Water Saving Control of Turfgrass Irrigation Robot Using Genetic Simulated Annealing Algorithm

Han, Bei

doi:https://doi.org/10.1155/2021/1082304

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Volume 2021 | Article ID 1082304 | https://doi.org/10.1155/2021/1082304

Water Saving Control of Turfgrass Irrigation Robot Using Genetic Simulated Annealing Algorithm

Bei Han¹

Academic Editor: Fazlullah Khan

Received03 Jun 2021

Accepted08 Jul 2021

Published16 Jul 2021

Abstract

Artificial intelligence through the robotic system offers a solution to the quest for an autonomous system with high cutting efficiency for lawn mowing. Because of the current trimming and maintenance operations on grasslands and gardens, it is essential to develop autonomous and efficient lawn pruning electromechanical equipment. This paper describes the design and construction of a high-performance automated grass trimming and irrigating robot. This device cuts and irrigates grass automatically with little human intervention. A genetic simulated annealing algorithm was employed to optimize motor parameters, specifically design a set of mowing mechanisms and mowing height adjustment system. The prototype was tested, which mainly includes the running status evaluation of the walking module, the mowing module, the cutter head lifting module, and the collision detection module. This robot can save water while watering the lawns, reduce labor costs, and improve mowing efficiency. We note that the proposed system can be implemented on a large scale under natural conditions in the future, which will be helpful in robotics applications and cutting grass on lawns and playing grounds.

1. Introduction

Lawn mowing is the process of trimming grasses to a specific height, usually for sanitation, decoration, and sport purposes [1–3]. At present, lawn trimming and irrigating on the market mainly rely on manual operation, which has the disadvantages of large labor, large environmental pollution, and high repetitive operation. Moreover, it is not easy to adapt to the needs of the fast-developing lawn industry [4]. Various grass-cutting and irrigating equipment have been developed over the years, such as spiral-rope cutter, reel-blade grass cutter, and gas-powered mower. However, they pose some challenges relating to operational efficiency. A lawnmower is a tool like scissors and a machine that consists of unified powered and electrical systems with an effective controlling mechanism.

With the introduction of intelligence in lawn mowing and irrigating robots, various intelligent lawn mowing robots have emerged in an endless stream. However, most of them have similar functions [5]. They can all automate functions such as starting, charging, mowing, and watering the grass. According to the degree of automation, the development of robots can be roughly divided into three stages: the initial stage of development, the intermediate stage of semi-intelligence development, and the current stage of higher intelligence [6, 7]. The Bosch Indigo mowing robot produced in Germany has many improvements compared with the traditional grass industry robot. It has improved the original working model of random mowing [8]. The robot uses sequential rows to work. The unique grass-cutting path planning dramatically improves mowing efficiency. The Indigo mowing robot also has the function of independent charging [9–11]. After the charging is completed, it can autonomously return to the original stopped area to continue cutting. Grass homework, the lawnmower robot developed by Ohio University in the United States, is highly intelligent [12, 13]. The overall structure is made of alloy materials, and a DC motor drives the motion device. The working speed can reach 16 km/h. In addition, the robot uses a differential global positioning system in the path planning process, which ensures the accuracy of the movement during the navigation process and improves the movement efficiency of the robot.

Derander et al. [14] designed a smart lawn grass trimming robot which does not require any boundary cable for its operation to reduce the cutting cycle time and operational cost. The author in [15] developed a mechanical push lawn with the double cylinder for blade spinning to increase the system’s capacity and operational efficiency. Moore [16] performed the measurement and analysis of a lawnmower in terms of its performance and noise level. Many other works have been reported on the design of autonomous lawnmowers and systems. It requires installing boundary wire around the perimeter of the lawn to keep the machine within the predetermined area and on a path [17–19]. Moreover, some existing mowers use a random path that requires more time to mow with low cutting efficiency and do not produce the typical even and clean mowing lines which characterize a well aesthetically manicured lawn. Furthermore, some existing lawnmowers operate semiautonomously, relying on human intervention for some degree of operation, thus, increasing the cutting cycle time, human drudgery, and labor cost.

