Abstract
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.
Highlights
Robotics expands from traditional industrial environments towards coworking and coexisting with humans in domains like medicine, education, leisure, and the general service domain
For the above-mentioned reasons, path planning, meaning finding trajectories optimized under a set of often colliding criteria is a problem of intense interest in the scientific community
It is certain that reliable, safe, and timely path planning presents a stepping-stone in the direction for robots to reach their full potential in collaboration with humans
Summary
Robotics expands from traditional industrial environments towards coworking and coexisting with humans in domains like medicine, education, leisure, and the general service domain. Apart from the ethical aspects of robots sharing an environment with humans, there is still a plethora of technical issues limiting the robots’ ability to fully immerse in collaboration with humans. One of such important problems is the limited ability of robots to perform efficiently in an unstructured environment. Such environments require robots to constantly re-plan, adapt, and optimize their actions. If an objective like time, distance, or collision avoidance is critical for the success of the operation of the robot, its ability of planning and optimizing according to required objectives becomes of paramount importance. It is certain that reliable, safe, and timely path planning presents a stepping-stone in the direction for robots to reach their full potential in collaboration with humans
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