Whole-body motion planning for humanoid robots with heuristic search

Athar, Ali; Zafar, Abdul Moeed; Rizwan, Asif; Ahmad, Khan, Armaghan; Islam, Fahad; Yasar,; Hasan, Osman

doi:10.1109/iros.2016.7759694

Cited by 2 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They used two cameras, a ceiling camera, and the robot's camera. Also, Athar et al [46] applied search-based for body motion planning through optimizing a graph search. There, they constructed a graph dynamically within three types of motion primitives and an A* algorithm variant called Weighted A*.…”

Section: Related Workmentioning

confidence: 99%

Active object search using a pyramid approach to determine the next-best-view

Flores-Rodriguez

Trujillo-Romero

González-Barbosa

2022

IJRA

View full text Add to dashboard Cite

<p>The development of service robotics continues to arouse interest in the scientific community due to the complexity of the activities performed like interaction in human environments, identifying and manipulating objects, and even learning by themselves. This paper proposed to improve the perception of the environment by searching for objects in service robotics tasks. We present the development and implementation of an active object search method based on three main phases: Firstly, image pyramid segmentation to examine in detail the im- age features. Second step, object detection at each level of the pyramid through a local feature descriptor and a mutual information calculation. Finally, the next camera position selection through analyzing the object detections accumulation in the pyramid. To evaluate the implementation of the proposed method, we use a NAO robot in a familiar place for humans, such as an office or a home. Ordinary objects are part of our database with the premise that a robot must know them before looking for an object. The results in the experiments showed an acceptable performance in simulation and with a real platform.</p>

show abstract

Section: Related Workmentioning

confidence: 99%

Active object search using a pyramid approach to determine the next-best-view

Flores-Rodriguez

Trujillo-Romero

González-Barbosa

2022

IJRA

View full text Add to dashboard Cite

show abstract

“…They may rely on a controller to produce feasible joint trajectories or the planner may generate these trajectories online to ensure footstep validity. Example whole-body planning techniques have been explored in [6], [7], [8]. These approaches are generally intended for object interaction tasks, and don't consider incorporating locomotion.…”

Section: Related Workmentioning

confidence: 99%

A Single-Planner Approach to Multi-Modal Humanoid Mobility

Dornbush

Vijayakumar

Bardapurkar

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

View full text Add to dashboard Cite

In this work, we present an approach to planning for humanoid mobility. Humanoid mobility is a challenging problem, as the configuration space for a humanoid robot is intractably large, especially if the robot is capable of performing many types of locomotion. For example, a humanoid robot may be able to perform such tasks as bipedal walking, crawling, and climbing. Our approach is to plan for all these tasks within a single search process. This allows the search to reason about all the capabilities of the robot at any point, and to derive the complete solution such that the plan is guaranteed to be feasible.A key observation is that we often can roughly decompose a mobility task into a sequence of smaller tasks, and focus planning efforts to reason over much smaller search spaces. To this end, we leverage the results of a recently developed framework for planning with adaptive dimensionality, and incorporate the capabilities of available controllers directly into the planning process. The resulting planner can also be run in an interleaved fashion alongside execution so that time spent idle is much reduced.

show abstract

Whole-body motion planning for humanoid robots with heuristic search

Cited by 2 publications

References 17 publications

Active object search using a pyramid approach to determine the next-best-view

Active object search using a pyramid approach to determine the next-best-view

A Single-Planner Approach to Multi-Modal Humanoid Mobility

Contact Info

Product

Resources

About