Path planning is important for the autonomy of Unmanned Aerial Vehicle (UAV), especially for scheduling UAV delivery. However, the operating environment of UAVs is usually uncertain and dynamic. Without proper planning, collisions may happen where multiple UAVs are congested. Besides, there may also be temporary no-fly zone setup by authorities that makes airspace unusable. Thus, proper pre-departure planning that avoids such places is needed. In this paper, we formulate this problem into a Constraint Satisfaction Problem to find a collision-free shortest path on a dynamic graph. We propose a collision-free path planning algorithm that is based on A* algorithm. The main novelty is that we invent a heuristic function that also considers waiting time. We later show that, with added waiting penalty, the proposed algorithm is optimal because the heuristic is admissible. Implementation of this algorithm simulates UAV delivery using Singapore's airspace structure. Our simulation exhibits desirable runtime performance. Using the proposed algorithm, the percentage of collision-free routes decreases as number of requests per unit area increases, and this percentage drops significantly at boundary value. Our empirical analysis could aid the decision-making of no-fly zone policy and infrastructure of UAV delivery.
Given we have global information regarding the the existing schedules of UAVs and their current locations, we can optimize the route through off-line planning. One natural way is using A* algorithm. A* algorithm @cite_4 is a best-first, efficient and optimal search algorithm. It uses heuristics to guide the search, and therefore, reduce the number of nodes needed to be explored. Another advantage for using A* is that it can apply on both 2D and 3D space as long as the graph is connected. @cite_1 proposed an offline improved A* algorithm to deal with realistic constraints in UAV movement, including maximum moving angle, minimum route leg length, minimum flight height and maximum route length. Their algorithm can avoid collision with terrain while minimize the flight height. However, it only handles static graph, and can be computationally expensive. proposed fixes by trimming some less-permissble nodes, or imposing some hard nodes that the route must follows, but doing so will lose some potentially optimal solutions, and thus lose its completeness and optimality.
@cite_1 @cite_4
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This paper studies an unmanned aerial vehicle (UAV)-enabled multicasting system, where a UAV is dispatched to disseminate a common file to a number of geographically distributed ground terminals (GTs). Our objective is to design the UAV trajectory to minimize its mission completion time, while ensuring that each GT is able to successfully recover the file with a high probability required. We consider the use of practical random linear network coding (RLNC) for UAV multicasting, so that each GT is able to recover the file as long as it receives a sufficiently large number of coded packets. However, the formulated UAV trajectory optimization problem is non-convex and difficult to be directly solved. To tackle this issue, we first derive an analytical lower bound for the success probability of each GT's file recovery. Based on this result, we then reformulate the problem into a more tractable form, where the UAV trajectory only needs to be designed to meet a set of constraints each on the minimum connection time with a GT, during which their distance is below a designed threshold. We show that the optimal UAV trajectory only needs to constitute connected line segments, thus it can be obtained by determining first the optimal set of waypoints and then UAV speed along the lines connecting the waypoints. We propose practical schemes for the waypoints design based on a novel concept of virtual base station (VBS) placement and by applying convex optimization techniques. Furthermore, for given set of waypoints, we obtain the optimal UAV speed over the resulting path efficiently by solving a linear programming (LP) problem. Numerical results show that the proposed UAV-enabled multicasting with optimized trajectory design achieves significant performance gains as compared to benchmark schemes. This paper presents a novel path planning algorithm for the autonomous exploration of unknown space using aerial robotic platforms. The proposed planner employs a receding horizon “next-best-view” scheme: In an online computed random tree it finds the best branch, the quality of which is determined by the amount of unmapped space that can be explored. Only the first edge of this branch is executed at every planning step, while repetition of this procedure leads to complete exploration results. The proposed planner is capable of running online, onboard a robot with limited resources. Its high performance is evaluated in detailed simulation studies as well as in a challenging real world experiment using a rotorcraft micro aerial vehicle. Analysis on the computational complexity of the algorithm is provided and its good scaling properties enable the handling of large scale and complex problem setups. In this paper, we propose a novel and computationally efficient algorithm for simultaneous exploration and coverage with a vision-guided micro aerial vehicle (MAV) in unknown environments. This algorithm continually plans a path that allows the MAV to fulfil two objectives at the same time while avoiding obstacles: observe as much unexplored space as possible, and observe as much of the surface of the environment as possible given viewing angle and distance constraints. The former and latter objectives are known as the exploration and coverage problems respectively. Our algorithm is particularly useful for automated 3D reconstruction at the street level and in indoor environments where obstacles are omnipresent. By solving the exploration problem, we maximize the size of the reconstructed model. By solving the coverage problem, we maximize the completeness of the model. Our algorithm leverages the state lattice concept such that the planned path adheres to specified motion constraints. Furthermore, our algorithm is computationally efficient and able to run on-board the MAV in real-time. We assume that the MAV is equipped with a forward-looking depth-sensing camera in the form of either a stereo camera or RGB-D camera. We use simulation experiments to validate our algorithm. In addition, we show that our algorithm achieves a significantly higher level of coverage as compared to an exploration-only approach while still allowing the MAV to fully explore the environment. In this work, we propose a search-based planning method to compute dynamically feasible trajectories for a quadrotor flying in an obstacle-cluttered environment. Our approach searches for smooth, minimum-time trajectories by exploring the map using a set of short-duration motion primitives. The primitives are generated by solving an optimal control problem and induce a finite lattice discretization on the state space which can be explored using a graph-search algorithm. The proposed approach is able to generate resolution-complete (i.e., optimal in the discretized space), safe, dynamically feasibility trajectories efficiently by exploiting the explicit solution of a Linear Quadratic Minimum Time problem. It does not assume a hovering initial condition and, hence, is suitable for fast online re-planning while the robot is moving. Quadrotor navigation with online re-planning is demonstrated using the proposed approach in simulation and physical experiments and comparisons with trajectory generation based on state-of-art quadratic programming are presented.