Fig 2 - uploaded by Alessio De Luca
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Overview of the framework pipeline, in green the perception component, in orange the motion execution module and in blue the hybrid planner component.
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Wheeled-legged robots have the potential to navigate in cluttered and irregular scenarios by altering the locomotion modes to adapt to the terrain challenges and effectively reach targeted locations in unstructured spaces. To achieve this functionality, a hybrid locomotion planner is necessary. In this work we present a search-based planner, which...
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... proposed framework is composed of three main components: the traversability extractor, the hybrid primitivebased planner and the plan executor. A visual representation of the framework can be seen in Fig. 2. For the traversability extractor we explored the method in [16] to create a 2.5D elevation map that is transformed into a binary map which we will call foothold validity map. This map takes into account the elevation and edges, extracted through the application of different filters together with the inflation radius specifying the ...
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... In our previous work [21] we proposed a framework to perform autonomous navigation in an offline manner with the use of a set of motion primitives, employing a search-based planner. In this work, we extend the framework by proposing a hierarchical planning architecture composed of search-based global and local planners. ...
... II. PROPOSED FRAMEWORK In our previous work [21], we designed and validated an offline method for hybrid navigation with wheeled-legged robots. The method was based on the use of parametrized motion primitives to adapt to different robots. ...
... The global planner has the objective of guiding the movements of the robot inside the map with its resulting plan without directly commanding the execution. For the global planner, we used the same implementation we proposed in our previous work [21], based on the ARA* [24]. In more detail, the inputs of the planner are the global map obtained using only the Velodyne Puck 16 Lidar sensor, the initial robot pose, the target position and orientation we want the robot to reach, and the primitives available (whole-robot driving, stepping, and single-wheel driving) together with their parameters, based on the platform considered. ...
Performing autonomous navigation in cluttered and unstructured terrains still remains a challenging task for legged and wheeled mobile robots. To accomplish such a task, online planners shall incorporate new terrain information perceived while the robot is moving within its environment. While hybrid mobility robots offer high flexibility in traversing challenging terrains by leveraging the advantages of both wheeled and legged locomotion, the effective hybrid planning of the mobility actions that transparently combine both modes of locomotion has not been extensively explored. In this work, we present a hierarchical online hybrid primitive-based planner for autonomous navigation with wheeled-legged robots. The framework is handled by a Behavior Tree (BT) and it takes into account recovery methods to deal with possible failures during the execution of the navigation/mobility plan. The framework was evaluated in multiple irregular and heavily cluttered simulated environments randomly generated and in real-world trials, using the CENTAURO robot platform. With these experiments, we demonstrated autonomous capabilities without any human intervention, even in case of collision or planner failures.
... Legged robots are a research hotspot in the field of robotics and have achieved excellent performance in structural design and motion control [1][2][3][4][5][6][7][8][9][10]. The one-legged robot is a special kind of legged robot because it is the basis of legged robot research. ...
... According to Equations (4), (6), and (7), it is simple to acquire ...
Legged animals can adapt to complex terrains because they can step or jump over obstacles. Their application of foot force is determined according to the estimation of the height of an obstacle; then, the trajectory of the legs is controlled to clear the obstacle. In this paper, we designed a three-DoF one-legged robot. A spring-loaded inverted pendulum model was employed to control the jumping. Herein, the jumping height was mapped to the foot force by mimicking the jumping control mechanisms of animals. The foot trajectory in the air was planned using the Bézier curve. Finally, the experiments of the one-legged robot jumping over multiple obstacles of different heights were implemented in the PyBullet simulation environment. The simulation results demonstrate the effectiveness of the method proposed in this paper.
