A Convergent Dynamic Window Approach to Obstacle Avoidance

Petter Ogren and Naomi Ehrich Leonard

The dynamic window approach (DWA) is a well known navigation scheme developed by Fox et. al. and extended by Brock and Khatib. It is safe by construction and has been shown to perform very efficiently in experimental setups. However, one can construct examples where the proposed scheme fails to attain the goal configuration. What has been lacking is a theoretical treatment of the algorithm's convergence properties. Here we present such a treatment by merging the ideas of the DWA with the convergent but less performance-oriented scheme suggested by Rimon and Koditschek. Viewing the DWA as a Model Predictive Control (MPC) method and using the Control Lyapunov Function (CLF) framework of Rimon and Koditschek we draw inspiration from a MPC/CLF framework put forth by Primbs et. al. to propose a version of the DWA that is tractable and convergent.