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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UAV route planning is to plan a feasible route for the UAV considering the time of the UAV reaching the target, fuel consumption, mission requirements and the terrain and threat distribution in the flight area and other factors, in order to ensure the completion of the mission. We describe an improved algorithm of A∗ search approach which called sparse A∗ search approach in detail, including its content and process, and carried out a simulation experiment. And based on its disadvantages, we give an optimized method. This paper considers the problem of finding minimum-energy cooperative routes in a wireless network with variable wireless channels. We assume that each node in the network is equipped with a single omnidirectional antenna and, motivated by the large body of physical layer research indicating its potential utility, that multiple nodes are able to coordinate their transmissions at the physical layer in order to take advantage of spatial diversity. Such coordination, however, is intrinsically intertwined with routing decisions, thus motivating the work. We first formulate the energy cost of forming a cooperative link between two nodes based on a two-stage transmission strategy assuming that only statistical knowledge about channels is available. Utilizing the link cost formulation, we show that optimal static routes in a network can be computed by running Dijkstra's algorithm over an extended network graph created by cooperative links. However, due to the variability of wireless channels, we argue that a many-to-one cooperation model in static routing is suboptimal. Hence, we develop an opportunistic routing algorithm based on many-to-many cooperation, and show that optimal routes in a network can be computed by a stochastic version of the Bellman-Ford algorithm. We use static and opportunistic optimal algorithms as baselines to develop heuristic link selection algorithms that are energy efficient while being computationally simpler than the optimal algorithms. We simulate our algorithms and show that while optimal cooperation and link selection can reduce energy consumption by almost an order of magnitude compared to non-cooperative approaches, our simple heuristics achieve similar energy savings while being computationally efficient as well. We present a route planning technique solely based on the concept of node contraction. The nodes are first ordered by 'importance'. A hierarchy is then generated by iteratively contracting the least important node. Contracting a node υ means replacing shortest paths going through v by shortcuts. We obtain a hierarchical query algorithm using bidirectional shortest-path search. The forward search uses only edges leading to more important nodes and the backward search uses only edges coming from more important nodes. For fastest routes in road networks, the graph remains very sparse throughout the contraction process using rather simple heuristics for ordering the nodes. We have five times lower query times than the best previous hierarchical Dijkstra-based speedup techniques and a negative space overhead, i.e., the data structure for distance computation needs less space than the input graph. CHs can be combined with many other route planning techniques, leading to improved performance for many-to-many routing, transit-node routing, goal-directed routing or mobile and dynamic scenarios. Maximizing information gathered within a budget is a relevant problem for information gathering tasks for robots with cost or operating time constraints. This problem is also known as the informative path planning (IPP) problem or correlated orienteering. It can be formalized as that of finding budgeted routes in a graph such that the reward collected by the route is maximized, where the reward at nodes can be dependent. Unfortunately, the problem is NP-Hard and the state of the art methods are too slow to even present an approximate solution online. Here we present Randomized Anytime Orienteering (RAOr) algorithm that provides near optimal solutions while demonstrably converging to an efficient solution in runtimes that allows the solver to be run online. The key idea of our approach is to pose orienteering as a combination of a Constraint Satisfaction Problem and a Traveling Salesman Problem. This formulation allows us to restrict the search space to routes that incur minimum distance to visit a set of selected nodes, and rapidly search this space using random sampling. The paper provides the analysis of asymptotic near-optimality, convergence rates for RAOr algorithms, and present strategies to improve anytime performance of the algorithm. Our experimental results suggest an improvement by an order of magnitude over the state of the art methods in relevant simulation and in real world scenarios.