Realtime motion path generation using subtargets in a changing environment

Bruijnen, Dennis; Helvoort, J. van; Molengraft, René van de

doi:10.1109/acc.2006.1657385

Cited by 5 publications

(8 citation statements)

References 11 publications

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“…In [3], a computationally cheap algorithm is presented which generates a motion path complying with the robot's physical limitations such as velocity, acceleration and jerk limitations in all directions. A simulation example is shown in Fig.…”

Section: B Motion Path Generationmentioning

confidence: 99%

“…In order to account for physical limitations of the robot (e.g. maximum acceleration and speed), a desirable reference trajectory from r O (t) to r O target (t) is computed by the smoothifier S, [3]. The difference between the reference trajectory r O ref (t) and r O (t) is transformed back to the encoder frame E used for collocated feedback control (C).…”

Section: Motion Controlmentioning

confidence: 99%

“…Recent research in this area focusses on color segmentation [2], [18], autocalibration [12], and adaptivity [11]. Results about path generation and tracking using this data can be found in [3], [5], [14], [19].…”

Section: Introductionmentioning

confidence: 99%

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From Vision to Realtime Motion Control for the RoboCup Domain

Bruijnen

Aangenent

Helvoort

et al. 2007

2007 IEEE International Conference on Control Applications

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In this paper, an overview is given of how the path from vision to motion has been developed in the TechUnited team. The vision module includes: (i) color calibration using a union of convex hulls to select an area in the 3D-colorspace, (ii) automatic calibration of the mapping from the camera image to the field via a genetic algorithm, (iii) self localization based on field lines. The output of the vision module is used by the motion module which includes: (i) vision and encoder sensor fusion by monitoring the drift caused by odometry, (ii) generating a motion path complying with the robot's limitations to prevent wheel slippage, (iii) collocated motion control. In contrast to closing the loop on vision, our approach uses wheel encoders as the basis for motion control, which has several advantages such as less delay due to a higher sampling frequency. Vision is only used to compensate for slow drift caused by slip in the wheel-surface contact.

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Section: B Motion Path Generationmentioning

confidence: 99%

Section: Motion Controlmentioning

confidence: 99%

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From Vision to Realtime Motion Control for the RoboCup Domain

Bruijnen

Aangenent

Helvoort

et al. 2007

2007 IEEE International Conference on Control Applications

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“…The Potential Field Theory (PFT) is one of the most widely used approaches for the aforementioned path planning problem [3], [4], [5]. Rimon and Koditschek developed a special artificial potential function, called a navigation function, that guarantees collisionfree motion and convergence to the destination from almost all initial free configurations.…”

Section: Introductionmentioning

confidence: 99%

“…Bruijnen et N Sadegh is with the The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta Georgia 30332-0405, sadegh@gatech.edu al. [5] proposed an extension of the PFT that can handle a changing obstacle field and generates a sub-optimal smooth path with bounds on the allowed velocity, acceleration and jerk. While the PFT is well suited for a varying obstacle fields, it does not necessarily generate a globally optimal path and in many instances it may render only a local optimum away from the destination.…”

Section: Introductionmentioning

confidence: 99%

Time-optimal motion planning of autonomous vehicles in the presence of obstacles

Sadegh

2008

2008 American Control Conference

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Abstract-This paper presents a novel motion planning approach inspired by the Dynamic Programming (DP) for mobile robots and other autonomous vehicles. The proposed discretetime algorithm enables a multi-degree of freedom vehicle to reach its destination through an arbitrary obstacle field in a minimum number of time steps. Furthermore, the resulting optimal path is guaranteed to posses the required degree of smoothness and incorporates the motion state constraints such as velocity, acceleration, and jerk limits. The algorithm is proven to terminate in a finite number of steps without its computational complexity increasing with the type or number of obstacles. The effectiveness of the algorithm is demonstrated on a mobile robot modeled as a point-mass in a 2-dimensional space subject to velocity and acceleration limits.

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Dual objective motion planning subject to state constraints

Sadegh

2015

Robotica

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SUMMARYThis paper presents a novel motion planning approach inspired by the Dynamic Programming (DP) applicable to multi degree of freedom robots (mobile or stationary) and autonomous vehicles. The proposed discrete–time algorithm enables a robot to reach its destination through an arbitrary obstacle field in the fewest number of time steps possible while minimizing a secondary objective function. Furthermore, the resulting optimal trajectory is guaranteed to be globally optimal while incorporating state constraints such as velocity, acceleration, and jerk limits. The optimal trajectories furnished by the algorithm may be further updated in real time to accommodate changes in the obstacle field and/or cost function. The algorithm is proven to terminate in a finite number of steps without its computational complexity increasing with the type or number of obstacles. The effectiveness of the global and replanning algorithms are demonstrated on a planar mobile robot with three degrees of freedom subject to velocity and acceleration limits. The computational complexity of the two algorithms are also compared to that of an A*–type search.

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Realtime motion path generation using subtargets in a changing environment

Cited by 5 publications

References 11 publications

From Vision to Realtime Motion Control for the RoboCup Domain

From Vision to Realtime Motion Control for the RoboCup Domain

Time-optimal motion planning of autonomous vehicles in the presence of obstacles

Dual objective motion planning subject to state constraints

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