3,154 research outputs found
Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications
Developing an intelligent vehicle which can perform human-like actions
requires the ability to learn basic driving skills from a large amount of
naturalistic driving data. The algorithms will become efficient if we could
decompose the complex driving tasks into motion primitives which represent the
elementary compositions of driving skills. Therefore, the purpose of this paper
is to segment unlabeled trajectory data into a library of motion primitives. By
applying a probabilistic inference based on an iterative
Expectation-Maximization algorithm, our method segments the collected
trajectories while learning a set of motion primitives represented by the
dynamic movement primitives. The proposed method utilizes the mutual
dependencies between the segmentation and representation of motion primitives
and the driving-specific based initial segmentation. By utilizing this mutual
dependency and the initial condition, this paper presents how we can enhance
the performance of both the segmentation and the motion primitive library
establishment. We also evaluate the applicability of the primitive
representation method to imitation learning and motion planning algorithms. The
model is trained and validated by using the driving data collected from the
Beijing Institute of Technology intelligent vehicle platform. The results show
that the proposed approach can find the proper segmentation and establish the
motion primitive library simultaneously
Search-Based Task and Motion Planning for Hybrid Systems: Agile Autonomous Vehicles
To achieve optimal robot behavior in dynamic scenarios we need to consider
complex dynamics in a predictive manner. In the vehicle dynamics community, it
is well know that to achieve time-optimal driving on low surface, the vehicle
should utilize drifting. Hence many authors have devised rules to split
circuits and employ drifting on some segments. These rules are suboptimal and
do not generalize to arbitrary circuit shapes (e.g., S-like curves). So, the
question "When to go into which mode and how to drive in it?" remains
unanswered. To choose the suitable mode (discrete decision), the algorithm
needs information about the feasibility of the continuous motion in that mode.
This makes it a class of Task and Motion Planning (TAMP) problems, which are
known to be hard to solve optimally in real-time. In the AI planning community,
search methods are commonly used. However, they cannot be directly applied to
TAMP problems due to the continuous component. Here, we present a search-based
method that effectively solves this problem and efficiently searches in a
highly dimensional state space with nonlinear and unstable dynamics. The space
of the possible trajectories is explored by sampling different combinations of
motion primitives guided by the search. Our approach allows to use multiple
locally approximated models to generate motion primitives (e.g., learned models
of drifting) and effectively simplify the problem without losing accuracy. The
algorithm performance is evaluated in simulated driving on a mixed-track with
segments of different curvatures (right and left). Our code is available at
https://git.io/JenvBComment: Accepted to the journal Engineering Applications of Artificial
Intelligence; 19 pages, 18 figures, code: https://git.io/JenvB. arXiv admin
note: text overlap with arXiv:1907.0782
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
A path planning and path-following control framework for a general 2-trailer with a car-like tractor
Maneuvering a general 2-trailer with a car-like tractor in backward motion is
a task that requires significant skill to master and is unarguably one of the
most complicated tasks a truck driver has to perform. This paper presents a
path planning and path-following control solution that can be used to
automatically plan and execute difficult parking and obstacle avoidance
maneuvers by combining backward and forward motion. A lattice-based path
planning framework is developed in order to generate kinematically feasible and
collision-free paths and a path-following controller is designed to stabilize
the lateral and angular path-following error states during path execution. To
estimate the vehicle state needed for control, a nonlinear observer is
developed which only utilizes information from sensors that are mounted on the
car-like tractor, making the system independent of additional trailer sensors.
The proposed path planning and path-following control framework is implemented
on a full-scale test vehicle and results from simulations and real-world
experiments are presented.Comment: Preprin
Search-Based Motion Planning for Performance Autonomous Driving
Driving on the limits of vehicle dynamics requires predictive planning of
future vehicle states. In this work, a search-based motion planning is used to
generate suitable reference trajectories of dynamic vehicle states with the
goal to achieve the minimum lap time on slippery roads. The search-based
approach enables to explicitly consider a nonlinear vehicle dynamics model as
well as constraints on states and inputs so that even challenging scenarios can
be achieved in a safe and optimal way. The algorithm performance is evaluated
in simulated driving on a track with segments of different curvatures.Comment: Accepted to IAVSD 201
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