TimeOptimal Control by Iterating Forward and Backward in Time
(2014)Department of Automatic Control
 Abstract
 When costumers look for a robot for their factory two things are important. The robot should be as efficient as possible, but still be cheap. In order to make the robot efficient the robotcontroller has to know the dynamics of the robot and its limits. Based on these it can then generate a timeoptimal plan (trajectory) for each movement. The standard way of generating a timeoptimal trajectory with the exact dynamics and limits is very computationally heavy. Today most approaches do the planning based on the hardest limits on each axis of the robot. These values are then used even though the current limits might allow the robot to move faster. This means that the full capacity of the robot will not be utilized and because of this the... (More)
 When costumers look for a robot for their factory two things are important. The robot should be as efficient as possible, but still be cheap. In order to make the robot efficient the robotcontroller has to know the dynamics of the robot and its limits. Based on these it can then generate a timeoptimal plan (trajectory) for each movement. The standard way of generating a timeoptimal trajectory with the exact dynamics and limits is very computationally heavy. Today most approaches do the planning based on the hardest limits on each axis of the robot. These values are then used even though the current limits might allow the robot to move faster. This means that the full capacity of the robot will not be utilized and because of this the efficiency is lowered.
In this thesis a different method for generating timeoptimal trajectories is tested. The approach is based on iterating forward and backward in time and finding the point where the two paths meet. This approach has the advantage of that it is based on simulating the system. Therefore more complex dynamics can be included in the planning by just calculating a value instead of complicating the optimization problem. Another benefit is that robot manufacturers usually create simulation models of new robots already. This means that very little extra effort would be needed to create the trajectory generator using this approach and this reduces development costs.
Four different approaches for patching together the forward and backward paths are discussed in the thesis. The different techniques are tested on a simplified model of one servo axis of a robot and compared against a known time optimal solution for the simplified model. One of the techniques shows very good results and generates trajectories that are timeoptimal. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/studentpapers/record/4778579
 author
 Bäckström, Adam
 supervisor

 Peter Valdt
 Klas Nilsson ^{LU}
 Anders Robertsson ^{LU}
 Rolf Johansson ^{LU}
 organization
 year
 2014
 type
 H3  Professional qualifications (4 Years  )
 subject
 other publication id
 ISRN LUTFD2/TFRT5944SE
 language
 English
 additional info
 month=nov
 id
 4778579
 date added to LUP
 20141117 09:49:46
 date last changed
 20141205 09:55:27
@misc{4778579, abstract = {When costumers look for a robot for their factory two things are important. The robot should be as efficient as possible, but still be cheap. In order to make the robot efficient the robotcontroller has to know the dynamics of the robot and its limits. Based on these it can then generate a timeoptimal plan (trajectory) for each movement. The standard way of generating a timeoptimal trajectory with the exact dynamics and limits is very computationally heavy. Today most approaches do the planning based on the hardest limits on each axis of the robot. These values are then used even though the current limits might allow the robot to move faster. This means that the full capacity of the robot will not be utilized and because of this the efficiency is lowered. In this thesis a different method for generating timeoptimal trajectories is tested. The approach is based on iterating forward and backward in time and finding the point where the two paths meet. This approach has the advantage of that it is based on simulating the system. Therefore more complex dynamics can be included in the planning by just calculating a value instead of complicating the optimization problem. Another benefit is that robot manufacturers usually create simulation models of new robots already. This means that very little extra effort would be needed to create the trajectory generator using this approach and this reduces development costs. Four different approaches for patching together the forward and backward paths are discussed in the thesis. The different techniques are tested on a simplified model of one servo axis of a robot and compared against a known time optimal solution for the simplified model. One of the techniques shows very good results and generates trajectories that are timeoptimal.}, author = {Bäckström, Adam}, language = {eng}, note = {Student Paper}, title = {TimeOptimal Control by Iterating Forward and Backward in Time}, year = {2014}, }