As many potted plants are placed at long distances from each other in public areas, watering them leads to much manpower consumption, while using large-scale irrigation in this situation is unsuitable. Since plants are relatively stationary, it’s appropriate to use robots to water them, instead of human labor. To make watering indoor plants easier, in this article, an Arduino-based wheeled robot for plant-watering in public places is designed. 3D modeling, circuit design, programming and verification experiments have been conducted to complete this design. The achievement includes a 3D model, a control circuit, an executive system and Arduino code. In this research, the cost issue has been taken into consideration. The designed robot is relatively economical, as it only uses some basic inexpensive components. The robot has the capacity to realize route-tracking for navigation, detect the plant right before it and spray water accurately, with various sensors. The existing design can achieve predetermined goals at present, and improvements are going to be undertaken in the future.

Lower extremity rehabilitation-assisted exoskeleton robots bring together multiple disciplines such as biomechanics, control engineering, robotics, and computer science. The main role of lower extremity rehabilitation exoskeleton robots is to help patients and rehabilitators to maintain or restore the mobility of lower extremities, therefore, proper research and discussion of human gait analysis is the basis for establishing and improving such exoskeleton robots. To analyse and evaluate the positive effects of modern technology on stroke patients, the development of human gait capture and simulation technologies will be mainly summarized, the latest human gait capture and simulation technologies will be classified and evaluated. And through a comprehensive review and analysis of research advances in these areas, the usefulness of lower extremity rehabilitation-assisted exoskeleton robots for stroke populations is evaluated. This paper is informative in studying the usefulness of a lower limb exoskeleton robot combined with gait capture and simulation technology for rehabilitation training of stroke patients.

Soft robots have become an emerging field of research with the advancement of science and technology. It has the advantages: flexible, soft and fast. It has good results within narrow space work and has great research potential in disaster relief, exploration and medical treatment. It has a theoretically infinite number of degrees of freedom, but degrees of freedom and actuators do not always correspond in a straightforward one-to-one manner, making it difficult to achieve accurate, control-accurate modelling. It is worth exploring how modelling techniques and sensor control techniques can be optimized to improve control accuracy and achieve human-machine interaction. In addition, most actuators currently use a single drive method, which leads to the limitations of redundant degree of freedom control. Combining multiple drive methods while ensuring drive control accuracy and drive force is also a future research direction. In this paper, the current state of control and drive research in soft robots will be introduced. Then three control modelling methods - modelled, modeless and hybrid control will be discussed in detail. Three common drive methods will be mentioned - fluid drive, SMA drive and EAP drive. A prospect on the control methods and drive mode of soft robots will be given. This paper is useful for robotics research.

Multi-legged robots have become a research hotspot in the field of robotics, and they have a wide range of applications in military, rescue, and detection fields. The current state of research in the field of multi-legged robots is reviewed in this paper, and the types of quadrupedal robots, hexapod robots, and biped robots are introduced in terms of multi-legged robot types. In terms of the technology of multi-legged robots, technology of multi-legged robots and the technical architecture are introduced. Some practical applications in the field of multi-legged robots are also presented. The comprehensive analysis shows that multi-legged robots have strong adaptability and flexibility, but there are still some challenges in control and stability. In the future, a focus will be placed on the enhancement of environment perception and intelligent control of multi-legged robots, which will result in an increase in their reliability and performance when they are applied in practical scenarios.

This paper discusses the problem of local path planning for autonomous vehicles. This article introduces pruning strategies and their related map construction and data processing. Next, a forward path planning strategy was introduced, and a universally applicable path selection method was provided. The value of forward prediction strategy for autonomous driving technology was demonstrated by comparing it with ordinary mobile robot path planning algorithms. Then, the optimization of the forward paths through a pruning strategy reduced the time required for updating data by the algorithm introduced in the article. This article refers to the data provided in two literatures, compares and analyzes the advantages and disadvantages of two path planning schemes, and attempts to combine some of their advantages. At the end of this article, the calculation results based on MATLAB mathematical modeling are provided, and the rationality analysis of this path planning strategy is provided as well. Based on these analyses, this article provides suggestions for optimizing path planning algorithms.

