Drone Swarm challenges and Technology

In recent years, with the rapid development of Unmanned aerial vehicle (UAV) technologies, UAVs have been widely used in many fields. Different types of UAVs can help people complete some relatively dangerous, urgent, and even impossible tasks, such as environmental investigation, material distribution, map reconstruction, aerial photography, ocean exploration, etc.

 

However, the current UAVs are insufficiently intelligent to perform complex tasks, and most of them still need people’s real-time control. A single UAV can only perform relatively simple tasks, but the UAV group can efficiently complete many complex and arduous tasks after reasonable task planning. In addition, in future 6G mobile communication technology, UAV-assisted marine applications will be one of the hot research directions

 

Small and lightweight UAVs, also known as micro aerial vehicles (MAVs), have further stretched the boundaries of their applications. In addition to their portability, smaller UAVs have more agility which allows them to navigate through narrow environments, and cause less damage to their surroundings due to their light weight.

 

The small size of MAVs, however, limits their capabilities in terms of less flight time, on-board sensing and compute power, and payload, which, as a result, significantly reduces the number of tasks that they can perform individually. This has motivated the development of aerial swarms in which multiple UAVs cooperate in large teams to overcome the limitations of the individual robots.

 

A robot swarm typically consists of tiny, simple, indistinguishable robots that are each equipped with a sensor (and a camera, radar and/or sonar so they can gather information about their surrounding environment). When one robot collects and shares data with the others in the group, it allows the singular robots to function as a homogeneous group. A robot swarm can combine the knowledge and insights of millions of independent, self-sustaining agents (or boids) to form and converge on a single, unified decision.

 

Robot swarms are envisioned to be fully distributed systems where each robot observes its local neighboring environment and coordinates with other robots to execute local actions that collectively lead to achieving an overall swarm goal. Indeed, this is a multi-disciplinary complex system that requires tight integration of multiple subsystems such as global and relative localization, safe trajectory planning , and swarm-level task coordination.

 

In swarm robotics multiple robots collectively solve problems by forming advantageous structures and behaviors similar to the ones observed in natural systems, such as swarms of bees, birds, or fish. These micro-drones have demonstrated advanced swarm behaviors such as collective decision-making, adaptive formation flying, and self-healing. A swarm of robots would work on the same principles as an ant colony: each member has a simple set of rules to follow, leading to self-organization and self-sufficiency.

 

Swarms of unmanned vehicles represent a disruptive evolutionary phase of unmanned warfare. general swarms are defined as groups of Unmanned Vehicle Systems (UXS) operating autonomously in a coordinated manner to carry out a mission. The UXS elements may be aerial, land-based, surface, or undersea robotic platforms carrying out tasks ranging from:

• Intelligence, Surveillance, and Reconnaissance (ISR)
• Target Acquisition and Attack
• Suppression and Destruction of Enemy Air Defense (SEAD/DEAD)
• interdiction of high priority targets such as command posts, communications equipment, and radars

 

The major factors are number/size of drones, payload carrying capacity, diversity, coverage distance and so on.

 

Drone swarms are multiple unmanned platforms and/or weapons deployed to accomplish a shared objective, with the platforms and/or weapons autonomously altering their behavior based on communication with one another.

 

Each drone is usually a simple UAV platform (e.g., a quadcopter) equipped with an on-board flight controller, GPS sensor for positioning, customized LEDs, and a communication module to communicate with the ground station.

 

Drones acting in groups can be controlled individually by human controllers or operate fully autonomously as a group. Other operating methods follow herding behavior, in which some members serve as leaders while others act as followers. UXS swarms are often controlled from a single multi-launcher and ground control station, thus simplifying and accelerating deployment. Once launched, the individual drone’s operation is mainly autonomous, enabling a single operator to manage the whole swarm instead of flying each drone.

 

The ground station is used to pre-compute the required individual missions (collision-free trajectories in open 3D space) of all drones during the show. Then, each mission is uploaded to the corresponding drone which is executed by the on-board flight controller. The ground station also continuously monitors the swarm status during the show and provides controls for any required emergency actions.

 

Unlike attacks by individual drones or loitering weapons that require a human controller in the loop, drone swarms receive the brief and pursue the mission autonomously, constantly coordinating their behavior based on the mission phase to achieve their goal most efficiently. For example, they can plan and maneuver to attack the target from different directions, strike multiple targets at once, or sacrifice some elements in the swarm to trigger the target to react and reveal itself before being hit. Human controllers are primarily in a supervising role and would intervene and instruct the drones only when needed or asked for by the swarm.

