The booming development of e-commerce has brought many challenges to the logistics industry. To ensure the sustainability of the logistics industry, the impact of environmental and social sustainability factors on logistics development needs to be considered. Unmanned Aerial Vehicles (UAVs)/drones are used in the logistics field because of their flexibility, low cost, environmental protection and energy-saving advantages, which can achieve both economic benefits and social benefits. This paper reviews 36 studies on UAVs applications in logistics from the Web of Science database from the past two years (2021–2022). The selected literature is classified into theoretical models (the traveling salesman problem and other path planning problems), application scenarios (medical safety applications and last-mile delivery problems) and other problems (UAV implementation obstacles, costs, pricing, etc.). Finally, future directions of UAVs are proposed, such as different application scenarios that can be considered and different algorithms that can be combined to optimize paths for UAVs to specific flight environments.

/pdf/application-of-unmanned-aerial-vehicles-in-logistics-a-6na0xm31.pdf

Application of Unmanned Aerial Vehicles in Logistics: A Literature Review

Recently, unmanned aerial vehicles (UAVs) have shown promising results for autonomous sensing. UAVs have been deployed for multiple applications that include surveillance, mapping, tracking, and search operations. Finding an efficient path between a source and a goal is a critical issue that has been the focus of recent exploration. Many path-planning algorithms are utilized to find an efficient path for a UAV to navigate from a source to a goal with obstacle avoidance. Despite the extensive literature and numerous research proposals for path planning, dynamic obstacle avoidance has not been addressed with machine learning. When the obstacles are dynamic, i.e., they can change their position over time, and the constraints of the path planning algorithm become more challenging. This in turn adds a layer of complexity to the path planning algorithm. To address this challenge, a Q-learning algorithm is proposed in this work to facilitate efficient path planning for UAVs with both static and dynamic obstacle avoidance. We introduced the Shortest Distance Prioritization policy in the learning process which marginally reduces the distance that the UAV has to travel to reach the goal. Further, the proposed Q-learning algorithm adopts a grid-graph-based method to solve the path-planning problem. It learns to maximize the reward based on the agent’s behavior in the environment. Through results, the performance comparison between the proposed approach and state-of-the-art path planning approaches such as A-star, Dijkstra, and Sarsa algorithms are evaluated in terms of learning time and path length. We show through results that the proposed approach results in improved performance when compared to state-of-the-art approaches. Further, the effect of an increased number of obstacles are evaluated on the performance of the proposed approach. 

Q-learning-based unmanned aerial vehicle path planning with dynamic obstacle avoidance

UAVs have been contributing substantially to multi-disciplinary research and around 70% of the articles have been published in just about the last five years, with an exponential increase. Primarily, while exploring the literature from the scientific databases for various aspects within the autonomous UAV path planning, such as type and configuration of UAVs, the complexity of their environments or workspaces, choices of path generating algorithms, nature of solutions and efficacy of the generated paths, necessitates an increased number of search keywords as a prerequisite. However, the addition of more and more keywords might as well curtail some conducive and worthwhile search results in the same pursuit. This article presents a Systematic Literature Review (SLR) for 20 useful parameters, organized into six distinct categories that researchers and industry practitioners usually consider. In this work, Web of Science (WOS) was selected to search the primary studies based on three keywords: “Autonomous” + “Path Planning” + “UAV” and following the exclusion and inclusion criteria defined within the SLR methodology, 90 primary studies were considered. Through literature synthesis, a unique perspective to see through the literature is established in terms of characteristic research sectors for UAVs. Moreover, open research challenges from recent studies and state-of-the-art contributions to address them were highlighted. It was also discovered that the autonomy of UAVs and the extent of their mission complexities go hand-in-hand, and the benchmark to define a fully autonomous UAV is an arbitral goal yet to be achieved. To further this quest, the study cites two key models to measure a drone’s autonomy and offers a novel complexity matrix to measure the extent of a drone’s autonomy. Additionally, since preliminary-level researchers often look for technical means to assess their ideas, the technologies used in academic research are also tabulated with references.

