Extreme path planning for exploration mobile robots
- Autores: Jose Ricardo Sánchez Ibáñez
- Directores de la Tesis: Carlos Pérez del Pulgar (dir. tes.), Alfonso José García Cerezo (codir. tes.)
- Lectura: En la Universidad de Málaga ( España ) en 2022
- Idioma: español
- Tribunal Calificador de la Tesis: Begoña C. Arrue Ullés (presid.), Víctor Fernando Muñoz Martínez (secret.), Miguel Angel Olivares Mendez (voc.)
- Programa de doctorado: Programa de Doctorado en Ingeniería Mecatrónica por la Universidad de Málaga
- Materias:
- Enlaces
- Resumen
Increasing autonomy on mobile robots is essential. The main reason is to provide them with the ability to perform operations on their own. This skill is desirable in fields such as planetary exploration or search and rescue operations. Mobile robots could carry out more tasks in a time window by not relying on human intervention. These systems could, for instance, drive longer distances and hence reach more places. Autonomous navigation rests on the use of path planning algorithms. These algorithms generate a path that guides the robot towards a target location. Yet, off-road and unstructured environments can pose a challenge to its locomotion capabilities. Thus, the path planner must address them at the same time as the robot mobility skills. In this way, this algorithm finds the path that minimizes a metric such as energy consumption. Not always precise information describing the environments is complete. The path planner must hence dynamically update the generated path whenever necessary. The robot may deal with terrain elements that were not addressed before in situ. This thesis tackles problems that arise in autonomous navigation on irregular terrains. As a first step, it presents an overview of the existing path planning algorithms. This overview focuses on those algorithms that are compatible with ground mobile robots. It serves to build a classification of the existing approaches. This classification rests on their functionality. In a few words, it tackles the different ways in which path planner model and process the environment and the robot mobility. This thesis puts the focus on some of them that are capable to produce the globally optimal path given a cost map. These are called PDE (Partial Derivative Equation) solving algorithms. How to adapt these algorithms to the autonomous navigation of irregular terrains is the cornerstone of this thesis. It deals first with the use of a path planner along with a reconfigurable robot. This kind of robot is capable to perform many modes of locomotion. This skill allows them to adapt to a larger variety of terrains, and the planner must acknowledge it. Second, this thesis presents a path planning architecture based on PDE solvers that serve to not only find an initial path but also update it. This architecture addresses environment information coming from different sources. One of them is an initial global map that covers a large area but with low resolution. The other source is the robot itself, which detects obstacles placed on its way. Third, another contribution considers the use of a path planner in scenarios with inclined surfaces. Such a planner acknowledges the effect of gravity on the robot and how the orientation affects the cost. Finally, this thesis presents the results of simulation and field experiments. They check the functionality of the described planners and serve to validate them.
