16,310 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
On green routing and scheduling problem
The vehicle routing and scheduling problem has been studied with much
interest within the last four decades. In this paper, some of the existing
literature dealing with routing and scheduling problems with environmental
issues is reviewed, and a description is provided of the problems that have
been investigated and how they are treated using combinatorial optimization
tools
Motion Planning for Autonomous Ground Vehicles Using Artificial Potential Fields: A Review
Autonomous ground vehicle systems have found extensive potential and
practical applications in the modern world. The development of an autonomous
ground vehicle poses a significant challenge, particularly in identifying the
best path plan, based on defined performance metrics such as safety margin,
shortest time, and energy consumption. Various techniques for motion planning
have been proposed by researchers, one of which is the use of artificial
potential fields. Several authors in the past two decades have proposed various
modified versions of the artificial potential field algorithms. The variations
of the traditional APF approach have given an answer to prior shortcomings.
This gives potential rise to a strategic survey on the improved versions of
this algorithm. This study presents a review of motion planning for autonomous
ground vehicles using artificial potential fields. Each article is evaluated
based on criteria that involve the environment type, which may be either static
or dynamic, the evaluation scenario, which may be real-time or simulated, and
the method used for improving the search performance of the algorithm. All the
customized designs of planning models are analyzed and evaluated. At the end,
the results of the review are discussed, and future works are proposed
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