The Unmanned Aerial Vehicles (UAVs) have played a much more important role in daily life, ranging from entertainment and military use, in which case UAV swarm is adopted as means to lower the operation cost, as well as to compensate for the insufficient performance of a single drone. However, sharp distinctions between individual drones and UAV swarm exist not only in the field of controlling, but in the area of aerodynamics as well. To conclude, the operator, instead of a team of engineers, needs to perform severe tasks with the help of several drones which act as a swarm rather than interfere with each other. Meanwhile, in order to improve the efficiency of UAV swarm, different formations are looked into from different fields, mainly aerodynamics and control engineering. This review will mainly focus on the aerodynamics performance of formations of swarming UAVs, both in close formation flight and extended formation flight. Comparisons will also be made to show the merits and demerits of these formations, providing convenience for path planning of different tasks.

With the development of autonomous driving technology, its applications permeate many aspects of work and life, providing convenience while reducing labor costs. Path planning has always been important for autonomous driving, where APF is widely used thanks to its simplicity of calculation and effectiveness. However, there’re still problems existing such as local minima, influenced by initial positions and parameters, and so on. In this study, a better approach to solving the local minimum issue is suggested. Firstly, the odometry method is used for the determination of falling into local minima by saving and computing the relationships between adjacent steps. Subsequently, a variable step length method is designed for escaping local minimum points and bypassing obstacles in front. The feasibility and robustness of the method were verified by simulations, and this method proved capable of solving the local minima and planning a reasonable trajectory.

With the development and widely use of unmanned aerial vehicles (UAVs) in recent years, the development of efficient path planning methods for automation has become crucial. Obstacle avoidance and path planning are the key components of UAV path planning. This article provides an overview of obstacle avoidance and path planning techniques for UAVs based on the artificial potential field method (APF method). This article begins with the explaining the principles of artificial potential field on this basis discusses its advantages and limitations. The article then summarizes the improvement strategies proposed by previous researchers to address issues like local minimum values and unreachable targets, such as introducing a new repulsive potential energy function, combining APF with other planning methods, and utilizing flow functions. Furthermore, it presents examples of the application and the performance of usage of these techniques in both static and dynamic environments. Based on this, the prospects and developing trend of UAV obstacle avoidance methods based on artificial potential field are foresee, such as combined with DRL and deep learning.

With the advancement of unmanned aerial vehicle (UAV) technology and its widespread use in many facets of manufacturing and daily life, the need for UAV mission automation is becoming more and more practical. In order to improve the automatic obstacle avoidance and path planning performance of UAVs, this essay proposes an optimized route planning algorithm based on the artificial potential field (APF) method, which have solved the typical issue of the APF. This method chooses to conduct a pre planning trajectory of the UAV based on a rapidly expanding random tree (RRT). The pre-planned path will be split into continuous particles, and then generating intermediate waypoints. A nearby waypoint offers a gravitational force to aid the UAV in escaping the local minimum when it enters it. At the same time, taking the distance from the UAV to the obstacle and the radius of influence of the obstacle itself into consideration, dynamically adjust the gravitational and repulsive coefficients, set up a non-gravitational zone around the obstacle, which is beneficial for the UAV to elude the obstacle. And set up a repulsive limited action zone to reduce unnecessary turns in the trajectory to achieve the effect of path optimization. Considering the difficulty of single UAV missions in most cases, this paper discusses cooperative flight path planning for multiple UAVs into consideration.

The optical fiber amplifier doped with rare earth elements has the characteristics of high gain, high doping concentration and short length. Compared with other fiber optic systems, the fiber used is shorter, also known as lumped fiber amplifier. At present, Er, Pr, Tm, Nd and Yb doped fiber amplifiers and lasers are mainly studied more. To further extend the transmission distance, improve the transmission quality and increase the transmission capacity, it is very important for the research of fiber amplifier. In this research, using a two-level structure, we investigate the maximum of the peak gain of the gain spectrum of a thulium-doped broadband fiber amplifier in the 1800-2000 nm wavelength range. Signal gain is inversely proportional to fiber length, doping concentration, and pump power in both the absorption spectrum and the emission spectrum, as shown by the relationship diagram of signal gain with these variables. And through the genetic algorithm data optimization, we get that when the fiber length is 2.2 µm, the maximum gain is 42.7 dB.

