Turning Segways into soccer robots

2007 International Symposium on Computational Intelligence in Robotics and Automation

2007

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Abstract-Learning motion models of a moving object is a challenge for autonomous robots. We address the particular instance of parameter learning when tracking object motions in a switching multi-model system. We present a general algorithm of joint parameter-state estimation based on multimodel particle filter. We apply the approach to a specific balltracking problem and extend the algorithm to learn model parameters in a dynamic Bayesian network (DBN). We show empirical results in simulation and in a team robot soccer environment, as a substrate for applying the learned models to object tracking in a team. The learning capability allow the tracker to much more effectively track mobile objects.

“…In our previous work, we have developed a Segway RMP robot base capable of playing Segway soccer [5]. The main sensor on our robots are two cameras.…”

Section: B Segway Rmp Soccer Robotmentioning

confidence: 99%

Learning Tactic-Based Motion Models of a Moving Object with Particle Filtering

2007 International Symposium on Computational Intelligence in Robotics and Automation

2007

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“…In our previous work, we have developed a Segway RMP robot base capable of playing Segway soccer [14]. The main sensor on our robots are two cameras.…”

Section: B Segway Rmp Soccer Robotmentioning

confidence: 99%

Learning tactic-based motion models with fast particle smoothing

2008 IEEE International Conference on Robotics and Automation

2008

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Abstract-Learning parameters of a motion model is an important challenge for autonomous robots. We address the particular instance of parameter learning when tracking motions with a switching state-space model. We present a general algorithm for dealing simultaneously with both unknown fixed model parameters and state variables. Using an ExpectationMaximization approach, we apply a tactic-based multi-model particle filter to estimate the state variables in the E-step, and use particle smoothing to update the parameters in the M-step. We test our algorithm both in simulation and in a team robot soccer environment, as a substrate for applying the learned models to object tracking in a team. One of the soccer robots learns the actuation model of its teammate. The experimental results show that the particle smoothing efficiency is substantially increased and the tracking performance is significantly improved using the learned teammate actuation model. This paper addresses estimating state and learning motion models in such a hybrid-state system. We are interested in tracking the ball in a robot soccer domain. This is a highly dynamic and multi-agent environment. All the robots in the field can actuate over the ball, e.g., grab and kick the ball, making the motion model of the ball very complex [3]. The good news is that we can acquire information about the ball motion from multiple sources besides the sensor. First, the robot's tactics provides valuable information and a tacticbased motion modeling and tracking algorithm is introduced in such scenarios [4]. Second, when the robot is playing a game as a member of a team, the team coordination knowledge provides further information that can be incorporated into the motion modeling and tracking process. We based our work upon a plan-dependent tracking algorithm called play-based tracking [5]. I. INTRODUCTIONAny model consists of one or multiple parameters. Usually the model parameters are set by a human expert, based upon the experience with the environment and the robot. In this paper, we present a novel method of automating the procedure of acquiring this probabilistic motion model. This approach deals simultaneously with both unknown fixed

“…Each team is identified by their distinct color. The ball is in orange [4]. We construct two single target trackers in the system, for the ball and the teammate respectively.…”

Section: Tracking Scenariomentioning

confidence: 99%

Effective team-driven multi-model motion tracking

Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction

2006

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Autonomous robots use sensors to perceive and track objects in the world. Tracking algorithms use object motion models to estimate the position of a moving object. Tracking efficiency completely depends on the accuracy of the motion model and of the sensory information. Interestingly, when the robots can actuate the object being tracked, the motion can become highly discontinuous and nonlinear. We have previously developed a successful tracking approach that effectively switches among object motion models as a function of the robot's actions. If the object to be tracked is actuated by a team, the set of motion models is quite more complex. In this paper, we report on a tracking approach that can use a dynamic multiple motion model based on a team coordination plan. We present the multi-model probabilistic tracking algorithms in detail and present empirical results both in simulation and real robot test. Our physical team is composed of a robot and a human in a real Segway soccer game scenario. We show how the coordinated plan allows the robot to better track a mobile object through the effective interaction with its human teammate.