# Modular Robotic Design and Reconfiguring Path Planning
## 1 Introduction
[18] combined the artificial bee colony algorithm to propose the optimization-seeking ants and the reconnaissance ants, which were given different pheromone update weights to improve the convergence speed of the algorithm, avoid the algorithm to fall into local optimum. However, the inability to deal with deadlocked ants or replenish the number of ants causes the algorithm to fail easily in path planning in complex environments. Obstacle avoidance is a fundamental problem of path planning has also achieved a lot of research results nowadays. [PERSON] et al. [19] proposed a new global path planning method for a self-reconfiguring robot based on the A-Star algorithm, which aims to cover a narrow space by generating path points with maximum coverage area to ensure excellent area coverage performance. [PERSON] et al. [20] proposed an improved artificial potential field method to consider the distance between the robot and obstacles in planning. [PERSON] et al. [21] proposed an EABC (Efficient artificial bee colony) algorithm with an obstacle avoidance policy for solving online path planning of multiple mobile devices. [PERSON] et al. [22; 23] proposed a probabilistic-based obstacle avoidance trajectory planning method that uses Gaussian motion prior probability estimation to obtain the optimal path trajectory. Although the path planning method based on the instructional programming model is feasible, it isn't easy to get the optimal path, and path planning is too time-consuming. As shown above, although a lot of researches have been conducted on the path planning problem, the above research still has the following characteristics: (1) There is no combination of path optimization and obstacle avoidance problems, which leads to the risk of collision or even damage when the robot is performing path planning and has limitations. (2) The collision-free path planning takes too long, which leads to multiple repetitions of the path when the robot performs path planning, and the movement is inefficient. (3) Lacking handling of lock-ups during the path planning, which makes the path planning prone to failure. The main objective of this paper is to build a reconfiguration platform for functional modules and design an optimized algorithm so that functional modules can find an optimal path in the platform to reconfigure the target configuration. We improve the movement efficiency of the reconfigured robot from the initial position to the working target position by optimizing the ant colony algorithm, reducing the unnecessary movement distance, and rationalizing obstacle avoidance. The paper is organized as follows: the first part introduces the advantages of the reconfiguration robot and the real-world application in self-reconfiguration planning. The second part investigates the module design and configuration modeling of self-reconfiguring robots. In the third part, a reconfiguration platform for functional modules is established, and the types of obstacle avoidance in the process of modules reconfiguration are defined. Subsequently, an optimized ant colony algorithm is designed based on the principle of obstacle avoidance for the path planning of modules of the reconfiguration platform. In the fourth part, simulation experiments of the platform path planning are conducted to compare and analyze the parameters of reconstruction time, obstacle avoidance time, and path planning length with the traditional ant colony algorithm. The fifth part concludes the paper and gives an outlook on future development.