## 10 Open Challenges and Future Directions
### Energy Consumption  
To face the challenges of the maneuverability and portability of the UAVs, hardware components are reduced to a miniaturized size. As a result, they cannot carry many batteries or large-capacity batteries [41]. This constraint limits the energy consumption and contributes to the degradation of the communication and mobility performance of  
Figure 20: Example of swarm navigation based on divide-and-conquer. UAVs [36]. The energy consumption related to the communication system is used to ensure the signal processing, the packet transmission, the routing protocol, and the data transmission of the onboard sensors. On the other hand, the power consumption of the propulsion system is much higher than the energy consumption for communication. It generates thrust and lift forces to ensure the mobility and movement of the UAVs in the air [41]. This depends on the type of UAV and number of rotors (see Section 3), payload weight, flying altitude, speed, and acceleration. In addition, there are environmental factors such as gravity, air density, wind conditions, and weather that affect the drone's energy consumption. In the case of a multi-UAV system, the problem becomes even more challenging depending on the communication architecture, the mobility of the swarm, and the complexity of the mission. Several challenges remain to be addressed, including the optimization of the path planning [12; 49; 57], task optimization, and fast battery charging [40; 42]. * **The optimization of the coverage paths** in order to reduce considerably the power consumption of the UAVs. This problem consists in finding the optimal paths that maximize the number of covered waypoints in a network while minimizing both energy costs and emissions [12; 49]. A concrete application of this problem is the drone delivery operation, which consists in serving all the delivery waypoints without passing twice by the same point [38]. It is a multi-criteria optimization problem [58] that consists in determining the best energy consumption model that optimizes both the path cost, the energy consumed, and the emissions associated with the delivery strategy. * **The efficient planning of the collaborative tasks** of the swarm. In a multi-UAV system, the tasks are running concurrently and in a parallel way to achieve a common goal. The UAVs share a large amount of information synchronously or asynchronously in the network for waypoint generation, map reconstruction, and collision avoidance [24; 40]. These tasks are memory-intensive and also consume a significant amount of energy resources related to the communication system [36]. * **The maintenance of the battery** is another problem that affects the flight times of UAVs as the batteries used have a very limited lifespan [35; 41]. It allows us to improve the battery life cycle and to extend its duration of use. Furthermore, this is one of the effective alternatives to have a battery considered safer and with a longer life cycle [35]. However, this task is complex because there is no exact method to determine the life of a battery and each type of UAV has its own maintenance pattern [42]. There are numerous factors affecting the life of the battery that must be considered, such as the average, voltage, temperature, type of charger, and charge/discharge cycles [37]. These challenges open future directions as follows:
* **The design of energy-efficient trajectory-based reinforcement learning**, which allows us to generate an adaptive path. Each UAV acts as an autonomous agent that readjusts its trajectory, speed, acceleration, and altitude to maximize energy gains. Moreover, it reinforces its adaptive capacity by using a multi-objective function that rewards the actions that minimize the cost of its path, its energy consumption, and its emissions. Some recent examples were presented in [36; 40].