Optimized Path Planning for Inspection by Unmanned Aerial Vehicles Swarm with Energy Constraints

Abstract

Autonomous inspection of large geographical areas is a central requirement for efficient hazard detection and disaster management in future cyber-physical systems such as smart cities. In this regard, exploiting unmanned aerial vehicle (UAV) swarms is a promising solution to inspect vast areas efficiently and with low cost. In fact, UAVs can easily fly and reach inspection points, record surveillance data, and send this information to a wireless base station (BS). Nonetheless, in many cases, such as operations at remote areas, the UAVs cannot be guided directly by the BS in real-time to find their path. Moreover, another key challenge of inspection by UAVs is the limited battery capacity. Thus, realizing the vision of autonomous inspection via UAVs requires energy-efficient path planning that takes into account the energy constraint of each individual UAV. In this paper, a novel path planning algorithm is proposed for performing energy-efficient inspection, under stringent energy availability constraints for each UAV. The developed framework takes into account all aspects of energy consumption for a UAV swarm during the inspection operations, including energy required for flying, hovering, and data transmission. It is shown that the proposed algorithm can address the path planning problem efficiently in polynomial time. Simulation results show that the proposed algorithm can yield substantial performance gains in terms of minimizing the overall inspection time and energy. Moreover, the results provide guidelines to determine parameters such as the number of required UAVs and amount of energy, while designing an autonomous inspection system.

Deep Reinforcement Learning for Minimizing Age-of-Information in UAV-Assisted Networks

“…. , or (5,10). Also, we have τ = 10 which is the required number of time slots needed to move directly from the initial cell to the final cell.…”

“…To show the effective trajectory optimization and scheduling of the UAV, in Fig. 3b, we consider simulation scenario 2 in which there are two ground nodes located at (2, 10) and (8,10). Then, we choose e max and τ such that the UAV can receive only one packet from any of ground nodes when it can be as close as possible to ground nodes.…”

“…3c, we set up three scenarios. In scenario 3, we consider three ground nodes located at (5,10), (0, 0), and (0, 10), where τ = 100 and e max varies between 2 0 and 2 10 and are equal for all of the ground nodes. In scenario 4, the locations of ground nodes are the same as scenario 3 while e max = 100 for all of the ground nodes and τ varies between 10 and 100.…”

Edge Cloud Resource-aware Flight Planning for Unmanned Aerial Vehicles

“…An interference-aware path planning scheme for a network of cellular-connected UAVs, wherein each UAV aims at achieving a trade-off between maximizing energy efficiency and minimizing both wireless latency and the interference level, is proposed in [14]. A novel path planning algorithm for performing energy-efficient inspection using UAVs under stringent energy availability constraints is proposed in [15]. The optimal trajectory planning for UAV-assisted data collection in wireless sensor networks under the age of data constrains is elaborated in [16].…”

Cognitive UAV Communication via Joint Maneuver and Power Control

“…In [28], an interference-aware path planning design was proposed for multiple UAV users, which aimed to achieve an optimal trade-off between energy efficiency, latency and interference caused by the UAVs to the ground network. The work [29] proposed an energy-efficient path planning design to minimize the energy consumption of UAV swarms, subject to individual energy availability constraints at UAVs.…”