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.
Online path finding algorithms is designed to deal with uncertainties and emergencies during the flight. @cite_6 presents some common online algorithms, including potential field approach and particle swarm optimization (PSO). They have advantages and disadvantages: potential field approach are simple and straightforward to understand, but may easily fall into local optimum should no adjustment to algorithm is done. PSO is also intuitive, but it can also be too computationally expensive to reroute in real-time. @cite_3 proposed a predictive control scheme that use shared knowledge between nearby UAVs to find collision-free route, meanwhile considering the dynamics of UAV itself.
@cite_3 @cite_6
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The main focus of this paper is to develop an optimization method for the automatic fighter tracking (AFT) problem. The AFT problem is similar to a general evader-pursuer maneuvering automation problem between the dynamic systems of two highly interactive objects. This paper proposes a particle swarm optimizer-based variable feedback gain controller (PSO-based VFGC) for dealing with AFT problems. The PSO-based VFGC is designed to obtain the control value of a pursuer through an error-feedback gain controller. Once conditions of system closed-loop stability have been satisfied, the optimal feedback gains can be obtained through PSO, and the actual control values can be derived from the obtained values. Simulation results confirm the capabilities of the proposed method by comparing the results against two other methods in the field: the weight matrix value defined Ricatti equation, and the linear matrix inequality (LMI) based linear quadratic regulator (LQR). The performance of the proposed method is superior to that of its alternatives. Abstract This paper presents an optimized reconfigurable control design methodology by separating control commands distribution task from flight controller for different types of fault handling. The proposed strategy improves the flight control performance in normal and fault situations. The particle swarm optimization (PSO) based multi-input multi-output (MIMO) linear quadratic regulator (LQR) is used to produce virtual command signals. A modified weighted pseudo-inverse (WPI) based cascaded re- allocation technique is employed for effective implementation of commands to redundant control surfaces in a realistic nonlinear aircraft benchmark model. Control surface fault modelling is performed for the evaluation of optimized reconfiguration based modular flight control strategy. Simulation results show that acceptable fault tolerant control (FTC) performance can be achieved by using swarm intelligence based optimization technique for modular control design. This paper presents a novel path planning algorithm for autonomous, uncertainty-aware exploration and mapping of unknown environments using aerial robots. The proposed planner follows a two-step, receding horizon, belief space-based approach. At first, in an online computed tree the algorithm finds the branch that optimizes the amount of space expected to be explored. The first viewpoint configuration of this branch is selected, but the path towards it is decided through a second planning step. Within that, a new tree is sampled, admissible branches arriving at the reference viewpoint are found and the robot belief about its state and the tracked landmarks of the environment is propagated. The branch that minimizes the expected localization and mapping uncertainty is selected, the corresponding path is executed by the robot and the whole process is iteratively repeated. The proposed planner is capable of running online onboard a small aerial robot and its performance is evaluated using experimental studies in a challenging environment. Aiming at solving the problems of unsatisfactory routes planning that are suboptimal to optimal routes planning and dissatisfactory real time routes planning and routes planning of multiple unmanned aerial vehicles (UAVs) cooperation, a cost function of multiple UAVs routes planning is presented after modeling the primary factors influencing on multiple UAVs route planning. Then, Based on particle swarm optimization (PSO) algorithm, this paper presents the approaches of static three-dimensional routes planning and dynamic three-dimensional routes planning for multiple UAVs cooperation. The approaches can also be used to plan a single UAV route. Using these approaches, an emluator is designed and some experiments are made, and the results demonstrate that the approaches can satisfy the requests of the multiple UAVs routes planning.