 

At sea, swarms of small boats or loitering weapons and unmanned submersible vehicles can be used to disable enemy vessels by taking out their vulnerable assets such as radar and communications or sonar. Swarms can also be used preemptively to suppress enemy activity in specific areas such as airfields, landing zones, or launching sites of ballistic missiles.

 

Swarms may include many elements of the same platform (known as homogeneous swarms) or different players forming a heterogeneous group. Each drone may perform a similar role or have several specialised functions, such as information gathering, weapons deployment, or communications relaying. The key to their behavior is the network connecting all members. Typically, such a network enables the group to link all members by constantly retransmitting information, position, and navigation. Specific group members may assume control of the entire formation at different times to coordinate and prioritize actions, assign tasks, alert on obstacles or threats, or hand over power to other members. If a control node is eliminated, other members will take control based on the network’s self-forming, self-healing algorithms.

 

Although using UAVs for image and video acquisition is currently the most popular application, developing the so-called flying cellular networks  has been receiving an increasing attention. On one hand, UAVs can be equipped with cellular communication modules in order to extend their operation range, therefore significantly improving their service. On the other hand, UAVs can offer a unique opportunity to deploy flying base stations that can be dynamically located in 3D in order to boost coverage and optimize user experience.

 

One of the main challenges to this kind of application is the UAV’s limited endurance as a typical electric UAV would require recharging every hour or so. This problem can be overcome by utilizing tethered UAVs (TUAVs). A TUAV receives continuous power and high bandwidth communication through a tether connected to a base station. Interestingly, tethered drones for this application outperforms free-flying UAVs especially for 5G networks as the 5G equipment are heavier and consumes more power than 4G.

 

Communication and networking

Communication and networking techniques are essential to enabling collaborate information sharing, coordinating multiple drones, and achieving autonomous drone swarm.

 

Most commercial remote-controlled drones are controlled via frequency hopping spread spectrum (FHSS), using an advanced frequency-agile waveform, or by Wireless LAN (WLAN). Signals transmitted from the drone also use FHSS, wideband, or WLAN signals. Other drones may rely on Radio Frequency (RF), cellular or satellite communications (SATCOM). Swarms often utilise ad-hoc networking technologies (MESH network) to communicate between the group members. This method is specifically advantageous when operating beyond visual line of sight and over broad areas where existing connectivity is not guaranteed. Individual drones may connect to and disconnect from the network all the time, making the decentralised ad-hoc network structure highly suitable for their operation.

 

Despite their autonomous operation, drones and robots require comprehensive preparations before being sent into the mission. Route planning, pre-flight network setup, GNSS link establishment, coordination with controller and other group members are all done before takeoff to initiate an autonomous mission. Most of this activity has a distinctive electronic signature that can be detected by signals intelligence (SIGINT) activity. But some preparations are less visible than others. For example, loitering drones packed ready to launch on their carriers often perform this preparation in radio silence, without any emissions, testing, and setup are performed on the carrier.

 

Task Planning

The task planning problem of multiple UAVs can be divided into two parts, the task allocation problem and route planning problem, which are interrelated and different from each other. The task allocation problem is equivalent to the combinatorial optimization decision problem for multiple UAVs. It is a combination scheme designed to meet UAV performance and the constraints. The purpose is to make a UAV consume the least resources or obtain the maximum benefits with the shortest total path. The route planning problem involves planning a flight route from the starting point to the endpoint in the constrained task space and making the fitness function optimal.

 

The common task allocation methods include optimization algorithms (e.g., the Hungarian algorithm, branch definition method, graph theory, etc.), heuristic algorithms (e.g., clustering algorithms, ant colony algorithms (ACOs), particle swarm optimization algorithms (PSOs), genetic algorithms (GAs), artificial bee colonies, etc.) and distributed algorithms (e.g., the decentralized Markov decision process, the contract net auction algorithm, etc.). Common route planning methods include traditional algorithms (e.g., the Voronoi diagram method, the artificial potential field method, etc.), heuristic algorithms (the Dijkstra algorithm, the Floyd algorithm, the A* algorithm, etc.), and intelligent bionic algorithms.

 

Once the swarm is launched and grouped, its members can maneuver into formations, making individual target detection more difficult. Navigation to the target could employ global navigation satellites (GNSS, GPS), inertial navigation, image-based scene matching, or a combination of several methods, making it more challenging to defeat. Group members can rely on each other to determine their position and location, thus maintaining sensor redundancy to overcome specific countermeasures such as GPS jamming.

 

The technology development in AI (Artificial Intelligence), Big data and IoT have made drone swarm system more effective. AI algorithms enable drones to imitate certain animals which work together which is very helpful for synchronized tasks being performed by drones.

 

References and Resources also include:

https://euro-sd.com/2022/04/articles/exclusive/25769/countering-the-swarm/