Integrating Learning, Optimization, and Prediction for Efficient Navigation of Swarms of Drones

Majd, Amin; Ashraf, Adnan; Troubitsynå, Elena; Daneshtalab, Masoud

doi:10.1109/pdp2018.2018.00022

Cited by 29 publications

(14 citation statements)

References 19 publications

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“…The NC column in Table 4 shows the number of collisions or crashes for the proposed, PSO based, greedy and GA based, RRT, and RRT* algorithms. The total number of crashes in all experiments was 0, 14,9,18, and 29, respectively. As stated in Section 3, the proposed system provides a safety-first approach in which a hazard-free, safe operation of the UAV swarm takes precedence over any other objectives including the route length.…”

Section: Results and Analysismentioning

confidence: 99%

“…A distinctive feature of the proposed system is its ability to foresee potential collisions and proactively find best ways to avoid the predicted collisions in order to ensure safety of the entire swarm. In contrast to the existing works [3,[6][7][8][9][11][12][13][14][15]18,19,24], our proposed system does not depend on a planning phase and produces efficient, collision-free paths in an online manner. We focus on collision prediction and avoidance and online path generation and navigation for swarms of UAVs.…”

Section: Introductionmentioning

confidence: 99%

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Online Path Generation and Navigation for Swarms of UAVs

Ashraf

Majd

Troubitsynå

2020

Scientific Programming

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With the growing popularity of Unmanned Aerial Vehicles (UAVs) for consumer applications, the number of accidents involving UAVs is also increasing rapidly. Therefore, motion safety of UAVs has become a prime concern for UAV operators. For a swarm of UAVs, a safe operation can not be guaranteed without preventing the UAVs from colliding with one another and with static and dynamically appearing, moving obstacles in the flying zone. In this paper, we present an online, collision-free path generation and navigation system for swarms of UAVs. The proposed system uses geographical locations of the UAVs and of the successfully detected, static and moving obstacles to predict and avoid: (1) UAV-to-UAV collisions, (2) UAV-to-static-obstacle collisions, and (3) UAV-tomoving-obstacle collisions. Our collision prediction approach leverages efficient runtime monitoring and Complex Event Processing (CEP) to make timely predictions. A distinctive feature of the proposed system is its ability to foresee potential collisions and proactively find best ways to avoid predicted collisions in order to ensure safety of the entire swarm. We also present a simulation-based implementation of the proposed system along with an experimental evaluation involving a series of experiments and compare our results with the results of four existing approaches. The results show that the proposed system successfully predicts and avoids all three kinds of collisions in an online manner. Moreover, it generates safe and efficient UAV routes, efficiently scales to large-sized problem instances, and is suitable for cluttered flying zones and for scenarios involving high risks of UAV collisions.

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Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online Path Generation and Navigation for Swarms of UAVs

Ashraf

Majd

Troubitsynå

2020

Scientific Programming

Self Cite

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“…Challita et al 9 used a DRL approach to provide interference-aware path planning for UAVs and minimize their transmission latency so that each UAV learns its path, transmitted power, and association vector. Majd et al 10 focused on estimating the movement of drones and optimizing drone routes and system states based on a RL algorithm.…”

Section: Related Workmentioning

confidence: 99%

Energy‐aware mobility for aerial networks: A reinforcement learning approach

et al. 2021

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With recent advancements in aerial networks, aerial base stations (ABSs) have become a promising mobile network technology to enhance the coverage and capacity of the cellular networks. ABS deployment can assist cellular networks to support network infrastructure or minimize the disruptions caused by unexpected and temporary situations. However, with 3D ABS placement, the continuity of the service has increased the challenge of providing satisfactory Quality of Service (QoS). The limited battery capacity of ABSs and continuous movement of users result in frequent interruptions. Although aerial networks provide quick and effective coverage, ABS deployment is challenging due to the user mobility, increased interference, handover delay, and handover failure. In addition, once an ABS is deployed, an intelligent management must be applied. In this paper, we model user mobility pattern and formulate energyaware ABS deployment problem with a goal of minimizing energy consumption and handover delay. To this end, the contributions of this paper are threefold: (i) analysis of reinforcement learning (RL)-based state action reward state action (SARSA) algorithm to deploy ABSs with an energy consumption model, (ii) predicting the user next-place with a hidden Markov model (HMM), and (iii) managing the dynamic movement of ABSs with a handover procedure. Our model is validated by comprehensive simulation, and the results indicate superiority of the proposed model on deploying multiple ABSs to provide the communication coverage.

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“…According to the optimal task allocation scheme, the drone dr 0 and the drones dr i ∈ D are orchestrated to perform distributed computing to complete the task Ψ 0 collaboratively. Note that the flying speeds of the low-cost drones are relatively slow, and the relative positions of them are relatively stable, instead of constantly changing [43], and meantime, the tasks which we studied is latency-sensitive, which is in general processed within a ultra-low duration. Hence, the status of the entire drones swarm will not change, during the extremely short slot from the task initiation to the completion of the task processing.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Distributed Fog Computing for Latency and Reliability Guaranteed Swarm of Drones

et al. 2020

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Swarm of drones, as an intensely significant category of swarm robots, is widely used in various fields, e.g., search and rescue, detection missions, military, etc. Because of the limitation of computing resource of drones, dealing with computation-intensive tasks locally is difficult. Hence, the cloud-based computation offloading is widely adopted, nevertheless, for some latency-sensitive tasks, e.g., object recognition, path planning, etc., the cloud-based manner is inappropriate due to the excessive delay. Even in some harsh environments, e.g., disaster area, battlefield, etc., there is no wireless infrastructure existed to combine the drones and cloud center. Thus, to solve the problem encountered by cloud-based computation offloading, in this paper, Fog Computing aided Swarm of Drones (FCSD) architecture is proposed. Considering the uncertainty factors in harsh environments which may threaten the success of FCSD processing tasks, not only the latency model, but also the reliability model of FCSD is constructed to guarantee the high reliability of task completion. Moreover, in view of the limited battery life of the drone, we formulated the problem as the task allocation problem which minimized the energy consumption of FCSD under the constraints of latency and reliability. Furthermore, to speed up the process of the optimization problem solving to improve the practicality, relying on the recent advances in distributed convex optimization, we develop a fast Proximal Jacobi Alternating Direction Method of Multipliers (ADMM) based distributed algorithm. Finally, simulation results validate the effectiveness of our proposed scheme. INDEX TERMS Swarm of drones, distributed fog computing, latency, reliability, energy consumption.

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Integrating Learning, Optimization, and Prediction for Efficient Navigation of Swarms of Drones

Cited by 29 publications

References 19 publications

Online Path Generation and Navigation for Swarms of UAVs

Online Path Generation and Navigation for Swarms of UAVs

Energy‐aware mobility for aerial networks: A reinforcement learning approach

Distributed Fog Computing for Latency and Reliability Guaranteed Swarm of Drones

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