We approach dynamic collision avoidance problem from the perspective of designing collision avoidance systems for unmanned aerial vehicles. Before unmanned aircraft can fly safely in civil airspace, robust airborne collision avoidance systems must be developed. Instead of hand-crafting a collision avoidance algorithm for every combination of sensor and aircraft configurations, we investigate au...

Worry is considered as one of the shared cognitive components in generalized anxiety disorder (GAD) and obsessive compulsive disorder (OCD). The study of cognitive factors influencing worry simultaneously has important implications for psychopathology. These factors have been discussed separately in different theoretical models. The aim of the present study was to investigate the simple and mul...

This paper describes a multi-robot collision avoidance system based on the velocity obstacle paradigm. In contrast to previous approaches, we alleviate the strong requirement for perfect sensing (i.e. global positioning) using Adaptive Monte-Carlo Localization on a per-agent level. While such methods as Optimal Reciprocal Collision Avoidance guarantee local collision-free motion for a large num...

Reinforcement learning can enable complex, adaptive behavior to be learned automatically for autonomous robotic platforms. However, practical deployment of reinforcement learning methods must contend with the fact that the training process itself can be unsafe for the robot. In this paper, we consider the specific case of a mobile robot learning to navigate an a priori unknown environment while...

We address the problem of uncertainty-aware local collision avoidance within the context of time-to-collision based navigation of multiple agents. We consider two specific models that account for uncertainty in the future trajectories of interacting agents: an isotropic model which conservatively considers all possible errors, and an adversarial model that assumes the error is towards a head-on...

CALU is a multi-robot collision avoidance system based on the velocity obstacle paradigm. In contrast to previous approaches, we alleviate the strong requirement for perfect sensing (i.e. global positioning) using Adaptive Monte-Carlo Localization on a per-agent level.