13,213 research outputs found
Model Predictive Control Based Trajectory Generation for Autonomous Vehicles - An Architectural Approach
Research in the field of automated driving has created promising results in
the last years. Some research groups have shown perception systems which are
able to capture even complicated urban scenarios in great detail. Yet, what is
often missing are general-purpose path- or trajectory planners which are not
designed for a specific purpose. In this paper we look at path- and trajectory
planning from an architectural point of view and show how model predictive
frameworks can contribute to generalized path- and trajectory generation
approaches for generating safe trajectories even in cases of system failures.Comment: Presented at IEEE Intelligent Vehicles Symposium 2017, Los Angeles,
CA, US
Real-time on-board obstacle avoidance for UAVs based on embedded stereo vision
In order to improve usability and safety, modern unmanned aerial vehicles
(UAVs) are equipped with sensors to monitor the environment, such as
laser-scanners and cameras. One important aspect in this monitoring process is
to detect obstacles in the flight path in order to avoid collisions. Since a
large number of consumer UAVs suffer from tight weight and power constraints,
our work focuses on obstacle avoidance based on a lightweight stereo camera
setup. We use disparity maps, which are computed from the camera images, to
locate obstacles and to automatically steer the UAV around them. For disparity
map computation we optimize the well-known semi-global matching (SGM) approach
for the deployment on an embedded FPGA. The disparity maps are then converted
into simpler representations, the so called U-/V-Maps, which are used for
obstacle detection. Obstacle avoidance is based on a reactive approach which
finds the shortest path around the obstacles as soon as they have a critical
distance to the UAV. One of the fundamental goals of our work was the reduction
of development costs by closing the gap between application development and
hardware optimization. Hence, we aimed at using high-level synthesis (HLS) for
porting our algorithms, which are written in C/C++, to the embedded FPGA. We
evaluated our implementation of the disparity estimation on the KITTI Stereo
2015 benchmark. The integrity of the overall realtime reactive obstacle
avoidance algorithm has been evaluated by using Hardware-in-the-Loop testing in
conjunction with two flight simulators.Comment: Accepted in the International Archives of the Photogrammetry, Remote
Sensing and Spatial Information Scienc
Modification of Hilbert's Space-Filling Curve to Avoid Obstacles: A Robotic Path-Planning Strategy
This paper addresses the problem of exploring a region using the Hilbert's
space-filling curve in the presence of obstacles. No prior knowledge of the
region being explored is assumed. An online algorithm is proposed which can
implement evasive strategies to avoid obstacles comprising a single or two
blocked unit squares placed side by side and successfully explore the entire
region. The strategies are specified by the change in the waypoint array which
robot going to follow. The fractal nature of the Hilbert's space-filling curve
has been exploited in proving the validity of the solution
Multi-Agent Pursuit-Evasion Game Based on Organizational Architecture
Multi-agent coordination mechanisms are frequently used in pursuit-evasion games with the aim of enabling the coalitions of the pursuers and unifying their individual skills to deal with the complex tasks encountered. In this paper, we propose a coalition formation algorithm based on organizational principles and applied to the pursuit-evasion problem. In order to allow the alliances of the pursuers in different pursuit groups, we have used the concepts forming an organizational modeling framework known as YAMAM (Yet Another Multi Agent Model). Specifically, we have used the concepts Agent, Role, Task, and Skill, proposed in this model to develop a coalition formation algorithm to allow the optimal task sharing. To control the pursuers\u27 path planning in the environment as well as their internal development during the pursuit, we have used a Reinforcement learning method (Q-learning). Computer simulations reflect the impact of the proposed techniques
Search and Pursuit-Evasion in Mobile Robotics, A survey
This paper surveys recent results in pursuitevasion
and autonomous search relevant to applications
in mobile robotics. We provide a taxonomy of search
problems that highlights the differences resulting from
varying assumptions on the searchers, targets, and the
environment. We then list a number of fundamental
results in the areas of pursuit-evasion and probabilistic
search, and we discuss field implementations on mobile
robotic systems. In addition, we highlight current open
problems in the area and explore avenues for future
work
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