Description:
Despite the recent progress in robot mobility, autonomous locomotion in cluttered and completely unknown environments remains a significant challenge for both wheeled and legged mobile robots. To fulfill this task, robots have to leverage their kinematics to negotiate the most effective locomotion strategy to adapt their configuration to the terrain conformation. This task becomes even more challenging when working with redundant hybrid wheeled-legged platforms, which combine both types of locomotion to enhance efficiency and safety during the traverse. Although the hybrid mobility system offers higher flexibility when traversing difficult terrains, effective hybrid locomotion planners that transparently combine different locomotion modes have not been extensively explored. Moreover, to further enhance the autonomy level during locomotion tasks, robots need to be equipped with the additional ability to reason about the scene composition and react autonomously to failures. This research project addresses these challenges by focusing on the implementation of multiple frameworks that can also be combined together to achieve autonomous loco-manipulation, ensuring safe traverses through irregular and cluttered environments. The first module introduces an online hybrid path planner for autonomous locomotion with wheeled-legged robots utilizing a set of parametrized motion primitives to adapt the robot's configuration to the treated scenario. The planned solution is continuously updated to incorporate changes in the scene while keeping track of possible failures that may arise during the execution. This allows the robot to effectively and autonomously react to such failures, without the need for failing the task or requiring human intervention. However, traversing unstructured environments is not always guaranteed to be feasible, especially in cluttered scenarios where multiple objects may prevent navigation. For this reason, an additional framework was developed to increase the robots' reasoning capabilities, enabling ...
Publisher:
Università degli studi di Genova
Contributors:
DE LUCA, Alessio ; MASSOBRIO, PAOLO
Year of Publication:
2025-02-20
Document Type:
info:eu-repo/semantics/doctoralThesis ; [Doctoral and postdoctoral thesis]
Subjects:
Motion and Path Planning ; Reactive Locomotion ; Recovery Behaviors ; Sensor-based Control ; Field Robotics ; Settore IINF-04/A - Automatica ; Settore IINF-05/A - Sistemi di elaborazione delle informazioni
Content Provider:
Università degli Studi di Genova: CINECA IRIS