Optical fiber amplifiers mainly use rare earth elements as dopants. In the past 20 years, with the research on plastic optical fiber amplifiers doped with different natural elements (such as erbium and thulium), the performance of optical fiber amplifiers has been significantly improved. However, the application potential of natural elements has reached its limit. To improve optical communication, researchers are working on a fiber amplifier with a better flat gain and more intense light. In this paper, the quantum dot fiber amplifier is treated as a three-level structure, and a genetic algorithm is used to optimize the quantum dot fiber amplifier to maximize the total output power. In this study, by solving and analyzing the rate and power propagation equations of the numerical model, the gain spectrum of the amplifier at a wavelength of 1200~1700nm is obtained as a function of fiber length and doping concentration. Draw three-dimensional images based on fiber length, doping concentration, and ASE power axis to find the maximum power at the highest point of the image. Then, combined with the optimization results of the genetic algorithm, the maximum value of the total power is obtained.

The gain spectrum of the neodymium-doped wideband fiber amplifier was modeled and simulated by Matlab, and the intelligent optimization algorithm (genetic algorithm) included in Matlab was used to optimize the fiber length and doping concentration to maximize the peak gain (find the optimal fiber length and doping concentration within the set range). According to the set signal optical wavelength range (1300 nm~1400 nm) and the corresponding neodymium ions four-level transition structure, the pump optical wavelength (800 nm) is designed and the four-level structure rate equation and power propagation equation are established, and Matlab programming calculates the change of gain with fiber length, neodymium ions doping concentration and pump optical power. According to the obtained gain curve, it can be inferred that under the parameter range set in this study, the gain of the neodymium-doped fiber amplifier increases significantly with fiber length and neodymium ions doping concentration, while the pump optical power has almost no effect on the gain value.

With an increasing focus on environmental protection and sustainability, electric vehicles have become the mainstream trend for future automotive development. However, the development of electric vehicles has been limited by the bottleneck of charging technology. As a result, wireless charging technology for electric vehicles is viewed as an important solution, featuring high efficiency, high power density, and low cost. Specifically, three-phase wireless charging technology offers superior advantages such as high power density, low magnetic field leakage, and high coupling tolerance, making it the major trend for future development. This paper provides an overview of wireless charging technology for electric vehicles, while highlighting the benefits of three-phase technology over single-phase alternatives.

With the innovation and advancement of technology, the research in the field of transformable robots have shown explosive growth and are moving towards intelligence and diversification. Transformable robots can transform their mobile mechanisms according to different terrains for obstacle surmounting, with high flexibility, strong adaptability, and scalability. Thus, transformable robots are widely used in fields such as reconnaissance, rescue, and military. In this article, the characteristics of different obstacle surmounting mechanisms of transformable robots are analysed. Based on three traditional obstacle surmounting mechanisms: wheeled, tracked, and legged, this article introduces the concepts and advantages of combined obstacle surmounting mechanisms and emerging soft and biomimetic robot obstacle surmounting mechanisms. After elaborating on the current research status and partial applications of composite transformable obstacle surmounting robots, the key technologies and difficulties in the research of obstacle surmounting mechanisms for transformable robots are summarized. In addition, this article provides prospects for the application of transformable obstacle surmounting robots in medical and agricultural fields.

Miniature unmanned aerial vehicle (MAV) is more and more widely used in daily life. The small size of MAVs brings them many advantages like high portability and manoeuvrability. MAVs are playing important roles in many aspects because of their own catachrestic and advantages. There are many kinds of MAVs such as single-rotor MAV, multi-rotor MAV, fixed-wing MAV and bionic MAV. Each of them has its own advantages and drawbacks. The applications of them are decided by these characteristics. In order to better design MAV, in this paper, the advantages and disadvantages of the MAVs are analysed. The four most common kinds of MAVs are discussed in this paper. They have differences in structure and characteristics. The differences between these aspects are displayed in this paper. The suitable application scenario for each kind of MAV is provided based on its own peformance characteristics. This paper may offer a reference for MAV design.

