Difference between revisions of "Energy Optimization"

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Energy optimization is a fundamental requirement to achieve long term autonomous deployments of mobile
 
Energy optimization is a fundamental requirement to achieve long term autonomous deployments of mobile
robots. Most robotic systems which operate on the field are subject to severe energy limitations. In particular,
+
robots. Most robotic systems which operate on the field are subject to severe energy limitations. Robot developers face trade-offs between payload capacity and battery life since existing batteries are
robot developers face trade-offs between payload capacity and battery life since existing batteries are
 
 
heavy and last a short amount of time under full actuation. Hence, it is critical to develop energy-aware algorithms that optimize the existing resources on the robot and harvest additional energy when available.
 
heavy and last a short amount of time under full actuation. Hence, it is critical to develop energy-aware algorithms that optimize the existing resources on the robot and harvest additional energy when available.
  
We frequently encounter this problem of limited on-board energy in our field experiments for the [[Data Mules]] and [[Environmental Monitoring]] projects. With a goal of achieving long-term autonomy, we are studying the following problems.
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We frequently encounter this problem in our field experiments for the [[Data Mules]] and [[Environmental Monitoring]] projects. With a goal of achieving long-term autonomy, we are studying the following problems.
  
 
== Trajectory Planning to Minimize Energy Consumption ==
 
== Trajectory Planning to Minimize Energy Consumption ==
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[[Image:Energy.png|center|700px]]
 
[[Image:Energy.png|center|700px]]
  
* When the path for the robot is given as input, we present a closed-form solution for finding the optimal velocity profiles for the robot that minimize the energy consumption (see [[#Related Publications|[2]]])
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* When the path for the robot is given as input, we present a closed-form solution for finding the optimal velocity profiles for the robot that minimizes the energy consumption (see [[#Related Publications|[2]]]).
* For finding minimum energy paths, there is a trade-off between the length of the paths, turning radius, frictional forces, velocity and acceleration of the robot. Unlike minimum length (Dubins') paths, a minimum energy path may contain segments with varying turning radius. We show how to efficiently construct a graph which generalizes Dubins’ paths by including segments with arbitrary radii.  
+
* For finding minimum energy paths, there is a trade-off between the length of the paths, turning radius, frictional forces, velocity and acceleration of the robot. Unlike minimum length (Dubins') paths, a minimum energy path may contain segments with varying turning radii. We show how to efficiently construct a graph which generalizes Dubins’ paths by including segments with arbitrary radii.  
* We present an algorithm uses that the closed-form solution for the optimal velocity profiles as a subroutine to find the minimum energy trajectories, up to a fine discretization. We investigate the structure of energy-optimal paths and highlight instances where these paths deviate from the minimum length Dubins’ curves.
+
* We present an algorithm that uses the closed-form solution for the optimal velocity profiles as a subroutine to find the minimum energy trajectories, up to a fine discretization. We investigate the structure of energy-optimal paths and highlight instances where these paths deviate from the minimum length Dubins’ curves.
 
* Finally, we present a calibration procedure to obtain the energy model and validate our algorithms with experiments conducted on a custom-built robot.
 
* Finally, we present a calibration procedure to obtain the energy model and validate our algorithms with experiments conducted on a custom-built robot.
 +
 +
A software package that implements the above contributions is available for download [[Downloads|here]].
  
 
== Energy Harvesting ==  
 
== Energy Harvesting ==  

Latest revision as of 20:11, March 16, 2014

Energy optimization is a fundamental requirement to achieve long term autonomous deployments of mobile robots. Most robotic systems which operate on the field are subject to severe energy limitations. Robot developers face trade-offs between payload capacity and battery life since existing batteries are heavy and last a short amount of time under full actuation. Hence, it is critical to develop energy-aware algorithms that optimize the existing resources on the robot and harvest additional energy when available.

We frequently encounter this problem in our field experiments for the Data Mules and Environmental Monitoring projects. With a goal of achieving long-term autonomy, we are studying the following problems.

