Abstract
When computing the trajectory of an autonomous vehicle, inevitable collision states must be avoided at all costs, so no harm comes to the device or pedestrians around it. In dynamic environments, considering collisions as binary events is risky and inefficient, as the future position of moving obstacles is unknown. We introduce a time-dependent probabilistic collision state checker system, which traces a safe route with a minimum collision probability for a robot. We apply a sequential Bayesian model to calculate approximate predictions of the movement patterns of the obstacles, and define a time-dependent variation of the Dijkstra algorithm to compute statistically safe trajectories through a crowded area. We prove the efficiency of our methods through experimentation, using a self-guided robotic device.
Highlights
In order for an autonomous vehicle to properly operate in an uncontrolled environment, safe trajectories must be ensured so it does not harm itself or pedestrians nearby
The Probabilistic Collision States method (PCS) assumes probable future occupation areas for each observed object, which depend on the geometry of both the object and the robot
To offer a safer and more efficient alternative to this model, in this paper we present a Bayesian time-dependent modification of the PCS system, which corrects all these flaws and meets the computational requirements to be implemented in a real autonomous vehicle
Summary
In order for an autonomous vehicle to properly operate in an uncontrolled environment, safe trajectories must be ensured so it does not harm itself or pedestrians nearby. The concept of inevitable collision state (ICS) [1] is defined as the configuration of an autonomous robotic vehicle for which the event of a collision with an obstacle is unavoidable. This definition is valid for environments with objects that remain static or that move following deterministic trajectories. The Probabilistic Collision States method (PCS) assumes probable future occupation areas for each observed object, which depend on the geometry of both the object and the robot
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