4 accepted papers
Manual derivation of optimal robot motions for task completion is difficult, especially when a robot is required to balance its actions between opposing preferences. One solution has been proposed to automatically learn near optimal motions with Reinforcement Learning (RL). This has been successful
Motion planning in stochastic dynamic uncertain environments is critical in several applications such as human interacting robots, autonomous vehicles and assistive robots. In order to address these complex applications, several methods have been developed. The most successful methods often predict…
Highly dynamic environments pose a particular challenge for motion planning due to the need for constant evaluation or validation of plans. However, due to the wide range of applications, an algorithm to safely plan in the presence of moving obstacles is required. In this paper, we propose a novel t…
Motion planning in stochastic dynamic environments is difficult due to the need for constant plan adjustment caused by the uncertainty of the environment. There are many motion planning problems, including flight coordination and autonomous vehicles, that require an algorithm to predict obstacle mot…