As a typical route planning algorithm, the APF method has been widely used in robotics, autopilot and other fields. However, it also has weakness in local optimal point. In computer science and mathematics, stochastic perturbation is an accepted approach that can assist optimization algorithms leave the local optimal solution and achieve the global optimal solution as well as improve robustness and adaptability in specific application situations. therefore, the problem can be successfully solved by using stochastic perturbation. In this article, the traditional APF improved through stochastic perturbation. When the robot is running in an APF, the distance between the robot and the target point is calculated at all times. If the distance between the robot and the target point remains unchanged for a period of time or if the stop point oscillates, it is determined whether the robot has reached the target point at this time. If not, it is determined that the robot has fallen into a local optimal point at this time. After completing the judgment, stochastic perturbation is applied to the robot, causing it to jump out of the local optimal point and continue to navigate to the target point. The results of the comparison between the paths generated by the traditional method and the improved method validate the effectiveness of this method.

The artificial potential field method (APF), a highly effective navigation technique, is currently utilized extensively in this area due to the rapid development of unmanned automatons. Traditional APF, however, have several shortcomings, including the issue of unreachable object points and the propensity to sink into local minima that prohibit the automaton from moving on. In this paper, a two-part improved APF model is created to address these issues. First, by including additional constraints, the repulsive field model at the stumbling block is enhanced to address the issue that the object point is impassable when too close a distance between the two stumbling blocks. Secondly, a new potential field is introduced to help the automaton walk out of the local minima. Analogue simulation show that the methods mentioned above can solve these problems better and make the route planning of unmanned automatons come true.

Spacecraft is the primary means of transportation for human exploration of outer space. With the advancement of science and technology, existing space exploration can no longer satisfy people's desire to explore, in the future, further and deeper space exploration is the goal of development. And the safety of spacecraft navigation in space requires the development of a path planning system. Although there have been many kinds of methods for path planning at this stage, all of them have their strengths and specialize in planning directions, but they also have defects. Based on this, this study improves the traditional artificial potential field method, by adjusting its repulsive field calculation and combining it with the RRT algorithm. The improved method can avoid the local minimum problem of the traditional artificial potential field and generate paths to meet the requirements of spacecraft. The effectiveness of the algorithm is verified by simulation experimental results.

Nowadays, with the development of robotics-related technology, its applications permeate many aspects of work and life; In product manufacturing and assembly, tech companies switch from manual to robot automation which improves the production volume and reduces the assembly time. In the tertiary sector including health and social work, the robots learn how to interact with people to meet specified requirements. Path planning constitutes a critical module of robotics engineering that aims to provide the optimal solution for the robot to reach its target point. The artificial potential field methods, refers to APF, are widely used to realize path planning due to their simplicity of calculation and effectiveness in obstacle avoidance. However, the traditional artificial potential field method features the local minimum and oscillation, and unreachable target point problems that make it hard for robots to reach the target point. Based on the weaknesses, an improved version of the gravitation and repulsion force function was introduced in this paper. In addition, the concept of safety distance also contributed to the path planning for robots. Through the simulation experiment, it was shown that the improved APF algorithm successfully addressed the local minima and unreachable target point problem, which could navigate robots to arrive at the destination in both 2D and 3D space by avoiding collision with obstacles.

Currently, the artificial potential field technique is commonly used in vehicle obstacle avoidance and route planning. However, this method may lead to local minima where the gravitational force and repulsive force are equal during the path planning process, and thus the target location cannot be reached. This paper suggests an approach to enhance the artificial potential field method in light of this. First, the calculation method of the attraction field in the traditional artificial potential field is modified for resolving the issue of unreachable targets. Second, for the local minimum, the path calculation is recovered by modifying the repulsive force range of obstacles and creating a virtual obstacle point, and applying an additional force to get rid of the gravitational force and repulsive force balance. The simulation results check the effectiveness of the designed method, which can solve the cases of unreachable targets and local minima and produce reasonable planned paths.