Multiple human trajectory prediction and cooperative navigation modeling in crowded scenes

As mobile robots start operating in environments crowded with humans, human-aware navigation is required to make these robots navigate safely, efficiently and in socially compliant manner. People navigate in an interactive and cooperative fashion so that, they are able to find their path to a destination even if there is no clear route leading to it. There are significant efforts to solve this problem for mobile robots; however, they are not scalable to high human density and learning based approaches depend heavily on the context and configuration of the set they are trained with. We develop a method which infers initial trajectories from Gaussian processes and updates these trajectories jointly for all agents using a cost based interaction approach. We condition Gaussian processes online with the best hypothesis at each step of prediction horizon. The method is tested on a common public dataset and it is shown that it outperforms two state-of-the-art approaches in terms of human-likeness of predicted trajectories

Developing a Motion Controller for Autonomous Agricultural Robot Otonom Tarim Robotu Icin Hareket Planlayicinin Gelistirilmesi

© 2020 IEEE.The advantages of autonomous agriculture over traditional agriculture have been reported both in national and international literature. Motion and trajectory planning is one of the fundamental problems an agricultural robot has to solve. Several trajectory guidance patterns have been developed in the literature. In this work, a visual trajectory planning interface based on Google maps is designed, and A+ pattern is implemented on it. The position of the vehicle obtained via GPS is shown on the interface in real time

Self-learning Road Detection with Stereo Vision

It is a hard to solve problem to detect traversable or road regions especially in unstructured roads or paths. In mobile robot applications, robots usually enter these kinds of roads and regions. To successfully complete its mission, it is important to find roads in these environments reliably. In this paper a novel unstructured road detection algorithm with the capability of learning road regions continuously is proposed

Adaptive unstructured road detection using close range stereo vision

Detection of road regions is not a trivial problem especially in unstructured and/or off-road domains since traversable regions of these environments do not have common properties unlike urban roads or highways. In this paper a novel unstructured road detection algorithm that can continuously learn the road region is proposed. The algorithm gathers close-range stereovision data and uses this information to estimate the long-range road region. The experiments show that the algorithm gives satisfactory results even under changing light conditions