Path planning is present in many areas, such as robotics, video games, and unmanned autonomous vehicles. In the case of robots, it is a primary low-level prerequisite for the successful execution of high-level tasks. It is a known and difficult problem to solve, especially in terms of finding optimal paths for robots working in complex environments. Recently, population-based methods for multi-objective optimization, i.e., swarm and evolutionary algorithms successfully perform on different path planning problems. Knowing the nature of the problem is hard for optimization algorithms, it is expected that population-based algorithms might benefit from some kind of diversity maintenance implementation. However, advantages and potential traps of implementing specific diversity maintenance methods into the evolutionary path planner have not been clearly spelled out and experimentally demonstrated. In this paper, we fill this gap and compare three diversity maintenance methods and their impact on the evolutionary planner for problems of different complexity. Crowding, fitness sharing, and novelty search are tailored to fit specific problems, implemented, and tested for two scenarios: mobile robot operating in a 2D maze, and 3 degrees of freedom (DOF) robot operating in a 3D environment including obstacles. Results indicate that the novelty search outperforms the other two methods for problem domains of higher complexity.Path planning can generally be classified based on the nature of the environment as static or dynamic. In the case of the static environment, the layout, once perceived by the robot remains unchanged. Dynamic environments include movable objects i.e., other robots.According to the algorithm used for planning, the planner can either be local-meaning sensor-based and reactive, or global-meaning map-based and planning the trajectory from the initial to the final position at once. Based on the completeness, planning can be classified either as complete or heuristic. In the case of complete planning, the algorithm will always find a path in finite time if the path exists, or report that no feasible path exists in finite time. These algorithms are preferred for the systems with a low number of degrees of freedom over the heuristics algorithms [2][3][4][5][6].In the case of a 3D problem considered in this paper, both the trajectory and the robot's body have to avoid obstacles present in the environment and thus checked for collisions. This makes planning significantly more complex when compared to simple point-wise planning. It is known that for similar problems, population-based heuristic planning methods, i.e., swarm and evolutionary approaches are successfully utilized to find high-quality solutions in acceptable time [7][8][9][10]. It is also proven that the path planning problem is NP-complete, with the complexity of tasks increasing exponentially with the DOF number [11]. This is an additional motivation of exploring alternative methods to path planning, seeking for high quality (instead of optimal) solut...

This paper presents a method based on evolutionary algorithms for the planning of robotic trajectories both in 2D and 3D space. Space can be arbitrary shaped containing randomly sized and distributed obstacles. Different methods were implemented and evaluated for improving the speed and the level of the convergence of the algorithm. It is shown that evolutionary algorithm in its simplest form cannot effectively solve this problem in 3D. A thorough statistical analysis is performed for various instances of the algorithm, and the best algorithmic procedures are identified, documented, and explained in the paper.