19,574 research outputs found
RELAX: Reinforcement Learning Enabled 2D-LiDAR Autonomous System for Parsimonious UAVs
Unmanned Aerial Vehicles (UAVs) have become increasingly prominence in recent
years, finding applications in surveillance, package delivery, among many
others. Despite considerable efforts in developing algorithms that enable UAVs
to navigate through complex unknown environments autonomously, they often
require expensive hardware and sensors, such as RGB-D cameras and 3D-LiDAR,
leading to a persistent trade-off between performance and cost. To this end, we
propose RELAX, a novel end-to-end autonomous framework that is exceptionally
cost-efficient, requiring only a single 2D-LiDAR to enable UAVs operating in
unknown environments. Specifically, RELAX comprises three components: a
pre-processing map constructor; an offline mission planner; and a reinforcement
learning (RL)-based online re-planner. Experiments demonstrate that RELAX
offers more robust dynamic navigation compared to existing algorithms, while
only costing a fraction of the others. The code will be made public upon
acceptance
Towards Flight Trials for an Autonomous UAV Emergency Landing using Machine Vision
This paper presents the evolution and status of a number of research programs focussed on developing an automated fixed wing UAV landing system. Results obtained in each of the three main areas of research as vision-based site identification, path and trajectory planning and multi-criteria decision making are presented. The results obtained provide a baseline for further refinements and constitute the starting point for the implementation of a prototype system ready for flight testing
Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions
To plan safe trajectories in urban environments, autonomous vehicles must be
able to quickly assess the future intentions of dynamic agents. Pedestrians are
particularly challenging to model, as their motion patterns are often uncertain
and/or unknown a priori. This paper presents a novel changepoint detection and
clustering algorithm that, when coupled with offline unsupervised learning of a
Gaussian process mixture model (DPGP), enables quick detection of changes in
intent and online learning of motion patterns not seen in prior training data.
The resulting long-term movement predictions demonstrate improved accuracy
relative to offline learning alone, in terms of both intent and trajectory
prediction. By embedding these predictions within a chance-constrained motion
planner, trajectories which are probabilistically safe to pedestrian motions
can be identified in real-time. Hardware experiments demonstrate that this
approach can accurately predict pedestrian motion patterns from onboard
sensor/perception data and facilitate robust navigation within a dynamic
environment.Comment: Submitted to 2014 International Workshop on the Algorithmic
Foundations of Robotic
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