Unmanned Ariel Vehicle (UAV) services with 5G connectivity is an emerging
field with numerous applications. Operator-controlled UAV flights and manual
static flight configurations are major limitations for the wide adoption of
scalability of UAV services. Several services depend on excellent UAV
connectivity with a cellular network and maintaining it is challenging in
predetermined flight paths. This paper addresses these limitations by proposing
a Deep Reinforcement Learning (DRL) framework for UAV path planning with
assured connectivity (DUPAC). During UAV flight, DUPAC determines the best
route from a defined source to the destination in terms of distance and signal
quality. The viability and performance of DUPAC are evaluated under simulated
real-world urban scenarios using the Unity framework. The results confirm that
DUPAC achieves an autonomous UAV flight path similar to base method with only
2% increment while maintaining an average 9% better connection quality
throughout the flight.

通过提出 DUPAC，一种基于深度强化学习的无人机路径规划框架来解决操作员控制的无人机飞行和手动静态飞行配置等问题，该框架在维持优秀无人机与蜂窝网络连接的同时，实现了从起点到终点的最佳路线确定，并在实验中展现了可行性和性能。

稠密城市环境下确保连接性的深度无人机路径规划

Deep UAV Path Planning with Assured Connectivity in Dense Urban Setting

The competition focuses on Multiparty Multiobjective Optimization Problems
(MPMOPs), where multiple decision makers have conflicting objectives, as seen
in applications like UAV path planning. Despite their importance, MPMOPs remain
understudied in comparison to conventional multiobjective optimization. The
competition aims to address this gap by encouraging researchers to explore
tailored modeling approaches. The test suite comprises two parts: problems with
common Pareto optimal solutions and Biparty Multiobjective UAV Path Planning
(BPMO-UAVPP) problems with unknown solutions. Optimization algorithms for the
first part are evaluated using Multiparty Inverted Generational Distance
(MPIGD), and the second part is evaluated using Multiparty Hypervolume (MPHV)
metrics. The average algorithm ranking across all problems serves as a
performance benchmark.

该研究论文关注于多方多目标优化问题（MPMOPs），着眼于冲突目标的多个决策者，如无人机路径规划。通过鼓励研究人员探索定制建模方法，致力于填补 MPMOPs 相对传统多目标优化的研究空白。测试套件由两个部分组成：具有常见帕累托最优解的问题和未知解的两方多目标无人机路径规划（BPMO-UAVPP）问题。第一部分的优化算法使用多方反转生成距离（MPIGD）进行评估，第二部分则使用多方超体积（MPHV）指标进行评估。所有问题上的平均算法排名作为性能基准。

CEC 2024 多方多目标优化竞赛的基准

Benchmark for CEC 2024 Competition on Multiparty Multiobjective  Optimization

In this paper, a modification of A* algorithm is considered for the shortest
path problem. A weightage is introduced in the heuristic part of the A*
algorithm to improve its efficiency. An application of the algorithm is
considered for UAV path planning wherein velocity is taken as the weigtage to
the heuristic. At the outset, calculus of variations based Lagrange's equation
was used to identify velocity as the decisive factor for the dynamical system.
This approach would be useful for other problems as well to improve the
efficiency of algorithms in those areas.

本文考虑了 A* 算法的一种修改方案，引入了启发式部分的加权来提高算法的效率，以无人机路径规划为应用场景，其中速度被视为启发式规划的权重。通过变分法基于拉格朗日方程计算可以确定速度为动态系统的决定性因素，该方法可以应用于其他领域的算法以提高效率。