A Hybrid Obstacle Avoidance Method: Follow the Gap with Dynamic Window Approach

Follow the Gap Method (FGM) is an obstacle avoidance method which uses gap arrays. This method recursively directs robot to the goal state while avoiding the obstacles through the safest gap. Since FGM is a geometric method, it does not consider the robot dynamics. For this reason, oscillations or collisions due to robot dynamics is possible. On the other hand, FGM calculates a desired heading angle but it does not give linear and angular velocity reference. Dynamic Window Approach (DWA) is one of the most popular obstacle avoidance algorithm which does take robot dynamics into consideration. It calculates best angular and linear velocity pair which is chosen by an objective function. In this paper, a FGM-DW approach which uses the strongest elements of FGM and DWA methods to achieve safe, smooth and fast navigation is proposed. The FGM-DW approach provides these concerns and meets the low level angular and rotational velocity requirement of FGM.

[1]  Tomás Lozano-Pérez,et al.  Spatial Planning: A Configuration Space Approach , 1983, IEEE Transactions on Computers.

[2]  Reid G. Simmons,et al.  The curvature-velocity method for local obstacle avoidance , 1996, Proceedings of IEEE International Conference on Robotics and Automation.

[3]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[4]  Brian P. Gerkey,et al.  Robot Developer Kits , 2011 .

[5]  Andrew Howard,et al.  Design and use paradigms for Gazebo, an open-source multi-robot simulator , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[6]  Zvi Shiller,et al.  Efficient and safe on-line motion planning in dynamic environments , 2009, 2009 IEEE International Conference on Robotics and Automation.

[7]  Javier Minguez,et al.  The obstacle-restriction method for robot obstacle avoidance in difficult environments , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[9]  Yoram Koren,et al.  The vector field histogram-fast obstacle avoidance for mobile robots , 1991, IEEE Trans. Robotics Autom..

[10]  Volkan Sezer,et al.  A novel obstacle avoidance algorithm: "Follow the Gap Method" , 2012, Robotics Auton. Syst..

[11]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[12]  Wolfram Burgard,et al.  The dynamic window approach to collision avoidance , 1997, IEEE Robotics Autom. Mag..

[13]  Javier Minguez,et al.  Nearness diagram navigation (ND): a new real time collision avoidance approach , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[14]  Nadeem Javaid,et al.  Control Strategies for Mobile Robot With Obstacle Avoidance , 2013, ArXiv.

[15]  Volkan Sezer.  Combined fuzzy approach for online speed planning and control with real vehicle implementation , 2015 .

[16]  Oliver Brock,et al.  High-speed navigation using the global dynamic window approach , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[17]  Iwan Ulrich,et al.  VFH+: reliable obstacle avoidance for fast mobile robots , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

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