Efficient Motion Planning Based on Kinodynamic Model for Quadruped Robots Following Persons in Confined Spaces

Zhang, Zhen; Yan, Jiaqing; Kong, Xin; Zhai, Guangyao; Liu, Yong

doi:10.1109/tmech.2021.3083594

Cited by 25 publications

(8 citation statements)

References 26 publications

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“…To improve the stability of the proposed system, we need to measure the spine state x s more accurately by implementing a continuous dynamic model. 13 For this purpose, we wrap up muscle-like material on the quadrupedal robot's spine, converting the discrete model into a continuous one. Now we detect readout value to test the network's performance with a feedback loop, as shown in Figure 3.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…To model the system's dynamic behavior using Reservoir Computing, the spine state x s is measured using bending sensors mounted along the spine's length. 1,13 The bending sensors' signals are then processed through a Reservoir Computing layer that employs Echo State Networks (ESN) to generate a time series prediction of the system's state. 9,14 The Reservoir Computing layer comprises three major components: the input, reservoir layers, and readout.…”

Section: Reservoir Computingmentioning

confidence: 99%

“…14,15 The Reservoir Computing layer then feeds the transformed input into a linear readout layer to estimate the system's response, the benefit for Reservoir Computing is that it able to analysis chaotic system which suitable for soft robotics dynamic modeling. 13,16 The only trainable part of reservoir computing is the readout, which is ridge node, can be trained by state of training and y[t], where the state of training is the activation of the reservoir trigger by x[t], 10 as shown in Figure 1. Spine in the picture shown has four segments with three actuator, these actuator will generate force to balance the center of mass into the dynamic surface, 2, 8 this spine figure is the internal structure, it will wrap the muscle-like material to make dynamic more like real-spine to deal with continuous model with chaotic system.…”

Section: Reservoir Computingmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent design for quadruped robot on a dynamic flexible surface with an active spine

Liu

Lokhande

2023

Unmanned Systems Technology XXV

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This paper proposes an approach to design a quadruped robot with a dynamic spine using Reservoir Computing. Four bending sensors are integrated into the continuous dynamics spine to adjust spine force for balancing and enable closed-loop control of the Center of Mass (CoMs) mapping onto the dynamic surface. Passively modeling the system enabled a quadrupedal robot with a flexible spine to navigate rough terrain with improved efficiency, agility, and minimized impact on explosive power. Additionally, paper introduce to develop a novel, intelligent control mechanism for the quadrupedal robot to operate effectively on dynamic, flexible surfaces. Our approach focuses on a hierarchical distributed control mechanism developed for leg to cooperatively change centralized frames for a better functional reflection.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Reservoir Computingmentioning

confidence: 99%

Section: Reservoir Computingmentioning

confidence: 99%

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Intelligent design for quadruped robot on a dynamic flexible surface with an active spine

Liu

Lokhande

2023

Unmanned Systems Technology XXV

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“…5 and Table I). Although the proposed reduced-order model in (3) does not assume a specific form of surface motion, the proposed analytical solution is derived based on the assumption that the surface motion is vertical and sinusoidal. Such a surface motion is typical for real-world ship motions in regular sea waves [27], [32].…”

Section: Discussionmentioning

confidence: 99%

Real-Time Walking Pattern Generation of Quadrupedal Dynamic-Surface Locomotion based on a Linear Time-Varying Pendulum Model

Iqbal¹,

Veer²,

Gu³

2023

Preprint

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This study introduces an analytically tractable and computationally efficient model of the legged robot dynamics associated with locomotion on a dynamic rigid surface (DRS), and develops a real-time motion planner based on the proposed model and its analytical solution. This study first theoretically extends the classical linear inverted pendulum (LIP) model from legged locomotion on a static surface to DRS locomotion, by relaxing the LIP's underlying assumption that the surface is static. The resulting model, which we call "DRS-LIP", is explicitly time-varying. After converting the DRS-LIP into Mathieu's equation, an approximate analytical solution of the DRS-LIP is obtained, which is reasonably accurate with a low computational cost. Furthermore, to illustrate the practical uses of the analytical results, they are exploited to develop a hierarchical motion planner that efficiently generates physically feasible trajectories for DRS locomotion. Finally, the effectiveness of the proposed theoretical results and motion planner is demonstrated both through PyBullet simulations and experimentally on a Laikago quadrupedal robot that walks on a rocking treadmill. The videos of simulations and hardware experiments are available at https://youtu.be/u2Q_u2pR99c.

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“…Trajectory prediction refers to predicting the future trajectories of one or several target agents based on past trajectories and road conditions. It is an essential and emerging subtask in autonomous driving [23,28,37,45] and industrial robotics [21,35,48].…”

Section: Introductionmentioning

confidence: 99%

IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction

Zhu¹,

Zhai²,

Yan³

et al. 2023

Preprint

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Reliable multi-agent trajectory prediction is crucial for the safe planning and control of autonomous systems. Compared with single-agent cases, the major challenge in simultaneously processing multiple agents lies in modeling complex social interactions caused by various driving intentions and road conditions. Previous methods typically leverage graph-based message propagation or attention mechanism to encapsulate such interactions in the format of marginal probabilistic distributions. However, it is inherently sub-optimal. In this paper, we propose IPCC-TP, a novel relevance-aware module based on Incremental Pearson Correlation Coefficient to improve multi-agent interaction modeling. IPCC-TP learns pairwise joint Gaussian Distributions through the tightly-coupled estimation of the means and covariances according to interactive incremental movements. Our module can be conveniently embedded into existing multi-agent prediction methods to extend original motion distribution decoders. Extensive experiments on nuScenes and Argoverse 2 datasets demonstrate that IPCC-TP improves the performance of baselines by a large margin.

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Efficient Motion Planning Based on Kinodynamic Model for Quadruped Robots Following Persons in Confined Spaces

Cited by 25 publications

References 26 publications

Intelligent design for quadruped robot on a dynamic flexible surface with an active spine

Intelligent design for quadruped robot on a dynamic flexible surface with an active spine

Real-Time Walking Pattern Generation of Quadrupedal Dynamic-Surface Locomotion based on a Linear Time-Varying Pendulum Model

IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction

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