From the perspective of adapting to market demand, this article aims to develop a prototype for high-performance autonomous grass trimming and irrigating robot. This robot can save water while irrigating the lawns, reduce labor costs, and improve mowing efficiency. This means that the garden machinery manufacturing industry will face more opportunities. Furthermore, the proposed grass trimming robot can adapt to domestic development needs, increase international market share, and provide good economic and social benefits.

The rest of the paper is structured as follows. Section 2 provides an overview of irrigating and grass trimming robots’ general architecture. Then, in Section 3, the simulated annealing algorithm is discussed. Then, Section 4 describes different results, and finally, the conclusion is given in Section 5.

2. Architecture of the Grass Trimming Robot

The successful development of intelligent lawnmower robots dramatically saves human resources and energy and reduces environmental pollution to a certain extent [14–16]. Compared with traditional hand-operated lawnmowers, the intelligent lawn mower robot has low noise and low dust characteristics. As a result, it reduces noise pollution to the surrounding environment. It has less impact on people’s health and the environment [17, 18]. The grass trimming and irrigating robot motion control system is comprised of three parts: the driving wheel motor, the cutter head motor, and the elevator motor. The robot’s walking mechanism relies on the rear wheels on both sides to drive and perform stop, straight and turn movements by controlling the motor speed on the left and right sides [19, 20]. The driving wheel uses a brushless DC motor drive system, which is convenient for the control and speed adjustment of the mobile platform. The rotary motion of the mowing blade is called the mowing mechanism of the mowing robot, which is required to regulate the speed of the motor and control start and stop. The schematic diagram of lawn grass robot control is shown in Figure 1.

The path planning method of mobile robots is based on the robot’s specific control architecture and different environmental characteristics. For mobile robots with different environments, different planning methods have their advantages and disadvantages [21]. At present, there is no general planning method that can adapt to any system in various environments. Therefore, it is necessary to choose several different planning methods to combine their respective advantages to obtain the best planning effect [22]. In the overall design of the autonomous grass trimming and irrigating robot, this section will propose the design of the autonomous grass trimming robot. It clarifies the designed autonomous robot’s functions and proposes a prototype according to actual requirements [23].

3. Water-Saving Control Based on Simulated Genetic Annealing

The simulated annealing algorithm is a random optimization algorithm based on the Monte Carlo iterative solution strategy. It has been widely used in engineering applications. During a Monte Carlo simulation, values are sampled randomly from the input probability distributions. Each set of samples is called an iteration, and the resulting outcome is recorded. The idea comes from the similarity between the annealing process of solid matter in physics and general combinatorial optimization problems. The temperature at the beginning of the simulated annealing algorithm is very high [24, 25]. As the temperature continues to drop, it randomly searches for the optimal global solution of the objective function in the solution interval. This can ensure that the optimal local solution probabilistically jumps out and finally reaches the optimal global solution [26]. Theoretically, the algorithm has probabilistic global optimization performance. It can accept inferior solutions to a limited extent to not fall into the local optimum. The rule of thumb requires the algorithm to search for the most significant possible solution space in a reasonable time, with only a sufficiently large time [27, 28]. This may lead to too long execution time, making the simulated annealing algorithm infeasible. The theoretical analysis points out that the algorithm should be quasi-balanced at the beginning.

When the control parameter decays slowly, the two adjacent values are smoothly distributed and close.

Therefore, on the one hand, if the quasi-balance is reached, only a small amount of transformation is sufficient to restore the quasi-balance. Therefore, a shorter length of the Markov chain can be selected to reduce the execution time [29, 30].

On the other hand, a slight attenuation of the control parameter may also increase the number of iterations of the algorithm. It can be expected that the algorithm process will accept more transformations, search a more extensive range of solution spaces, and return a higher quality final solution.

The main disadvantage of the simulated annealing algorithm is that it takes a long time to find a high-quality approximate optimal solution significantly when the problem size inevitably increases [31]. However, proper selection of the cooling schedule can effectively improve the performance of the algorithm.

We combine simulated annealing and genetic algorithm to provide a general framework for solving complex system optimization problems. It does not depend on the domain and type of the problem. The genetic algorithm is used to deal with the optimization problem. The feasible solution of the optimization problem corresponds to everyone in the group [32–34].

The objective function of the optimization problem corresponds to the environment in which the group is located, and the function value corresponds to the fitness of the individual to the environment. The chromosomes are encoded in binary strings in the algorithm, and each encoded string is a candidate solution group. There are multiple chromosomes, that is, a group of candidate solutions [35].

