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Autonomous Target Tracking
Overview
Our motivation is to build autonomous robots that can follow people and
recognize their activities. Such capabilities are important in
applications such as home care for the elderly, intelligent
environments, and iteractive media.
In particular, the goal of this project is to develop reliable and
efficient motion strategies for an autonomous robot to follow a target
and keep it within the sensor range, despite occlusion by obstacles. We are
currently investigating two
approaches, POMDPs
and greedy strategies.
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Experimental
setup: a Sick laser mounted a Pioneer DX3 indoor robot.
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POMDP Trackers
Target tracking has two variants that are often studied independently
with different approaches: target
searching requires a robot to find a target initially not
visible, and target
following requires a robot to maintain visibility on a
target initially visible. We use partially observable Markov
decision process (POMDP) to build a single model that unifies target
searching and target following. The resulting POMDP policy exhibits
interesting tracking behaviors, such as anticipatory moves that exploit
target dynamics, information-gathering moves that reduce target
position uncertainty, and energy-conserving actions that allow the
target to get out of sight, but do not compromise tracking performance.
Some preliminary results are shown in the videos below. The problem
setting is motivated by homecare applications. Imagine that an elderly
person moves around at home and has a
call button to call a robot over for help. The call status stays on for
some time and then goes off. If the robot arrives
while the call status is on, it gets a reward; otherwise, it gets no
reward. Clearly the robot should stay close the person in
order to improve the chance of receiving rewards, but at the same time,
the robot needs to minimize movement in order to
reduce power consumption. So the naive strategy of following right
behind the person does not work well. What is
interesting about these examples is that the robot manage to "track"
the target while the target is outside the robot's sensor visibility
region most of the time.
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Quicktime video,
1.1MB
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Quicktime video,
1.0MB
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Quicktime video,
2.3MB
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| Light blue areas
indicate obstacles. The green area around the robot
indicate sensor' visibility region. The various shades of gray show the
robot’s beli ef of the target position. Lighter color
indicates higher probability. |
References
- D. Hsu, W.S. Lee, and N. Rong. A point-based POMDP planner for target tracking. In Proc. IEEE Int. Conf. on Robotics
& Automation, pp. 2644–2650, 2008.
BibTeX PDF
Greedy Followers
POMDP trackers integrate global information on the target
behavior
and the environment for optimal decision making. When
little is
known about the target behavior or the environment, a local greedy
strategy is more effective. Key to our algorithm is the definition of a
risk function,
which tries to capture the target’s ability in escaping from
the robot sensors’ visibility region in both short and long
terms. To select actions effectively, the robot must balance between
the short-term goal of preventing the immediate loss of the target and
the long-term goal of keeping it visible for the maximum duration
possible. Interestingly, a good comprise can be achieved, using only
local information available to the robot’s sensors. By
analyzing the local geometry, our algorithm computes a global risk
function as a weighted sum of components, each associated with a single
visibility constraint. It then chooses an action to minimize the risk
locally in a greedy fashion.
As the algorithm uses only local geometric information
available to the robot’s visual sensors, it does not require
a global map and thus bypasses the difficulty of localization with
respect to a
global map. Furthermore, uncertainty in sensing and motion control does
not accumulate. This improves the reliability of tracking.
This approach can be developed in both 2-D and 3-D.
The 3-D case is,
however, much more challenging technically, as both the robot and the
target gain one more degree of freedom to maneuver and the
visibility
relationships in 3-D are more complex than those in 2-D. More details
can be found here.
References
- T. Bandyopadhyay, D. Hsu, and Ang Jr., M.H.. Motion strategies for people tracking in cluttered dynamic
environments . In Proc. Int. Symp. on Experimental Robotics, 2008. To appear.
BibTeX PDF - T. Bandyopadhyay, Ang Jr., M.H., and D. Hsu. Motion
planning for 3-D target tracking among obstacles. In Proc.
Int. Symp. on Robotics Research, 2007.
BibTeX PDF
- T. Bandyopadhyay, Y.P. Li, Ang Jr., M.H., and D. Hsu. A
greedy strategy for tracking a locally predictable target among
obstacles. In Proc. IEEE Int. Conf. on Robotics
& Automation, pp. 2342–2347, 2006.
BibTeX PDF
- T. Bandyopadhyay, Y.P. Li, Ang Jr., M.H., and D. Hsu. Stealth
tracking of an unpredictable target among obstacles. In M.
Erdmann and others, editors, Algorithmic Foundations of
Robotics VI---Proc. Workshop on the Algorithmic Foundations of Robotics
(WAFR), pp. 43–58, Springer, 2004.
BibTeX PDF
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