Route-Planning Method for Plant Protection Rotor Drones in Convex Polygon Regions

Evaluation of landfill leachate biodegradability using IOT through geotracking sensor based drone surveying

In specific this work focuses on designing and developing a trial model first of its kind which is chemically non reactive using Urea Formaldehyde (UF) and a value added flat board that is to be prepared from solid waste. The display setup is to be monitored by Arduino Controller which incorporates the assessment of numerical factors and measurable compositions in the board. The compilation of manufactured goods from organic waste is a course of development network that is centrally operated by Radio Frequency Identification (RFID) tools. Preservation of database from the organic fraction is managed through software guidelines, the fifth generation radio technology with lower economic can cover communication over a larger radio spectrum with the support of ultra high frequency RFID Tags and systems, which assure the most favourable management of valuable products from the storage unit. Sensors attached with board and cloud platform supports collection of data from the huge solid waste yard. The current leachate management has a desire to suggest a strong resolution in handling solid waste that is elaborated with a novel methodology, where we suggest the radiofrequency identification of leachate monitoring by the use of modern versions of unmanned aerial vehicle the drones. At some stage in neutralizing the toxic waste the risk of life may be eliminated and accumulated waste were identified and with the support of advanced techniques and instruments like drones are used in managing wastes.

Robust control strategy for multi-UAVs system using MPC combined with Kalman-consensus filter and disturbance observer

The stability of formation flight is not only sensitive to external disturbances but also to data observed and transferred between sensors and unmanned aerial vehicles (UAVs). A multi-constrained model predictive control (MPC) strategy, combined with Kalman-consensus filter (KCF) and fixed-time disturbance observer (FTDOB) is developed for the formation control of multiple quadrotors here Firstly, KCF is used to effectively fuse the data shared in the formation with noise and uncertainty, which improves the applicability and robustness of the formation in complex environments. Secondly, FTDOB is able to estimate the external disturbances suffered by the quadrotor in a fixed time and provides real-time compensation for the controller. On this basis, an improved MPC (IMPC) is designed for each UAV of the formation, which improves the computational efficiency while ensuring the asymptotic stability of the system. Eventually, the capability and effectiveness of the proposed strategy are verified by simulation in terms of disturbance rejection and noise suppression, as well as good trajectory tracking of the formation.

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields

The complex environments and weak infrastructure constructions of hilly mountainous areas complicate the effective path planning for plant protection operations. Therefore, with the aim of improving the current status of complicated tea plant protections in hills and slopes, an unmanned aerial vehicle (UAV) multi-tea field plant protection route planning algorithm is developed in this paper and integrated with a full-coverage spraying route method for a single region. By optimizing the crossover and mutation operators of the genetic algorithm (GA), the crossover and mutation probabilities are automatically adjusted with the individual fitness and a dynamic genetic algorithm (DGA) is proposed. The iteration period and reinforcement concepts are then introduced in the pheromone update rule of the ant colony optimization (ACO) to improve the convergence accuracy and global optimization capability, and an ant colony binary iteration optimization (ACBIO) is proposed. Serial fusion is subsequently employed on the two algorithms to optimize the route planning for multi-regional operations. Simulation tests reveal that the dynamic genetic algorithm with ant colony binary iterative optimization (DGA-ACBIO) proposed in this study shortens the optimal flight range by 715.8 m, 428.3 m, 589 m, and 287.6 m compared to the dynamic genetic algorithm, ant colony binary iterative algorithm, artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO), respectively, for multiple tea field scheduling route planning. Moreover, the search time is reduced by more than half compared to other bionic algorithms. The proposed algorithm maintains advantages in performance and stability when solving standard traveling salesman problems with more complex objectives, as well as the planning accuracy and search speed. In this paper, the research on the planning algorithm of plant protection route for multi-tea field scheduling helps to shorten the inter-regional scheduling range and thus reduces the cost of plant protection.

Genetic Algorithm and Greedy Strategy-Based Multi-Mission-Point Route Planning for Heavy-Duty Semi-Rigid Airship

The large volume and windward area of the heavy-duty semi-rigid airship (HSA) result in a large turning radius when the HSA passes through every mission point. In this study, a multi-mission-point route planning method for HSA based on the genetic algorithm and greedy strategy is proposed to direct the HSA maneuver through every mission point along the optimal route. Firstly, according to the minimum flight speed and the maximum turning slope angle of the HSA during turning, the minimum turning radius of the HSA near each mission point is determined. Secondly, the genetic algorithm is used to determine the optimal flight sequence of the HSA from the take-off point through all the mission points to the landing point. Thirdly, based on the optimal flight sequence, the shortest route between every two adjacent mission points is obtained by using the route planning method based on the greedy strategy. By determining the optimal flight sequence and the shortest route, the optimal route for the HSA to pass through all mission points can be obtained. The experimental results show that the method proposed in this study can generate the optimal route with various conditions of the mission points using simulation studies. This method reduces the total voyage distance of the optimal route by 18.60% on average and improves the flight efficiency of the HSA.