Chromosomes are the main objects of evolution. Like biological evolution, crossover and mutation make the final solution global. For a practical application problem that requires optimization calculation, the genetic algorithm to solve the problem can generally be constructed according to the following steps:

The simulated annealing algorithm has strong local searchability. It can jump out of the local minimum and tend to the global optimum [36, 37]. In addition, it has good asymptotic convergence, simple description, flexible application, suitable for parallel calculation, and overcomes initial value dependence. The other advantages can effectively solve the problem with nondeterministic polynomial-time (NP) complexity [38, 39]. However, at the same time, the simulating annealing algorithm requires a higher initial temperature and a longer sampling step and a larger time.

4. Water-Saving Control Process Test and Analysis

From the perspective of adapting to market demands, this article aims to develop high-performance autonomous grass trimming and irrigating robots [40, 41]. We performed experiments and analyzed the prototype, which mainly included the running status evaluation of the walking module, the mowing module, the cutter head lifting module, and the collision detection module [42].

4.1. Technical Performance Test

Technical performance testing is an indispensable part of the research process of autonomous irrigating and grass trimming robots. Tests are used to verify the feasibility of the overall scheme and test the reliability of each module of the prototype and the rationality of the mechanical structure design [43]. After the overall design plan was determined, the 3D modeling of the autonomous irrigating and grass trimming robot was carried out with solid works software. Figure 2 describes the simulation test data distribution. The distribution of data shows that the system has the best performance for water-saving and grass cutting. Figure 3 shows the test results of the vibration frequency of the grass-cutting and irrigating robot using four different methods [44].

The walking module is an integral part of the autonomous irrigating and grass trimming robot system. The high-performance walking system is an essential guarantee for the irrigating and grass trimming robot to complete the mowing work efficiently. The stability, accuracy, and flexibility of its actions will directly affect the robot’s performance [45]. Figure 4 describes the water-saving control effect in three different grass lawns and environments when the frequency is 100 Hz.

The robot provides the save performance even if the environment is changing and that the change of environment does not affect the efficiency of the proposed model. Figure 5 provides a comparison of the water-saving efficiency between the proposed optimized robotic machine and the ordinary machine. For 200 robot data samples, the water-saving rate of the proposed grass trimming and irrigating robot is 0.7%, whereas that of the ordinary lawn robot is 0.6%. Similarly, for 800 data samples, the proposed model has a water-saving rate of 0.8%, which is greater than the ordinary machine having a water-saving rate of 0.5%. This confirms that the proposed model has an improved water-saving effect compared with traditional irrigating and grass trimming robots.

In addition to static positioning, the lawnmower robot is based on differential positioning technology. Therefore, it needs to ensure that the robot changes the position dynamically during the working process, which requires the system to achieve high dynamic accuracy. Compared with traditional hand-operated lawnmowers, the intelligent lawn mower robot has low noise and low dust characteristics. Therefore, it reduces noise pollution to the surrounding environment and has less impact on people and the environment. Although the mowing robot designed in this study is an outdoor mobile device, its evaluation in the real environment needs further verification.

5. Conclusion

This article comprehensively and profoundly analyzes the actual market demand of trimming and watering robots and carefully studies the current development status of automatic lawnmower robots. A compact and economically feasible overall plan for an autonomous grass trimming and irrigating robot is proposed after a comprehensive study of the plan of the autonomous robot based on the dynamics and kinematics analysis of the outdoor wheeled mobile robot. It is combined with the work requirements of the grass trimming and irrigating robot. The overall layout and car body of the robot are studied. This paper describes the design and construction of a high-performance automated grass trimming and irrigating robot. A genetic simulated annealing algorithm was used to enhance motor parameters, specifically design a set of mowing apparatus and mowing height adjustment system. The prototype was evaluated, including the running status evaluation of the walking module, the mowing module, the cutter head lifting module, and the collision detection module. The proposed robot can save water while watering the lawns, reduce labor costs, and improve mowing efficiency. The proposed system will be implemented on a large scale under real conditions in the future. It will be useful in robotics applications and cutting and irrigating grass on lawns and playing grounds.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The author declares that there are no conflicts of interest.

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Copyright © 2021 Bei Han. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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