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Mustafa Mashali

dblp:161/8111 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Motion planning and robot control · 50% Robot manipulation · 38% Robot navigation and mapping · 12%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › mobile robot control
mobile manipulator control
0.312018
Controlling a Non-Holonomic Mobile Manipulator in a Constrained Floor Space · ICRA 2018
Robotics › Robot manipulation › mobile manipulation
nonholonomic mobile manipulator
0.312018
Controlling a Non-Holonomic Mobile Manipulator in a Constrained Floor Space · ICRA 2018
Robotics › Robot navigation and mapping
obstacle avoidance
0.112018
Controlling a Non-Holonomic Mobile Manipulator in a Constrained Floor Space · ICRA 2018
Robotics › Motion planning and robot control › collision avoidance
sensor-based collision avoidance
0.112018
Controlling a Non-Holonomic Mobile Manipulator in a Constrained Floor Space · ICRA 2018

Methods — techniques the papers use, named apart from their topics

simulation · 0.3
YearPublicationVenuePosition
2018 Controlling a Non-Holonomic Mobile Manipulator in a Constrained Floor Space
abstract
Robotic manipulators that are attached to mobile platforms are often used in workspaces that require the end-effector to mobilize beyond the manipulator's limited reach, such as in warehouse shelf stacking and similar applications. However, such assistive robots fall short of completing tasks that require the end-effector to be situated in a specific configuration at a critical time during the task. Traditionally, users control the mobile base to situate the arm such that the task can be completed through continuous operation. This requires an experienced operator who can predict the needed end-effector workspace, and can operate the base accordingly to maximize the likelihood of a successful task while avoiding any floor obstacles. In this work, we propose a straightforward control method that provides sufficient freedom to the end-effector to complete a task that is bound by time-dependent constraints. This is achieved by relaxing the time constraints on the mobile base trajectory in a floor space obstructed by obstacles. The trajectory of the platform is determined by sensor-assisted obstacle avoidance algorithm such that a single degree of freedom mobility can be represented through a safe obstacle-free time-independent path. The proposed control method is implemented in simulation and on physical hardware built in our labs. The simulation included a 5-DoF redundant Planar Mobile Manipulator (PMM). The hardware implementation and testing utilized a 9-DoF redundant mobile manipulator. The implementation results demonstrate the effectiveness of the control method in adjusting the mobile platform motion along its allowed obstacle-free path to enable the end-effector to follow its trajectory for task completion that would otherwise fail to complete when conventional control methods are used.
Mustafa Mashali, Redwan Alqasemi, Rajiv V. Dubey
ICRA1