Robot path planning: complexity, flexibility and application scope

The three essential requirements of sensor based robotic arm manipulation and mobile robot navigation are localization (position and pose determination), environmental mapping (forming a model of the working environment) and trajectory (for manipulators) or path (for mobile robots) planning. This paper concerns the last of these and compares a number of approaches with regard to complexity, flexibility and application scope. Knowledge about these aspects is crucial in choosing an effective strategy for particular application domains.

[1]  R. A. Jarvis,et al.  Collision-free trajectory planning using distance transforms , 1985 .

[2]  Steven M. LaValle,et al.  Randomized Kinodynamic Planning , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[3]  Ray Jarvis,et al.  Robotic path planning using rapidly exploring random trees , 2003 .

[4]  Ray A. Jarvis,et al.  Covert Robotics: hiding in known environments , 2004, IEEE Conference on Robotics, Automation and Mechatronics, 2004..

[5]  S. LaValle.  Rapidly-exploring random trees : a new tool for path planning , 1998 .

[6]  Michael T. Goodrich,et al.  K-D Trees Are Better when Cut on the Longest Side , 2000, ESA.

[7]  W. W. Bledsoe,et al.  Review of "Problem-Solving Methods in Artificial Intelligence by Nils J. Nilsson", McGraw-Hill Pub. , 1971, SGAR.

[8]  Sunil Arya,et al.  An optimal algorithm for approximate nearest neighbor searching fixed dimensions , 1998, JACM.

[9]  Ray A. Jarvis,et al.  Covert robotics: covert path planning in unknown environments , 2004 .

[10]  Azriel Rosenfeld,et al.  Sequential Operations in Digital Picture Processing , 1966, JACM.

[11]  Tomás Lozano-Pérez,et al.  An algorithm for planning collision-free paths among polyhedral obstacles , 1979, CACM.

[12]  Ray Jarvis,et al.  Covert path planning for autonomous robot navigation in known environments , 2003 .

[13]  Michael T. Goodrich,et al.  Balanced aspect ratio trees: combining the advantages of k-d trees and octrees , 1999, SODA '99.

[14]  Nils J. Nilsson,et al.  Problem-solving methods in artificial intelligence , 1971, McGraw-Hill computer science series.