An efficient method for computing the minimum distance and predicting collisions between moving objects is presented. This problem has been incorporated in the framework of an in-line motion planning algorithm to satisfy collision avoidance between a robot and moving objects modelled as convex polyhedra. In the beginning, the deterministic problem where the information about the objects is assumed to be certain is examined. If instead of the Euclidean norm, L1 or L∞ norms are used to represent distance, the problem becomes a linear programming problem. The stochastic problem is formulated, where the uncertainty is induced by sensing and the unknown dynamics of the moving obstacles. Two problems are considered: First, filtering of the distance between the robot and the moving object, at the present time. Second, prediction of the minimum distance in the future, in order to predict the collision time.
- Collision avoidance
- collision prediction
- distance functions
- random search
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering