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Abstract | ||
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Target tracking among obstacles is an interesting class of motion planning problems that combine the usual motion constraints with robot sensors' visibility constraints. In this paper, we introduce the notion of vantage time and use it to formulate a risk function that evaluates the robot's advantage in maintaining the visibility constraint against the target. Local minimization of the risk function leads to a greedy tracking strategy. We also use simple velocity prediction on the target to further improve tracking performance. We compared our new strategy with earlier work in extensive simulation experiments and obtained much improved results. I. INTRODUCTION The target tracking problem considers motion strategies for an autonomous mobile robot to track a moving target among obstacles, i.e., to keep the target within the robot sensor's visibility region. Target tracking has many applications. In home care settings, a tracking robot can follow elderly people around and alert caregivers of emergencies. In security and surveillance systems, tracking strategies enable mobile sensors to monitor moving targets in cluttered environments. Target tracking is an especially interesting class of motion planning problems. Just as in classic motion planning (7), we must consider motion constraints resulting from both obstacles in the environment and the robot's mechanical limitations. In particular, the robot must not collide with obstacles. Target tracking has the additional visibility constraints due to sensor limitations, e.g., obstacles blocking the view of the robot's camera. The robot must move in such a way that the target remains visible at all times. Both motion constraints and visibility constraints play important roles in target tracking. Inspired by earlier work (5), we propose a greedy strategy for target tracking. It uses the robot's sensor to acquire local information on the target and the environment, and use this information to compute the robot's motion at each step. Thus, it does not need ap rioriknowledge of the environment or localization with respect to (w.r.t.) a global map. The key element of the greedy strategy is a local function ϕ that gives a combined estimate of the immediate risk of losing the target and the future risk. Our definition of ϕ is based on two important considerations: • Vantage time, which is a combined estimate of the robot's ability to maneuver against the target in both the current and future time. • The target's instantaneous velocity, which can be esti- mated locally, indicates the target's future movement. We show that by leveraging these two considerations, our new strategy achieves significant improvement in performance over the earlier strategy presented in (5). In the following, after a brief review of related work (Section II), we state the target tracking problem formally (Section III) and present our solution (Section IV). We have implemented the new tracking strategy and compared it with an existing one in simulated environments. The simulation results are shown in Section V. Finally, we conclude with some remarks on future research directions (Section VI). |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ROBOT.2006.1642052 | Orlando, FL |
Keywords | Field | DocType |
mobile robots,motion control,path planning,risk analysis,target tracking,mobile robots,motion planning,risk function,target tracking among obstacles,vantage time,velocity prediction,visibility constraint | Motion planning,Visibility,Motion control,Robotic sensing,Control engineering,Minification,Engineering,Robot,Trajectory,Mobile robot | Conference |
Volume | Issue | ISSN |
2006 | 1 | 1050-4729 |
ISBN | Citations | PageRank |
0-7803-9505-0 | 23 | 1.08 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tirthankar Bandyopadhyay | 1 | 171 | 13.51 |
| Yuanping Li | 2 | 48 | 6.10 |
| Marcelo H. Ang | 3 | 775 | 98.60 |
| David Hsu | 4 | 824 | 50.86 |