A Combined Monte-Carlo Localization and Tracking Algorithm for RoboCup

2009 IEEE International Conference on Robotics and Automation

2009

In this paper, the path following problem of an omnidirectional mobile robot has been studied. Given the error dynamic model derived from the robot state vector and the path state vector, model predictive control (MPC) is employed to design the control law, which can deal explicitly with the rate of progression of a virtual vehicle to be followed along the path. The distinct advantage over other control strategies is that input and system constraints are able to be handled straightforwardly in the optimization problem so that the robot can travel safely with a high velocity. Unlike nonholonomic mobile robots, omnidirectional mobile robots, which we focus on in this paper, have simultaneously and independently controlled rotational and translational motion capabilities. Then, our purposed MPC controller was validated by experiments with a real omnidirectional mobile robot.

Section: Resultsmentioning

confidence: 99%

Path following for an omnidirectional mobile robot based on model predictive control

Kanjanawanishkul

2009 IEEE International Conference on Robotics and Automation

2009

“…It has an omnidirectional camera as sole sensor, which is used for self localization. Thanks to [21], the self localization applied for the RoboCup field has been employed in our experiments. This self-localization algorithm is based on probabilistic Monte-Carlo localization (MCL).…”

Section: Resultsmentioning

confidence: 99%

A model-predictive approach to formation control of omnidirectional mobile robots

Kanjanawanishkul

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems

2008

Abstract-This paper presents a solution to the problem of steering a group of real omnidirectional mobile robots along a given path, while maintaining a desired formation pattern. This problem can be divided into a leader agent subproblem and a follower agent subproblem such that a leader agent follows a given path and each follower agent tracks a trajectory, estimated by using the leader's information. In this paper, we exploit nonlinear model predictive control (NMPC) as a local control law for real-world experiments due to its advantages of taking the robot constraints and future information into account. To solve the path following problem for the leader agent, we propose to integrate the rate of progression of a virtual vehicle to be followed along that path into the local cost function of NMPC. After the open-loop optimization problem is solved, the optimal rate of progression at each time step in the future is obtained. This information and the leader's current state are broadcasted to all follower agents. With respect to a desired formation configuration and a reference path, each follower agent can estimate its own reference trajectory by using the leader's information and its time stamp. NMPC is also employed as a local control law to steer the follower agent to track that reference trajectory. Our approach was validated by experiments using three omnidirectional mobile robots.

“…Based on the real-time output signal of the camera, a self-localization algorithm described in [22] determines the robot's position in the play field, and a fast object detection algorithm is used to get the real world positions of other objects as introduced in [23].…”

Section: Resultsmentioning

confidence: 99%

Nonlinear predictive control of an omnidirectional robot dribbling a rolling ball

2008 IEEE International Conference on Robotics and Automation

2008

Self Cite

Abstract-This paper focuses on the dribbling control problem of an omnidirectional mobile robot and a rolling ball in the RoboCup Middle Size domain. Because the ball easily slides away from the robot when the ball moves along a curve, dribbling control is more challenging than the normal mobile robot motion control problem. Based on an introduced reference point with respect to the robot body and a sophisticated planning method of robot pose, the nonlinear predictive control is used to steer the robot to follow the planned poses so as to prevent the ball from leaving the robot. Real-world experiments showed that nonlinear predictive control is capable of solving the pose following problem in a real-time application.