Related papers: Comparative Analysis of UAV Path Planning Algorith…
With the rapid development of the logistics industry, the path planning of logistics vehicles has become increasingly complex, requiring consideration of multiple constraints such as time windows, task sequencing, and motion smoothness.…
Safe UAV navigation is challenging due to the complex environment structures, dynamic obstacles, and uncertainties from measurement noises and unpredictable moving obstacle behaviors. Although plenty of recent works achieve safe navigation…
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectory in multi-UAV non-cooperative scenarios while collecting data from distributed Internet of Things (IoT) nodes…
In this paper, we propose an algorithm for the formation of multiple UAVs used in vision-based inspection of infrastructure. A path planning algorithm is first developed by using a variant of the particle swarm optimisation, named…
Sampling-based algorithms for robot path planning offer probabilistic completeness and strong empirical convergence properties across environments with diverse obstacle configurations. However, in practice, these methods often require many…
Mobile robots dedicated in security tasks should be capable of clearly perceiving their environment to competently navigate within cluttered areas, so as to accomplish their assigned mission. The paper in hand describes such an autonomous…
Finding asymptotically-optimal paths in multi-robot motion planning problems could be achieved, in principle, using sampling-based planners in the composite configuration space of all of the robots in the space. The dimensionality of this…
The Unmanned Aerial Vehicle (UAV) path planning problem is a complex optimization problem in the field of robotics. In this paper, we investigate the possible utilization of this problem in benchmarking global optimization methods. We…
Within this paper, the problem of 3D structural inspection path planning for distributed infrastructure using aerial robots that are subject to time constraints is addressed. The proposed algorithm handles varying spatial properties of the…
Safe and efficient path planning is crucial for autonomous mobile robots. A prerequisite for path planning is to have a comprehensive understanding of the 3D structure of the robot's environment. On MAVs this is commonly achieved using…
The Autonomy of Unmanned Aerial Vehicles (UAVs) in indoor environments poses significant challenges due to the lack of reliable GPS signals in enclosed spaces such as warehouses, factories, and indoor facilities. Micro Aerial Vehicles…
Probabilistic sampling methods have become very popular to solve single-shot path planning problems. Rapidly-exploring Random Trees (RRTs) in particular have been shown to be very efficient in solving high dimensional problems. Even though…
This work addresses the path planning problem for a group of unmanned aerial vehicles (UAVs) to maintain a desired formation during operation. Our approach formulates the problem as an optimization task by defining a set of fitness…
This paper presents a mission system and the therein implemented algorithms for path planning in a time-varying environment based on graph methods. The basic task of the introduced path planning algorithms is to find a time-optimal path…
Robot path planning plays a pivotal role in enabling autonomous systems to navigate safely and efficiently in complex and uncertain environments. Despite extensive research on classical graph-based methods and sampling-based planners,…
Multiple fixed-wing unmanned aerial vehicles (multi-UAVs) encounter significant challenges in cooperative path following over complex Digital Elevation Model (DEM) low-altitude airspace, including wind field disturbances, sudden obstacles,…
Path planning is an essential component of mobile robotics. Classical path planning algorithms, such as wavefront and rapidly-exploring random tree (RRT) are used heavily in autonomous robots. With the recent advances in machine learning,…
With the pervasiveness of Stochastic Shortest-Path (SSP) problems in high-risk industries, such as last-mile autonomous delivery and supply chain management, robust planning algorithms are crucial for ensuring successful task completion…
This paper addresses the challenge of navigating unmanned aerial vehicles in contested environments by introducing a cooperative multi-agent framework that increases the likelihood of safe UAV traversal. The approach involves two types of…
Rapidly-exploring Random Tree Star(RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal path regardless of obstacle's geometry in a given environment.…