Legged robots have the advantage of being able to maneuver rough, unstructured terrains unlike their wheeled counterparts. However, many legged robots require multiple sensors and online computations to specify the gait, trajectory or contact forces in real-time for a given terrain, and these methods can break down when sensory information is unreliable or not available. Over the years, underactuated mechanisms have demonstrated great success in object grasping and manipulation tasks due to their ability to passively adapt to the geometry of the objects without sensors. In this paper, we present an application of underactuation in the design of a legged robot with prismatic legs that maneuvers unstructured terrains under open-loop control using only four actuators – one for stance for each half of the robot, one for forward translation, and one for steering. Through experimental results, we show that prismatic legs can support a statically stable stance and can facilitate locomotion over unstructured terrain while maintaining its body posture.
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This content will become publicly available on October 1, 2024
Informed Sampling-Based Planning to Enable Legged Robots to Safely Negotiate Permeable Obstacles
Abstract Legged robots have a unique capability of traversing rough terrains and negotiating cluttered environments. Recent control development of legged robots has enabled robust locomotion on rough terrains. However, such approaches mainly focus on maintaining balance for the robot body. In this work, we are interested in leveraging the whole body of the robot to pass through a permeable obstacle (e.g., a small confined opening) with height, width, and terrain constraints. This paper presents a planning framework for legged robots manipulating their body and legs to perform collision-free locomotion through a permeable obstacle. The planner incorporates quadrupedal gait constraint, biasing scheme, and safety margin for the simultaneous body and foothold motion planning. We perform informed sampling for the body poses and swing foot position based on the gait constraint while ensuring stability and collision avoidance. The footholds are planned based on the terrain and the contact constraint. We also integrate the planner with robot control to execute the planned trajectory successfully. We validated our approach in high-fidelity simulation and hardware experiments on the Unitree A1 robot navigating through different representative permeable obstacles.
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- Award ID(s):
- 2133091
- NSF-PAR ID:
- 10441154
- Date Published:
- Journal Name:
- Journal of Mechanisms and Robotics
- Volume:
- 15
- Issue:
- 5
- ISSN:
- 1942-4302
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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