## 6 Conclusions and Future Work  
In this article, MOEA was presented to resolve the path planning problem. We dealt with three objectives: length, safety, and smoothness. In addition, we also proposed extra evolutionary operators to find precise and effective paths. Moreover, we used the well-known PSO algorithm for comparison. The obtained results were evaluated with the hypervolume indicator (HV) and the set coverage metric (scm). The comparisons show that the solutions obtained by MOEA are larger and denser in objective space and closer to the approximate Pareto front. Accordingly, five different scenarios were used for testing. The results show the advantages of proposed MOEA. This is a significant contribution. Another contribution is that we combine the advantages of the evolutionary algorithm with the traditional heuristic Dijkstra method to further improve the search efficiency.  
The shortcomings in this work are as following: (1) Since the operators are not very fast, the algorithm is currently only suitable for the off-line path planning problem. The next step is to further improve the computing speed of the operators. (2) Because the proposed algorithm does not consider the size and dynamic behavior of a specific robot, the algorithm can only plan the optimal path for the mobile robot. Other work can then be carried out, such as trajectory tracking.  
In the field of path planning, there are still many issues that are worthy of attention. For the single-objective path planning problems, at present, some scholars are focused on the following topics: (1) More efficient algorithms are needed to solve large-scale and real-time path planning problems. (2) Based on the dynamic characteristics of robots, by controlling the behavior of the robot motor in real time, the path planning problem and the trajectory tracking problem are combined to find an optimal walking path. For multi-objective path planning problems, there are several problems that need to be solved: (1) Most algorithms designed by scholars are offline. It is a challenging task to improve the computational efficiency of the algorithms. Hybrid algorithms may be effective approaches. (2) Currently, several researchers are focused on the dynamic environment. There are three types of dynamic obstacles: (a) The positions and velocities of the dynamic obstacles at any one time are completely known; (b) the positions and velocities of the dynamic obstacles are unknown, but predictable; and (c) the positions and velocities of the dynamic obstacles are unknown and unpredictable. There are some heuristic algorithms such as D* lite that can solve the single-objective shortest path in dynamic environments. For multi-objective path planning problems in dynamic environments, it may be a good solution to combine evolutionary algorithms with the advantages of traditional heuristic algorithms. (3) In recent years, some scholars have focused on the multi-robot multi-objective path planning problem. This is a complex coupling optimization problem. We will continue to focus on and solve these issues over the next few years.  
Figure 18: Graphical solution for the Figure 8E with MOEA.  
**Author Contributions:** [PERSON] investigated the relevant literature, proposed and implemented the algorithm, and wrote the paper; [PERSON] provided resources, confirmed the feasibility of the algorithm and revised the article.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflicts of interest.