/pdf/a-systematic-literature-review-slr-on-autonomous-path-ncgdy6dc.pdf

A Systematic Literature Review (SLR) on Autonomous Path Planning of Unmanned Aerial Vehicles

Cooperative multi-task traversing with complex marine environment for multiple unmanned surface vehicles inspired by membrane computing

Deep Q-network (DQN) is one of the standard methods to solve the Unmanned Aerial Vehicle (UAV) path planning problem. However, the way agent deepens its cognition of the environment through frequent random trial-and-error leads to slow convergence. This paper proposes an optimized DQN with Artificial Potential Field (APF) as prior knowledge called B-APFDQN for path planning. Replacing the traditional neural network which has only one Q-value output with a multi-output neural network to promote the training process in combination with APF. Furthermore, a SA- $\varepsilon $ -greedy algorithm that can automatically adjust the stochastic exploration frequency with steps and successes is proposed in order to prevent the agent from falling into local optimum. We remove the nodes that do not affect the path connectivity and apply the B-spline algorithm to make the path shorter and smoother. Simulation experiments show that the proposed B-APFDQN algorithm performs better than the classical DQN, has a strong ability to avoid falling into local optimum, and the obtained paths are smooth and shorter, which proves the effectiveness of B-APFDQN in the UAV path planning problem.

B-APFDQN: A UAV Path Planning Algorithm Based on Deep Q-Network and Artificial Potential Field

K-means algorithm is a typical distance-based clustering analysis algorithm, and the neighborhood granular K-means clustering algorithm uses the neighborhood granulation technique on its basis to solve the problem of poor clustering of the K-means clustering algorithm on non-linear separable datasets. However, the use of neighborhood relations in the granulation lead to the inability of the algorithm to deal with outliers effectively, resulting in some samples being misclassified. By introducing the concept of fuzzy set and combining granular computing theory, a new neighborhood granular K-means clustering method is proposed, where samples are constructed using fuzzy neighborhood granularity technique to construct fuzzy neighborhood granules, and the membership between samples is used for neighborhood granularity instead of clear neighborhood relations, which reduces the impact of outliers. A new distance measure for fuzzy neighborhood granules is proposed based on the size and form of the fuzzy neighborhood granules, which reduces the computational complexity. In addition, the algorithm combines the idea of K Nearest Neighbor to define a neighborhood threshold metric that adaptively generates neighborhood thresholds for any data set. It considers the size of the data set objectively and reduces the subjective influence of manual selection of neighborhood threshold. Finally, the Accuracy and Fowlkes Mallows Index values of the algorithm are increased by 6.4 %, 9.21%, 3.5%, and 7.89% on the synthetic data set and UCI data set, respectively, and increased by 8.65% and 9.43% on the seismic facies lithology identification data set, respectively. The experimental results prove the feasibility and effectiveness of the fuzzy neighborhood granular K-means clustering method.

A K-means Clustering Method for Fuzzy Neighborhood Granules

Unmanned aerial vehicles (UAVs) are playing an increasingly important role in people’s daily lives due to their low cost of operation, low requirements for ground support, high maneuverability, high environmental adaptability, and high safety. Yet UAV path planning under various safety risks, such as crash and collision, is not an easy task, due to the complicated and dynamic nature of path environments. Therefore, developing an efficient and flexible algorithm for UAV path planning has become inevitable. Aimed at quality-oriented UAV path planning, this paper is designed to analyze UAV path planning from two aspects: global static planning and local dynamic hierarchical planning. Through a theoretical and mathematical approach, a three-dimensional UAV path planning model was established. Based on the A* algorithm, the search strategy, the step size, and the cost function were improved, and the OPEN set was simplified, thereby shortening the planning time and greatly improving the execution efficiency of the algorithm. Moreover, a dynamic exploration factor was added to the exploration mechanism of Q-learning to solve the exploration-exploitation dilemma of Q-learning to adapt to the local dynamic path adjustment for UAVs. The global-local hybrid UAV path planning algorithm was formed by combining the two. The simulation results indicate that the proposed planning model and algorithm can efficiently solve the problem of UAV path planning, improve the path quality, and can be a significant reference for solving other problems related to path planning, such as the reliability, security, and safety of UAV, when embedded into the heuristic function of the proposed algorithm.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09665744.pdf

Quality-Oriented Hybrid Path Planning Based on A* and Q-Learning for Unmanned Aerial Vehicle

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A K-means Clustering Method for Fuzzy Neighborhood Granules

Quality-Oriented Hybrid Path Planning Based on A* and Q-Learning for Unmanned Aerial Vehicle