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University of California – Berkeley
1.
Tsai, Chi-Shen.
Online Trajectory Generation for Robot Manipulators in Dynamic Environment – An Optimization-based Approach.
Degree: Mechanical Engineering, 2014, University of California – Berkeley
URL: http://www.escholarship.org/uc/item/3x02b7x5 
Interest in robot manipulators interacting with dynamic environments has been continuously growing because of the increasing demand for industrial robot collaboration. Human-robot collaboration and robot-robot collaboration are the two scenarios of robot collaboration that have generally been considered. The difficulties of such applications may be described from two perspectives: a good perception of environment and a proper algorithm to react to the dynamic environment for the robot manipulators. Online trajectory generation is one of the approaches for robot reaction. In the generation of the trajectory, the transformation between joint space and task space is necessary since the sensor measurement of the environment is in task space and the trajectory of the robot manipulator is in joint space. The transformation needs to be done online in a dynamic environment and hence easily results in an exponential increase of the computational load.This dissertation proposes a safety index and the associated robot safety system in order to assess and ensure the safety of the agent in the collaboration scenarios. The agent could be a human worker in human-robot collaboration or another robot in robot-robot collaboration. In the robot safety system, the online trajectory generation algorithm is formulated in the optimization-based trajectory planning framework. The safety index is evaluated using the ellipsoid coordinates attached to the robot links that represents the distance between the robot manipulator and the agent. To account for the inertial effect, the momentum of therobot links are projected onto the coordinates to generate additional measures of safety. The safety index is used as a constraint in the formulation of the optimization problem so that a collision-free trajectory within a finite time horizon is generated online iteratively for the robot to move toward the desired position. To reduce the computational load for real-time implementation, the formulated optimization problem is further approximated by a quadratic problem. Moreover, a heuristic strategy is proposed to select the active constraints for the next iteration so as to further reduce the computational load. The safety index andthe proposed online trajectory generation algorithm are simulated and validated in both a two-link planar robot and a seven-DOF robot in human-robot collaboration and robot-robot collaboration. Simulation results show that the proposed algorithm and robot safety system are capable of generating collision-free and smooth trajectories online.The proposed algorithm has been extended to consider measurement noise in the agent information. Two possible approaches have been proposed for handling zero-mean Gaussian noise in the agent information.
Subjects/Keywords: Mechanical engineering; obstacle avoidance; online trajectory generation; Optimization
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APA (6th Edition):
Tsai, C. (2014). Online Trajectory Generation for Robot Manipulators in Dynamic Environment – An Optimization-based Approach. (Thesis). University of California – Berkeley. Retrieved from http://www.escholarship.org/uc/item/3x02b7x5
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Tsai, Chi-Shen. “Online Trajectory Generation for Robot Manipulators in Dynamic Environment – An Optimization-based Approach.” 2014. Thesis, University of California – Berkeley. Accessed June 20, 2019.
http://www.escholarship.org/uc/item/3x02b7x5.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Tsai, Chi-Shen. “Online Trajectory Generation for Robot Manipulators in Dynamic Environment – An Optimization-based Approach.” 2014. Web. 20 Jun 2019.
Vancouver:
Tsai C. Online Trajectory Generation for Robot Manipulators in Dynamic Environment – An Optimization-based Approach. [Internet] [Thesis]. University of California – Berkeley; 2014. [cited 2019 Jun 20].
Available from: http://www.escholarship.org/uc/item/3x02b7x5.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Tsai C. Online Trajectory Generation for Robot Manipulators in Dynamic Environment – An Optimization-based Approach. [Thesis]. University of California – Berkeley; 2014. Available from: http://www.escholarship.org/uc/item/3x02b7x5
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Lund
2.
Ghazaei, Mahdi.
On Trajectory Generation for Robots.
Degree: 2016, University of Lund
URL: http://lup.lub.lu.se/record/a17ad6f3-1e6b-4209-ae9f-92a6d80ed994
;
http://portal.research.lu.se/ws/files/17350248/thesis_mahdi_v161115.pdf
A fundamental problem in robotics is the generation
of motion for a task. How to translate a task to a set of movements
is a non-trivial problem. The complexity of the task, the
capabilities of the robot, and the desired performance, affect all
aspects of the trajectory; the sequence of movements, the path, and
the course of motion as a function of time.This thesis is about
trajectory generation and advances the state of the art in several
directions. Special attention to trajectories in constrained
situations when interaction forces are involved is paid. We bring a
control perspective to trajectory generation and propose novel
solutions for online trajectory generation with a rapid response to
sensor inputs. We formulate and find optimal trajectories for
various problems, closing the gap between path planning and
trajectory generation. The inverse problem of finding the control
signal corresponding to a desired trajectory is investigated and we
extend the applicability of an existing algorithm to a broader
class of problems.To collect human-generated trajectories involving
force interactions, we propose a method to join two robotic
manipulators to form a haptic interface for task demonstration.
Furthermore, fast algorithms for fixed-time point-to-point
trajectory generation are investigated. More importantly, two
optimal closed-loop trajectory generation methods are proposed. We
derive an optimal controller for the fixed-time
trajectory-generation problem with a minimum-jerk cost functional.
The other method is based on Model Predictive Control, which allows
a more generic form of system dynamics and constraints. In
addition, a ball-and-finger system is modeled for studying
trajectory generation where interaction plays an important role.
Efficient movements for rotating the ball are numerically computed
and simulated.Iterative Learning Control (ILC) finds a proper
control signal for obtaining a desired trajectory. We derive
frequency-domain criteria for the convergence of linear ILC on
finite-time intervals that are less restrictive than existing ones
in the literature.
Subjects/Keywords: Robotteknik och automation; Reglerteknik; Haptic Interface; Fixed-Time Point-to-Point Trajectory
Generation; Online Trajectory Generation; Dynamic Simulation and Optimization; Dynamics with Varying Contacts; Iterative Learning Control
Record Details
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Share »
Record Details
Similar Records
Cite
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ghazaei, M. (2016). On Trajectory Generation for Robots. (Doctoral Dissertation). University of Lund. Retrieved from http://lup.lub.lu.se/record/a17ad6f3-1e6b-4209-ae9f-92a6d80ed994 ; http://portal.research.lu.se/ws/files/17350248/thesis_mahdi_v161115.pdf
Chicago Manual of Style (16th Edition):
Ghazaei, Mahdi. “On Trajectory Generation for Robots.” 2016. Doctoral Dissertation, University of Lund. Accessed June 20, 2019.
http://lup.lub.lu.se/record/a17ad6f3-1e6b-4209-ae9f-92a6d80ed994 ; http://portal.research.lu.se/ws/files/17350248/thesis_mahdi_v161115.pdf.
MLA Handbook (7th Edition):
Ghazaei, Mahdi. “On Trajectory Generation for Robots.” 2016. Web. 20 Jun 2019.
Vancouver:
Ghazaei M. On Trajectory Generation for Robots. [Internet] [Doctoral dissertation]. University of Lund; 2016. [cited 2019 Jun 20].
Available from: http://lup.lub.lu.se/record/a17ad6f3-1e6b-4209-ae9f-92a6d80ed994 ; http://portal.research.lu.se/ws/files/17350248/thesis_mahdi_v161115.pdf.
Council of Science Editors:
Ghazaei M. On Trajectory Generation for Robots. [Doctoral Dissertation]. University of Lund; 2016. Available from: http://lup.lub.lu.se/record/a17ad6f3-1e6b-4209-ae9f-92a6d80ed994 ; http://portal.research.lu.se/ws/files/17350248/thesis_mahdi_v161115.pdf
. 
Open Access Theses and Dissertations