Trajectory Planning to Minimize Energy Consumption

One of the main bottlenecks for robots is the limited lifetime of on-board batteries. Motion is a major source of energy consumption. In [7], we studied the problem of minimizing the energy consumption for a car-like robot by optimizing its path and velocity profile. We make the following contributions:

Energy.png
  • When the path for the robot is given as input, we present a closed-form solution for finding the optimal velocity profiles for the robot that minimizes the energy consumption (see [2]).
  • For finding minimum energy paths, there is a trade-off between the length of the paths, turning radius, frictional forces, velocity and acceleration of the robot. Unlike minimum length (Dubins') paths, a minimum energy path may contain segments with varying turning radii. We show how to efficiently construct a graph which generalizes Dubins’ paths by including segments with arbitrary radii.
  • We present an algorithm that uses the closed-form solution for the optimal velocity profiles as a subroutine to find the minimum energy trajectories, up to a fine discretization. We investigate the structure of energy-optimal paths and highlight instances where these paths deviate from the minimum length Dubins’ curves.
  • Finally, we present a calibration procedure to obtain the energy model and validate our algorithms with experiments conducted on a custom-built robot.

A software package that implements the above contributions is available for download here.

Energy Harvesting

In addition to optimizing the onboard energy consumption, we are studying how to efficiently harvest solar energy on robots. See Solar Power project page for details.

Related Publications

2016
11P. A. Plonski, J. Vander Hook, C. Peng, N. Noori, V. Isler
Environment Exploration in Sensing Automation for Habitat Monitoring
IEEE Transactions on Automation Science and Engineering, PP(99): 1-14, 2016.
10Patrick A. Plonski, Joshua Vander Hook, Volkan Isler
Environment and Solar Map Construction for Solar-Powered Mobile Systems
IEEE Trans. Robotics, 32(1): 70--82, 2016.
9P. Tokekar, J. V. Hook, D. Mulla, V. Isler
Sensor Planning for a Symbiotic UAV and UGV System for Precision Agriculture
IEEE Transactions on Robotics, PP(99): 1-1, 2016.
2014
8P. Tokekar, N. Karnad, V. Isler
Energy-Optimal Trajectory Planning for Car-Like Robots
Autonomous Robots, 2014.
pdf,.bib
7P. A. Plonski, V. Isler
A Competitive Online Algorithm for Exploring a Solar Map
In Proc. International Conference on Robotics and Automation, 2014.
pdf,.bib
6 Patrick A Plonski, Joshua Vander Hook, Volkan Isler
Environment and Solar Map Construction for Solar-Powered Mobile Systems
Technical Report, Department of Computer Science, University of Minnesota, 2014.
pdf,.bib
2013
5 Patrick A Plonski, Pratap Tokekar, Volkan Isler
Energy-Efficient Path Planning for Solar-Powered Mobile Robots
Journal of Field Robotics, 2013.
pdf,.bib
4 Narges Noori, Patrick Plonski, Alessandro Renzaglia, Pratap Tokekar, Joshua Vander Hook, Volkan Isler
Long-Term Search Through Energy Efficiency and Harvesting
Technical Report, Department of Computer Science, University of Minnesota, 2013.
pdf,.bib
2012
3 Patrick A. Plonski, Pratap Tokekar, Volkan Isler
Energy-Efficient Path Planning for Solar-Powered Mobile Robots
In The 12th International Symposium on Experimental Robotics, 2012.
pdf,.bib
2011
2P. Tokekar, N. Karnad, V. Isler
Energy-Optimal Velocity Profiles for Car-Like Robots
In Proc. IEEE Int. Conf. on Robotics and Automation, 2011.
pdf,.bib
2009
1O. Tekdas, N. Karnad, V. Isler
Efficient Strategies for Collecting Data from Wireless Sensor Network Nodes using Mobile Robots
In 14th International Symposium on Robotics Research (ISRR), 2009.
pdf,.bib