Probabilistic Collision Constraint for Motion Planning in Dynamic Environments
This addresses safety challenges for autonomous navigation systems in uncertain dynamic settings, representing an incremental improvement over existing methods.
The paper tackles the problem of real-time collision avoidance in dynamic environments by incorporating robot and obstacle state uncertainties, deriving a tight upper bound for collision probability and formulating it as a motion planning constraint, achieving successful collision avoidance in simulations with mobile robots and quadrotors.
Online generation of collision free trajectories is of prime importance for autonomous navigation. Dynamic environments, robot motion and sensing uncertainties adds further challenges to collision avoidance systems. This paper presents an approach for collision avoidance in dynamic environments, incorporating robot and obstacle state uncertainties. We derive a tight upper bound for collision probability between robot and obstacle and formulate it as a motion planning constraint which is solvable in real time. The proposed approach is tested in simulation considering mobile robots as well as quadrotors to demonstrate that successful collision avoidance is achieved in real time application. We also provide a comparison of our approach with several state-of-the-art methods.