Motion Planning of UAV Swarm: Recent Challenges and Approaches

*Muhammad Mubashir Iqbal, Zain Anwar Ali, Rehan Khan and Muhammad Shafiq*

#### **Abstract**

The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work.

**Keywords:** UAV, swarm intelligence, motion planning, swarm challenges, flight, aerial mission

#### **1. Introduction**

UAV has significance in our lives due to their potential applications. Single UAVs are restricted to limited power, capabilities, sensing, and flight time. This has raised a requisite for employing swarms of UAV systems. UAV swarm conquers the exploitations and restrictions of an unaccompanied UAV and assists larger teams to cooperate for successful aerial missions. Swarm has benefits and brings versatile possibilities as the strength lies in numbers. Many of them are task completion in less time, redundancy, and collaborative task execution.

#### **1.1 Background**

Swarming is not a contemporary conception. It existed in nature and was motivated by the cooperation and mutual communication of biological populations [1]. Studying the flocking of birds, movement of the ant colony, cooperation of bees,

schools of fish, and predation of wolves the concept of the swarm of UAVs came into existence. The unity of the animal kingdom makes it possible to achieve a common challenging and complex goal.

Nevertheless, swarming is not restricted to a natural phenomenon. It is also inspired by a military tactic in which many units from multiple axes coverage attack a common target in a coordinated and deliberately structured form [2]. Since the fourth century, swarming has been observed throughout military history. However, today swarming has changed the traditional concepts of command and control into innovative ones. Moreover, a single person is capable to command and control several UAVs at a time.

#### **1.2 Related work**

Swarm of UAVs is evolving because of its significant capabilities of long-range operations, enhanced robustness, and flexibility [3]. Swarm intelligence has a high impact on many fields such as technology, science, society, and various systems like inspection, tracking, transporting, and many others [4]. For the motion planning of UAV swarms, many improvements in terms of control designs, path planning algorithms, communication structure, monitoring and tracking architectures, and safe flight protocols are considered in different studies [5].

The researchers combined computational techniques with mathematical models in [6] to examine the communication effects. The modeling process was simplified through this approach, but the process of modeling was slow and run out of memory. In [7] a controller based on a decentralized, leader-follower strategy, and a geometry of the tree-based network were suggested. This study achieved the arrival of multi-UAVs at a common spot with maintained synchronization. Moreover, the suggested design showed flexibility and robust performance. However, this study was bounded to a limited number of UAVs. In [8] researchers developed a framework for novel path planning of UAV swarm. This proposed algorithm resulted in efficient path planning with a reduction in energy and inspection time. Additionally, it provided the guidelines for determining various parameters.

In [9] the study presented an algorithm for computing the control of swarm and modeling their distributed behavior. The examination and simulations have shown the communication latency effects on different scenarios. In [10] an improved algorithm with resilience metric is proposed while considering the limited communication range effects. This strategy is implemented in a surveillance mission, which showed its significance as a more realistic method that can face efficiently the external disturbance and threats. In a recent study [11], the concepts of PIO algorithm, proportional-integral controller, and proportional integral differential controller are employed for the formation control of UAV clusters. This strategy has outperformed the traditional methods and provided a safe flight protocol. Further extensive reflection on how this technology has evolved is in the section of the related survey.

#### **1.3 Motivation and contribution**

The motivation for this paper is to gather multiple challenges, which can hinder the performance of a UAV swarm, on a single platform. Moreover, to provide appropriate approaches as the solutions to achieve optimal motion planning. This study can assist researchers in exploring multiple motion planning strategies with their contributions and limitations. The appropriate selection of the motion planning

techniques and models can complete the complex tasks quickly and targets the applications dotage as well. Following are the significant contributions of this paper:


#### **1.4 Organization of the Paper**

The paper is organized into many sections. Section 2 provides the state-of-theart of UAV swarms. Section 3 evaluates the concept of swarm intelligence. Section 4 presents challenges faced by the UAV swarm. Section 5 reflects on an extensive survey of the techniques and models used to address many challenges concerning the UAV swarm. Section 6 discusses the key findings and limitations. Section 7 gives the conclusion, and Section 8 recommends some future work for further research and development.

#### **2. State-of-the-art**

The swarm makes decisions collectively and completes its aerial mission using relatively simple instructions due to the Artificial Intelligence (AI) technology and edge computing [12]. Features like following the leader and missions, path planning, sensing, and avoiding are already developed in the Veronte Autopilot. This advancement in the features makes teamwork possible and ensures task success. Surveillance and attack induction is a milestone event in the swarm globally. This game-changing capability of the swarm of UAVs is benefitting both larger as well as smaller nations. Other significant aspects of swarming include combined decision-making, self-healing, and adaptive formation flying. The swarm of UAVs is still in the progressing phase as further research is being conducted to further enhance the systems. Further focus includes the expansion of capability of artificial swarm intelligence, increase in the autonomy state among the swarm agents, and commodification to reduce the cost impacts.

The most amazing aspect of the UAV swarm is its application for both civilian and military purposes using swarm intelligence [13]. The civilian agencies are using the swarm technology for bigger plans. The National Aeronautics and Space Administration (NASA) is also employing this AI-based swarm technology for climate change analysis [14]. This results in the accomplishment of the required things, which were not possible while using one. Moreover, many developed nations have passed regulations to widespread the commercial application of UAV swarms. The swarm shows tremendous performance in power line and structure inspections, precision agriculture, surveying, search and rescue operations, and others.

However, the swarm of UAVs gained the spotlight for its potential and efficiency in military usage. If in combat, some of the UAVs of the swarm get shot down then still the remaining ones complete the mission with similar tactics, power, and flexibility. Raytheon demonstrated this by employing swarm operation during a field exercise of the US Defense Advanced Research Projects Agency (DARPA) program [15]. The

Raytheon swarm had the communication and coordination ability. Moreover, all the individuals had sensors, cameras, and Tactical Assault Kit (TAK) integration capability for environmental explorations.

The swarm technology is enhancing the capabilities of the military in complex environmental tasks. Many militaries, like the US and China militaries, are in a lead in testing and observing the simulations for swarm operations on the highest levels [16]. Some militaries, like the British military, are using this technology for real-time operations. The UK has also experimented with Leonardo's Brite Cloud for swarming that contained electronic warfare jammers. Similarly, soon Russia aims large UAV swarm induction, "Flock 93," in its army. Moreover, it is trying to fill the gap by 2025. Iran, Turkey, and India are also attempting efforts to mature and proliferate this technology using distributed intelligence and edge computing. Swarms of UAVs are the future of aerial wars, and the future is now [17].
