Can robots help each other to plan optimal paths in dynamic maps?

Mobile robots need to plan an optimal and collision-free path in complex environments to the various service locations in the map. Robot perception is limited to sensor range and hence robots are only aware of the obstacles within the range of its sensor. Moreover, the knowledge of new obstacles observed by each robot is kept local to itself. Due to these limitations, robots are often unaware of the new obstacles or path blockages in remote sections of the map. We proposed a knowledge sharing architecture in which the robots share the knowledge of new obstacles and blocked paths with each other. This enables the robots to update their map with the new and remote obstacles, and plan efficient paths with the timely information. This eliminates the need for re-planning of paths in traditional navigation methods. Experimental results show that the proposed method is efficient for multi-robot navigation in large and dynamic maps.

[1]  Yi Guo,et al.  A distributed and optimal motion planning approach for multiple mobile robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[2]  Narendra Ahuja,et al.  A potential field approach to path planning , 1992, IEEE Trans. Robotics Autom..

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

[4]  Jonas Stenzel,et al.  Concept of decentralized cooperative path conflict resolution for heterogeneous mobile robots , 2016, 2016 IEEE International Conference on Automation Science and Engineering (CASE).

[5]  Yukinori Kobayashi,et al.  An intelligent docking station manager for multiple mobile service robots , 2015, 2015 15th International Conference on Control, Automation and Systems (ICCAS).

[6]  Mark H. Overmars,et al.  Coordinated path planning for multiple robots , 1998, Robotics Auton. Syst..

[7]  Alessandro Gasparetto,et al.  Path Planning and Trajectory Planning Algorithms: A General Overview , 2015 .

[8]  Anthony Stentz.  Optimal and Efficient Path Planning for Unknown and Dynamic Environments , 1993 .

[9]  Ankit A. Ravankar,et al.  Map building from laser range sensor information using mixed data clustering and singular value decomposition in noisy environment , 2011, 2011 IEEE/SICE International Symposium on System Integration (SII).

[10]  Yukinori Kobayashi,et al.  On a Hopping-Points SVD and Hough Transform-Based Line Detection Algorithm for Robot Localization and Mapping , 2016 .

[11]  Anthony Stentz,et al.  The Focussed D* Algorithm for Real-Time Replanning , 1995, IJCAI.

[12]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[13]  Nak Young Chong,et al.  Robot collaboration in warehouse , 2016, 2016 16th International Conference on Control, Automation and Systems (ICCAS).

[14]  Jean-Claude Latombe,et al.  Robot motion planning , 1970, The Kluwer international series in engineering and computer science.

[15]  Vadim Indelman,et al.  Multi-robot decentralized belief space planning in unknown environments via efficient re-evaluation of impacted paths , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[16]  S. LaValle,et al.  Randomized Kinodynamic Planning , 2001 .

[17]  Yukinori Kobayashi,et al.  SHP: Smooth Hypocycloidal Paths with Collision-Free and Decoupled Multi-Robot Path Planning , 2016 .

[18]  N. Zulkifli,et al.  Robotic motion planning in unknown dynamic environments: Existing approaches and challenges , 2015, 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS).

[19]  Yukinori Kobayashi,et al.  Algorithms and a Framework for Indoor Robot Mapping in a Noisy Environment Using Clustering in Spatial and Hough Domains , 2015 .

[20]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[21]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .