A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuratio...CSCJournals
This paper presents the implementation of a novel technique for sensor based path planning of autonomous mobile robots. The proposed method is based on finding free-configuration eigen spaces (FCE) in the robot actuation area. Using the FCE technique to find optimal paths for autonomous mobile robots, the underlying hypothesis is that in the low-dimensional manifolds of laser scanning data, there lies an eigenvector which corresponds to the free-configuration space of the higher order geometric representation of the environment. The vectorial combination of all these eigenvectors at discrete time scan frames manifests a trajectory, whose sum can be treated as a robot path or trajectory. The proposed algorithm was tested on two different test bed data, real data obtained from Navlab SLAMMOT and data obtained from the real-time robotics simulation program Player/Stage. Performance analysis of FCE technique was done with existing four path planning algorithms under certain working parameters, namely computation time needed to find a solution, the distance travelled and the amount of turning required by the autonomous mobile robot. This study will enable readers to identify the suitability of path planning algorithm under the working parameters, which needed to be optimized. All the techniques were tested in the real-time robotic software Player/Stage. Further analysis was done using MATLAB mathematical computation software.
Path Planning for Mobile Robot Navigation Using Voronoi Diagram and Fast Marc...Waqas Tariq
For navigation in complex environments, a robot needs to reach a compromise between the need for having efficient and optimized trajectories and the need for reacting to unexpected events. This paper presents a new sensor-based Path Planner which results in a fast local or global motion planning able to incorporate the new obstacle information. In the first step the safest areas in the environment are extracted by means of a Voronoi Diagram. In the second step the Fast Marching Method is applied to the Voronoi extracted areas in order to obtain the path. The method combines map-based and sensor-based planning operations to provide a reliable motion plan, while it operates at the sensor frequency. The main characteristics are speed and reliability, since the map dimensions are reduced to an almost unidimensional map and this map represents the safest areas in the environment for moving the robot. In addition, the Voronoi Diagram can be calculated in open areas, and with all kind of shaped obstacles, which allows to apply the proposed planning method in complex environments where other methods of planning based on Voronoi do not work.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Path Planning Technique For Autonomous Mobile Robot Using Free-Configuratio...CSCJournals
This paper presents the implementation of a novel technique for sensor based path planning of autonomous mobile robots. The proposed method is based on finding free-configuration eigen spaces (FCE) in the robot actuation area. Using the FCE technique to find optimal paths for autonomous mobile robots, the underlying hypothesis is that in the low-dimensional manifolds of laser scanning data, there lies an eigenvector which corresponds to the free-configuration space of the higher order geometric representation of the environment. The vectorial combination of all these eigenvectors at discrete time scan frames manifests a trajectory, whose sum can be treated as a robot path or trajectory. The proposed algorithm was tested on two different test bed data, real data obtained from Navlab SLAMMOT and data obtained from the real-time robotics simulation program Player/Stage. Performance analysis of FCE technique was done with existing four path planning algorithms under certain working parameters, namely computation time needed to find a solution, the distance travelled and the amount of turning required by the autonomous mobile robot. This study will enable readers to identify the suitability of path planning algorithm under the working parameters, which needed to be optimized. All the techniques were tested in the real-time robotic software Player/Stage. Further analysis was done using MATLAB mathematical computation software.
Path Planning for Mobile Robot Navigation Using Voronoi Diagram and Fast Marc...Waqas Tariq
For navigation in complex environments, a robot needs to reach a compromise between the need for having efficient and optimized trajectories and the need for reacting to unexpected events. This paper presents a new sensor-based Path Planner which results in a fast local or global motion planning able to incorporate the new obstacle information. In the first step the safest areas in the environment are extracted by means of a Voronoi Diagram. In the second step the Fast Marching Method is applied to the Voronoi extracted areas in order to obtain the path. The method combines map-based and sensor-based planning operations to provide a reliable motion plan, while it operates at the sensor frequency. The main characteristics are speed and reliability, since the map dimensions are reduced to an almost unidimensional map and this map represents the safest areas in the environment for moving the robot. In addition, the Voronoi Diagram can be calculated in open areas, and with all kind of shaped obstacles, which allows to apply the proposed planning method in complex environments where other methods of planning based on Voronoi do not work.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Two guest lectures about motion planning in the course S2016 ECE 486: Robot Dynamics and Control, Spring 2016, Electrical and Computer Engineering Department, University of Waterloo. Useful Resources: - Open source libraries: http://ompl.kavrakilab.org/ http://wiki.ros.org/motion_planners http://moveit.ros.org/ - Book: Steven M. LaValle, Planning Algorithm. Available at: http://planning.cs.uiuc.edu/, last accessed, July 12, 2016
Camera-Based Road Lane Detection by Deep Learning IIYu Huang
lane detection, deep learning, autonomous driving, CNN, RNN, LSTM, GRU, lane localization, lane fitting, ego lane, end-to-end, vanishing point, segmentation, FCN, regression, classification
Exact Cell Decomposition of Arrangements used for Path Planning in RoboticsUmair Amjad
This is short overview of research paper.
