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The multi-locomotion robot (MLR), including bionic insect microrobot, bionic animal robot and so on, should choose different locomotion modes according to the obstacles it faces. However, under different locomotion modes, the power consumption, moving speed, and falling risk of MLR are different, and in most cases, they are mutually exclusive. This...
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... grid of the map contains environmental information of its location. There is an example of a 10 × 10 grid map, as shown in Figure 4, in this example, black grids represent impassable obstacles, white grids are traversable. The position of each grid can be obtained by index or coordinate values, which can be converted to each other as Equation (1): ...
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... But there are also some drawbacks, such as complex control variables. In [6], a map operator is proposed, which divides obstacles in a map into multiple regions and stores corresponding index values for each region. The species diversity operator is also proposed to achieve species diversity and avoids falling into local optimum conditions. ...
... (3) We select the chromosomes of both parents to cross with a certain probability to produce offspring; (4) The offspring are mutated according to a certain probability; (5) Steps 2, 3, and 4 are repeated; (6) The loop exits when the number of iterations is reached; (7) Binary encoding is set. One of the binaries represents information in both states and binaries long enough to represent all the features of a chromosome. ...
Due to the shortcomings of GA in path planning, there are too many control variables and it is easy to fall into local areas. We introduce the idea of a gene bank and store the new chromosomes generated each time in the gene bank. The idea of linear regression is proposed to predict the probability of crossover and mutation of the next generation. At the same time, a method of increasing chromosome diversity is proposed, which avoids the problem that it is difficult to set the control variable parameters reasonably and fall into local optimum when the GA is applied in path planning. To overcome the shortcomings of ACO in path planning, an ACO with improved heuristic function and transition probability was proposed, and the method of the real-time environment was given. Through the traveling salesman problem, the improved GA and ACO are combined to apply to path planning. The experimental results show that the fusion of GA and ACO in this paper is superior to ordinary GA and ACO in all aspects of path planning.
... Microrobots have gained a lot of attention in recent years because of the advancement of science and technology, which has led to their widespread application in engineering, agriculture, the military, and transportation [1,2]. One of these is path planning and obstacle avoidance [3]. While most algorithms are better at planning the collision-free operation path of a microrobot in some simple application scenarios, in real life, especially in complex environments, there are often obstacles, people, or other unpredictable things [4], and it is challenging to obtain the global information of the environment. ...
A hard issue in the field of microrobots is path planning in complicated situations with dense obstacle distribution. Although the Dynamic Window Approach (DWA) is a good obstacle avoidance planning algorithm, it struggles to adapt to complex situations and has a low success rate when planning in densely populated obstacle locations. This paper suggests a multi-module enhanced DWA (MEDWA) obstacle avoidance planning algorithm to address the aforementioned issues. An obstacle-dense area judgment approach is initially presented by combining Mahalanobis distance, Frobenius norm, and covariance matrix on the basis of a multi-obstacle coverage model. Second, MEDWA is a hybrid of enhanced DWA (EDWA) algorithms in non-dense areas with a class of two-dimensional analytic vector field methods developed in dense areas. The vector field methods are used instead of the DWA algorithms with poor planning performance in dense areas, which greatly improves the passing ability of microrobots over dense obstacles. The core of EDWA is to extend the new navigation function by modifying the original evaluation function and dynamically adjusting the weights of the trajectory evaluation function in different modules using the improved immune algorithm (IIA), thus improving the adaptability of the algorithm to different scenarios and achieving trajectory optimization. Finally, two scenarios with different obstacle-dense area locations were constructed to test the proposed method 1000 times, and the performance of the algorithm was verified in terms of step number, trajectory length, heading angle deviation, and path deviation. The findings indicate that the method has a smaller planning deviation and that the length of the trajectory and the number of steps can both be reduced by about 15%. This improves the ability of the microrobot to pass through obstacle-dense areas while successfully preventing the phenomenon of microrobots going around or even colliding with obstacles outside of dense areas.
... Most existing studies of transverse brachiation robots used multi-limbed robot designs based on human climbing motions [20,[26][27][28]. Lin and Yang [1] employed a four-link arm-body-tail robot configuration, dividing transverse ricochetal brachiation into a swing phase, a flight phase, and a landing phase. ...
Brachiation robots mimic the locomotion of bio-primates, including continuous brachiation and ricochetal brachiation. The hand-eye coordination involved in ricochetal brachiation is complex. Few studies have integrated both continuous and ricochetal brachiation within the same robot. This study seeks to fill this gap. The proposed design mimics the transverse movements of sports climbers holding onto horizontal wall ledges. We analyzed the cause-and-effect relationship among the phases of a single locomotion cycle. This led us to apply a parallel four-link posture constraint in model-based simulation. To facilitate smooth coordination and efficient energy accumulation, we derived the required phase switching conditions as well as joint motion trajectories. Based on a two-hand-release design, we propose a new style of transverse ricochetal brachiation. This design better exploits inertial energy storage for enhanced moving distance. Experiments demonstrate the effectiveness of the proposed design. A simple evaluation method based on the final robot posture from the previous locomotion cycle is applied to predict the success of subsequent locomotion cycles. This evaluation method serves as a valuable reference for future research.
