Online Dynamic Motion Planning and Control for Wheeled Biped Robots

Xin, Songyan; Vijayakumar, Sethu

doi:10.1109/iros45743.2020.9340967

Cited by 18 publications

(9 citation statements)

References 26 publications

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“…The policy was updated at every 1,000 steps. The size of our replay buffer was 1e 6 . The learning rate was set to 1e −3 .…”

Section: B Training Detailsmentioning

confidence: 99%

“…The most common approach of control for these high dimensional nonlinear systems is to model a robot using reduced-order models (RoMs), such as Linear Inverted Pendulums (LIP) and Wheeled Inverted Pendulums (WIP), and adopt model-based linear quadratic regulator (LQR) [5], [6] or model predictive control (MPC) [7]. Alternatively, differential dynamic programming (DDP) and Nonlinear MPC (NMPC) are utilized to generate whole-body motion as a nonlinear approach [8], [9].…”

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

Baek¹,

Purushottam²,

Ramos³

2022

Preprint

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Control of wheeled humanoid locomotion is a challenging problem due to the nonlinear dynamics and underactuated characteristics of these robots. Traditionally, feedback controllers have been utilized for stabilization and locomotion. However, these methods are often limited by the fidelity of the underlying model used, choice of controller, and environmental variables considered (surface type, ground inclination, etc). Recent advances in reinforcement learning (RL) offer promising methods to tackle some of these conventional feedback controller issues, but require large amounts of interaction data to learn. Here, we propose a hybrid learning and modelbased controller Hybrid LMC that combines the strengths of a classical linear quadratic regulator (LQR) and ensemble deep reinforcement learning. Ensemble deep reinforcement learning is composed of multiple Soft Actor-Critic (SAC) and is utilized in reducing the variance of RL networks. By using a feedback controller in tandem the network exhibits stable performance in the early stages of training. As a preliminary step, we explore the viability of Hybrid LMC in controlling wheeled locomotion of a humanoid robot over a set of different physical parameters in MuJoCo simulator. Our results show that Hybrid LMC achieves better performance compared to other existing techniques and has increased sample efficiency.

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“…The policy was updated at every 1,000 steps. The size of our replay buffer was 1e 6 . The learning rate was set to 1e −3 .…”

Section: B Training Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Baek¹,

Purushottam²,

Ramos³

2022

Preprint

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show abstract

“…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]. However, the success of these methods depends greatly on the precision of the modeled dynamics, a precision that is difficult to attain and may not adjust well to changes in the system or environment.…”

Section: Related Workmentioning

confidence: 99%

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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“…When the height of obstacles is greater than the radius of the wheels, they cannot effectively cross obstacles (Ding and Zhang, 2022). Legged robots have excellent adaptability when moving on the uneven and rough roads, but slow moving speed and low movement energy efficiency have always been technical problems that are difficult to break through (Xin et al, 2019;Xin and Vijayakumar, 2020). To solve this problem, the researchers turned their attention to wheellegged robots.…”

Section: Introductionmentioning

confidence: 99%

Design and dynamic analysis of jumping wheel-legged robot in complex terrain environment

et al. 2022

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Wheel-legged robots have fast and stable motion characteristics on flat roads, but there are the problems of poor balance ability and low movement level in special terrains such as rough roads. In this paper, a new type of wheel-legged robot with parallel four-bar mechanism is proposed, and the linear quadratic regulator (LQR) controller and fuzzy proportion differentiation (PD) jumping controller are designed and developed to achieve stable motion so that the robot has the ability to jump over obstacles and adapt to rough terrain. The amount of energy released by the parallel four-bar linkage mechanism changes with the change of the link angle, and the height of the jump trajectory changes accordingly, which improves the robot’s ability to overcome obstacles facing vertical obstacles. Simulations and real scene tests are performed in different terrain environments to verify obstacle crossing capabilities. The simulation results show that, in the pothole terrain, the maximum height error of the two hip joint motors is 2 mm for the obstacle surmounting method of the adaptive retractable wheel-legs; in the process of single leg obstacle surmounting, the maximum height error of the hip joint motors is only 6.6 mm. The comparison of simulation data and real scene experimental results shows that the robot has better robustness in moving under complex terrains.

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Online Dynamic Motion Planning and Control for Wheeled Biped Robots

Cited by 18 publications

References 26 publications

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

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

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

Design and dynamic analysis of jumping wheel-legged robot in complex terrain environment

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