Feedback motion planning of legged robots by composing orbital Lyapunov functions using rapidly-exploring random trees

Zamani, Ali; Galloway, Joseph D.; Bhounsule, Pranav A.

doi:10.1109/icra.2019.8793578

Cited by 6 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Once the ROAs are computed for multiple cyclic or steady-state gaits, they may be sequenced together by reasoning about overlapping regions to create nonsteady or agile gaits using heuristics 4 or more systematic motion planning algorithms such as rapidly exploring random trees. 5 The flow of the paper is as follows. First, we give some background and related work on the ROA for legged systems in Section 2.…”

Section: Introductionmentioning

confidence: 99%

Control Policies for a Large Region of Attraction for Dynamically Balancing Legged Robots: A Sampling-Based Approach

Bhounsule¹,

Zamani²,

Krause³

et al. 2020

Robotica

Self Cite

View full text Add to dashboard Cite

SUMMARY The popular approach of assuming a control policy and then finding the largest region of attraction (ROA) (e.g., sum-of-squares optimization) may lead to conservative estimates of the ROA, especially for highly nonlinear systems. We present a sampling-based approach that starts by assuming an ROA and then finds the necessary control policy by performing trajectory optimization on sampled initial conditions. Our method works with black-box models, produces a relatively large ROA, and ensures exponential convergence of the initial conditions to the periodic motion. We demonstrate the approach on a model of hopping and include extensive verification and robustness checks.

show abstract

Section: Introductionmentioning

confidence: 99%

Control Policies for a Large Region of Attraction for Dynamically Balancing Legged Robots: A Sampling-Based Approach

Bhounsule¹,

Zamani²,

Krause³

et al. 2020

Robotica

Self Cite

View full text Add to dashboard Cite

show abstract

“…Further approaches employed the principles of motion planning algorithms which can compute collision-free path required for a robot to successfully complete a task [3]. Researchers have employed various motion planning algorithm, such as Dynamic roadmaps (DRM) [4,5], Rapidly Exploring Random Trees (RRT) [6,7], and elastic [8,9]. Although, significant improvements have been made using this approach, in general, motion planning algorithms is computational complex due to the high dimensionality of the configuration space that must be considered.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Collision-Free Reaching Skill Learning from Demonstration

Ogenyi

Zhou

et al. 2020

Communications in Computer and Information Science

View full text Add to dashboard Cite

In this paper, we considered the task of the robot learning low-level trajectory task in a novel clustered constraint environment.We propose a novel adaptive trajectory algorithm used to generate the necessary trajectory which satisfies the constraint of avoiding collision with an obstacle. Our approach is based on Gaussian mixture model which decomposes the trajectory into several ellipses since the isoline of a single Gaussian model is also an ellipse. Moreover, we employed the principle of the artificial potential field to modify the direction of the motion in the presence of obstacles. Since our approach is based on the underlying reactive skill dynamics, it does not share the same disadvantages as approaches which assume both the model of the task trajectory and the response from the obstacle should be learned from the demonstrations.

show abstract

Multi-Cloud Path Planning of Unmanned Aerial Vehicles with Multi-Criteria Decision Making: A Literature Review

Santhi

Valarmathi

Chellatamilan

2021

Operationalizing Multi-Cloud Environments

View full text Add to dashboard Cite

Feedback motion planning of legged robots by composing orbital Lyapunov functions using rapidly-exploring random trees

Cited by 6 publications

References 19 publications

Control Policies for a Large Region of Attraction for Dynamically Balancing Legged Robots: A Sampling-Based Approach

Control Policies for a Large Region of Attraction for Dynamically Balancing Legged Robots: A Sampling-Based Approach

Adaptive Collision-Free Reaching Skill Learning from Demonstration

Multi-Cloud Path Planning of Unmanned Aerial Vehicles with Multi-Criteria Decision Making: A Literature Review

Contact Info

Product

Resources

About