to ensure motion safety of a swarm, which are that swarms do collide with static objects, with each other and/or with the objects that dynamically appear in the fly zone of the swarm
The Colonies are divided between the Imperialists and the overall search space is divided into empires
Summary
A swarm of drones is a group of autonomously functioning drones providing some services in a coordinated manner. We should address the problem of ensuring motion safety, i.e., the ability of a system to avoid collisions, while designing autonomous swarm of drones. We propose a novel approach to ensuring motion safety of swarms of drones. Evolutionary computing comprises a set of optimization algorithms, which are inspired by a biological or societal evolution [27]. An example of the former is Imperialist Competitive Algorithm (ICA) [4]. ICA starts by a random generation of a set of countries—the chromosomes (an encoding of the possible solutions)—in the search space of the optimization problem. An association of a Colony with an Imperialist means that only the chromosomes of the Imperialist and its associated colonies will be used to crossover
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