... The frational-order high-gain observer is designed as (13). By properly selecting the controller paremeters, the designed control signals (24), (27), (33), (40), (44), (48), (54) and (58) based on the PPM and the ESO can ultimately make the error signals of the entire system for the UAH uniformly bounded, and realize the tracking of the given periodic desired signal. Proof Proof Proof: For the close-loop control system of the UAH, the Lyapunov function is choosen as ...
... On the basis of the improved WPA described in Table 1 , the three-dimensional space trajectory is planned, and the result of the final planning trajectory is shown in Fig. 3 . In order to illustrate the superiority of using the improved WPA to plan threedimensional trajectory in this paper, the traditional WPA, the traditional particle swarm optimization (PSO) algorithm and the genetic algorithm (GA) 47,48 are introduced for the three-dimensional trajectory planning, and the simulation results are shown in Figs. 4 -6 . According to the planned three-dimensional trajectory in Fig. 4 and Fig. 5 and the change curves of fitness values in Fig. 6 , we can be seen that the improved WPA has the better planned track quality, the shorter distance and the faster convergence speed than the traditional WPA, PSO and GA. ...
The tracking control problem of trajectory planning is studied in this paper based on prescribed performance method (PPM) for the small-scale unmanned autonomous helicopter (UAH) with wind-gust disturbances (WGDs) and unmeasurable states. For the purpose, the nonlinear model with flapping dynamics is established, and the transformation performance function is used to ensure that the errors of trajectory tracking satisfy the corresponding performance. The fractional-order observers are investigated to estimate the longitudinal and lateral flapping angles that are treated as unmeasurable states, and estimate the WGDs, respectively. Based on PPM and the designed observers, the fractional-order theory-based backstepping trajectory tracking control scheme is developed for the UAH system, and the three-dimensional trajectory is planned by the improved wolf pack algorithm. Then the stability of the entire system is proven through strict theoretical analysis. Finally, the simulation analysis on the UAH are presented to demonstrate the efficiency of the designed method.
... When multi-robots form a formation, each robot can be regarded as an agent; thus, the research on formation control is generally based on the coordinated control structure of the MAS [2,3] or the multi-robot control structure [4]. On the premise of coordinating the whole formation, the MAS also need to coordinate and deal with the dynamic relationship between all agents and judge and adjust their positions in the formation according to their position information, speed information and obstacle information [5][6][7][8][9][10][11][12]. Formation control involves making the actual state value of the agent gradually tend to the expected control value through control, which achieves the effect of convergence and stability. ...
When performing a specific task, a Multi-Agent System (MAS) not only needs to coordinate the whole formation but also needs to coordinate the dynamic relationship among all the agents, which means judging and adjusting their positions in the formation according to their location, velocity, surrounding obstacles and other information to accomplish specific tasks. This paper devises an integral separation feedback method for a single-agent control with a developed robot motion model; then, an enhanced strategy incorporating the dynamic information of the leader robot is proposed for further improvement. On this basis, a method of combining second-order formation control with path planning is proposed for multiple-agents following control, which uses the system dynamic of one agent and the Laplacian matrix to generate the consensus protocol. Due to a second-order consensus, the agents exchange information according to a pre-specified communication digraph and keep in a certain following formation. Moreover, an improved path planning method using an artificial potential field is developed to guide the MAS to reach the destination and avoid collisions. The effectiveness of the proposed approach is verified with simulation results in different scenarios.
... Considering the smaller and smaller scales in micro-nanorobotic systems, paper [11] has also been selected. Moreover, looking at a systems point-of-view in micronanorobotics network control, contribution [12] has been considered. These last two contributions are selected to remark how general strong hardware techniques at small scales and wide-range optimization algorithms in large scales can be integrated for advanced functional micromachines systems. ...
... The path-planning problem, which is a common problem in more classes of robots and, in particular, in robot networks, is approached by using an improved class of Genetic Algorithms in the paper proposed by Liu et al. [12]. The authors studied an improved optimization algorithm by using a parameter weight vector. ...
Robotics and micromachines are challenging topics in engineering [...]
A dynamic path planning method combining the adaptive potential field with the hierarchical replacement immune algorithm is proposed to realize the optimal navigation path and real-time obstacle avoidance. An improved ant-crawling mechanism, which incorporates the initial pheromones and heuristic information, is designed to achieve the initial population viability. Then to select superior antibodies from this initial population, the elite retention strategy and the roulette approach are applied simultaneously. According to the affinity, the number of antibodies is adaptively adjusted using the novel clone hierarchy model. Meanwhile, a new replacement mutation operator and adaptive replacement probability function are designed to produce better individuals. Finally, an adaptive-potential-field obstacle avoidance strategy is introduced to predict the imminent collision between vehicles and dynamic obstacles and activate the artificial potential field to replan the local path. The experiments prove that the method can improve the quality of the global path and realize real-time dynamic obstacle avoidance to ensure unmanned vehicle safety. The results show that the program running time, convergence iterations and the number of turns can be reduced by 87.35, 64.85 and 18.18%, respectively, in the complex environment.
