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Unmanned aerial vehicle (UAVs) is capable of adding significant potential to the internet of thing (IoT) devices and hence smart UAV–IoT collaborative system has attracted the attention of many researchers. This system has to be energy efficient for its nature and functionalities. Optimized trajectory planning is a significant area of research for any automatic movable device. In this article, we propose a technique, called EETO-GA for energy-efficient trajectory optimization of UAV–IoT using a genetic algorithm (GA). This technique prescribes each device of: (i) the next timestamp of arrival on the present cluster of IoT devices, at which the task queue of its header contains the highest possible number of tasks, and (ii) the minimum amount of energy that requires to complete all the tasks present in task queue of the IoT device. This technique uses a GA for optimization where the fitness function is designed by optimizing objectives: (i) the total number of tasks that can be completed, (ii) minimization of consumed energy, and (iii) the number of devices that could be served. A GA is applied here to accommodate a large number of IoT devices. A binary method of encoding is applied and methods like cross-over and mutation are used to arrive at the optimal solution. Through a simulation study, the proposed technique shows significant improvement in terms of UAV energy saved (UAVE), energy saving in IoT devices (IoTDEC), the average delay in execution of the task (ADET), and the percentage of tasks that could be completed (PTSK). Proposed EETO-GA improved average UAVE: 43%, IoTDEC: 56%, PTSK: 7.5%, and ADET: 38% over the state of the art. [ABSTRACT FROM AUTHOR]

The ability of an autonomous Unmanned Aerial Vehicle (UAV) in an unknown environment is a prerequisite for its execution of complex tasks and is the main research direction in related fields. The autonomous navigation of UAVs in unknown environments requires solving the problem of autonomous exploration of the surrounding environment and path planning, which determines whether the drones can complete mission-based flights safely and efficiently. Existing UAV autonomous flight systems hardly perform well in terms of efficient exploration and flight trajectory quality. This paper establishes an integrated solution for autonomous exploration and path planning. In terms of autonomous exploration, frontier-based and sampling-based exploration strategies are integrated to achieve fast and effective exploration performance. In the study of path planning in complex environments, an advanced Rapidly Exploring Random Tree (RRT) algorithm combining the adaptive weights and dynamic step size is proposed, which effectively solves the problem of balancing flight time and trajectory quality. Then, this paper uses the Hermite difference polynomial to optimization the trajectory generated by the RRT algorithm. We named proposed UAV autonomous flight system as Frontier and Sampling-based Exploration and Advanced RRT Planner system (FSEPlanner). Simulation performs in both apartment and maze environment, and results show that the proposed FSEPlanner algorithm achieves greatly improved time consumption and path distances, and the smoothed path is more in line with the actual flight needs of a UAV. [ABSTRACT FROM AUTHOR]

In recent years, the use of modern technology in military operations has become standard practice. Unmanned systems play an important role in operations such as reconnaissance and surveillance. This article examines a model for planning aerial reconnaissance using a fleet of mutually cooperating unmanned aerial vehicles to increase the effectiveness of the task. The model deploys a number of waypoints such that, when every waypoint is visited by any vehicle in the fleet, the area of interest is fully explored. The deployment of waypoints must meet the conditions arising from the technical parameters of the sensory systems used and tactical requirements of the task at hand. This paper proposes an improvement of the model by optimizing the number and position of waypoints deployed in the area of interest, the effect of which is to improve the trajectories of individual unmanned systems, and thus increase the efficiency of the operation. To achieve this optimization, a modified simulated annealing algorithm is proposed. The improvement of the model is verified by several experiments. Two sets of benchmark problems were designed: (a) benchmark problems for verifying the proposed algorithm for optimizing waypoints, and (b) benchmark problems based on typical reconnaissance scenarios in the real environment to prove the increased effectiveness of the reconnaissance operation. Moreover, an experiment in the SteelBeast simulation system was also conducted. [ABSTRACT FROM AUTHOR]