Hands-free Telelocomotion of a Wheeled Humanoid

Purushottam, Amartya; Jung, Yeongtae; Murphy, Kevin; Baek, Donghoon; Ramos, João Carlos

doi:10.1109/iros47612.2022.9981629

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…Wheeled-Legged Robot Control: The most popular approach to control wheeled-legged robots is model-based control, which leverages a mathematical dynamics model. Some strategies use simplified models, like the Wheeled Inverted Pendulum (WIP), and linearize them around the system's equilibrium point [3], [2]. To achieve better tracking and versatility, more complex models have been applied [1], [10], [11].…”

Section: Related Workmentioning

confidence: 99%

“…5. The WIP is often used as a template model to control a wheeled-legged robot [3]. Note that while the degree of freedom of WIP is less complex, this still maintains intricate interactions between the robot and its surroundings, including contact dynamics distinct from those of a traditional inverted pendulum.…”

Section: B Target Prototype Hardware Testbed: Wheeled-inverted Pendulummentioning

confidence: 99%

“…Combining the mobility of wheels and the mobility of legs, the capability to navigate difficult environments could be greatly improved. [1], [2], [3], [4].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid LMC: Hybrid Learning and Model-based Control for Wheeled Humanoid Robot via Ensemble Deep Reinforcement Learning

Baek

Purushottam

Ramos

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Model-based controllers using a linearized model around the system's equilibrium point is a common approach in the control of a wheeled humanoid due to their less computational load and ease of stability analysis. However, controlling a wheeled humanoid robot while it lifts an unknown object presents significant challenges, primarily due to the lack of knowledge in object dynamics. This paper presents a framework designed for predicting the new equilibrium point explicitly to control a wheeled-legged robot with unknown dynamics. We estimated the total mass and center of mass of the system from its response to initially unknown dynamics, then calculated the new equilibrium point accordingly. To avoid using additional sensors (e.g., force torque sensor) and reduce the effort of obtaining expensive real data, a data-driven approach is utilized with a novel real-to-sim adaptation. A more accurate nonlinear dynamics model, offering a closer representation of real-world physics, is injected into a rigid-body simulation for real-to-sim adaptation. The nonlinear dynamics model parameters were optimized using Particle Swarm Optimization. The efficacy of this framework was validated on a physical wheeled inverted pendulum, a simplified model of a wheeledlegged robot. The experimental results indicate that employing a more precise analytical model with optimized parameters significantly reduces the gap between simulation and reality, thus improving the efficiency of a model-based controller in controlling a wheeled robot with unknown dynamics.

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Section: Related Workmentioning

confidence: 99%

Section: B Target Prototype Hardware Testbed: Wheeled-inverted Pendulummentioning

confidence: 99%

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Hybrid LMC: Hybrid Learning and Model-based Control for Wheeled Humanoid Robot via Ensemble Deep Reinforcement Learning

Baek

Purushottam

Ramos

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…We aim to develop a large force haptic interface for the teleoperation system shown in Figure 1 [22][23][24]. The system utilizes the user's skills to explore the dynamic balancing and manipulation of the teleoperated robot.…”

Section: Introductionmentioning

confidence: 99%

A Large Force Haptic Interface with Modular Linear Actuators

Jung

Ramos

2023

Actuators

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This paper presents a haptic interface with modular linear actuators that addresses the limitations of conventional devices based on rotary joints. The proposed haptic interface is composed of parallel linear actuators that provide high backdrivability and small inertia. The performance of the haptic interface is compared to those of conventional mechanisms in terms of force capability, reflected inertia, and structural stiffness. High stiffness, large range of motion, and high force capability, which are in trade-off relationships in traditional haptic interfaces, are achieved. The device can apply up to 83 N continuously, i.e., three-fold more than most haptic devices. The theoretical minimum haptic force density and stiffness of the proposed mechanism are 1.3 to 1.9 and 37 times those of the conventional mechanisms under similar conditions, respectively. The system is scalable because the structural stiffness depends on only the timing belt stiffness, whereas that of conventional haptic interfaces is inversely proportional to the cube of the structural length. The modular actuator enables changes in the degrees of freedom (DOFs) for different applications. The proposed haptic interface was tested through an interaction experiment in a virtual environment with virtual walls.

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