We present a practical algorithm for the automatic generation of a map that describes the operation environment of an indoor mobile service robot. The input is a CAD description of a building consisting of line segments that represent the walls. The algorithm is based on the exact cell decomposition obtained when these segments are extended to infinite lines, resulting in a line arrangement. The cells are represented by nodes in a connectivity graph. The map consists of the connectivity graph and additional environmental information that is calculated for each cell. The method takes into account both the path planning and position verification requirements of the robot and has been implemented.
Scenario-Based Development & Testing for Autonomous DrivingYu Huang
Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World, 2020
A Scenario-Based Development Framework for Autonomous Driving, 2020
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving, 2020
Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction, 2021
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles, 2021
Systems Approach to Creating Test Scenarios for Automated Driving Systems, Reliability Engineering and System Safety (215), 2021
[Paper research] GOSELO: for Robot navigation using Reactive neural networksJehong Lee
GOSELO: Goal-Direction Obstacle and Self-Location Map for Robot Navigation Using Reactive Neural Networks 라는 논문을 중심으로, mobile platform의 Path planning을 CNN으로 End-to-End 방식으로 수행하는 방법에 관하여 소개합니다.
광주과학기술원 인공지능 스터디 A-GIST 모임에서 발표했습니다.
발표영상(유튜브, 한국어): https://youtu.be/l-gKjzWKuHA
Simulation for autonomous driving at uber atgYu Huang
Testing Safety of SDVs by Simulating Perception and Prediction
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
Recovering and Simulating Pedestrians in the Wild
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
SceneGen: Learning to Generate Realistic Traffic Scenes
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Appendix: (Waymo)
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
Two guest lectures about motion planning in the course S2016 ECE 486: Robot Dynamics and Control, Spring 2016, Electrical and Computer Engineering Department, University of Waterloo. Useful Resources: - Open source libraries: http://ompl.kavrakilab.org/ http://wiki.ros.org/motion_planners http://moveit.ros.org/ - Book: Steven M. LaValle, Planning Algorithm. Available at: http://planning.cs.uiuc.edu/, last accessed, July 12, 2016
Camera-Based Road Lane Detection by Deep Learning IIYu Huang
lane detection, deep learning, autonomous driving, CNN, RNN, LSTM, GRU, lane localization, lane fitting, ego lane, end-to-end, vanishing point, segmentation, FCN, regression, classification
Exact Cell Decomposition of Arrangements used for Path Planning in RoboticsUmair Amjad
This is short overview of research paper.
We present a practical algorithm for the automatic generation of a map that describes the operation environment of an indoor mobile service robot. The input is a CAD description of a building consisting of line segments that represent the walls. The algorithm is based on the exact cell decomposition obtained when these segments are extended to infinite lines, resulting in a line arrangement. The cells are represented by nodes in a connectivity graph. The map consists of the connectivity graph and additional environmental information that is calculated for each cell. The method takes into account both the path planning and position verification requirements of the robot and has been implemented.
Scenario-Based Development & Testing for Autonomous DrivingYu Huang
Formal Scenario-Based Testing of Autonomous Vehicles: From Simulation to the Real World, 2020
A Scenario-Based Development Framework for Autonomous Driving, 2020
A Customizable Dynamic Scenario Modeling and Data Generation Platform for Autonomous Driving, 2020
Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction, 2021
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles, 2021
Systems Approach to Creating Test Scenarios for Automated Driving Systems, Reliability Engineering and System Safety (215), 2021
[Paper research] GOSELO: for Robot navigation using Reactive neural networksJehong Lee
GOSELO: Goal-Direction Obstacle and Self-Location Map for Robot Navigation Using Reactive Neural Networks 라는 논문을 중심으로, mobile platform의 Path planning을 CNN으로 End-to-End 방식으로 수행하는 방법에 관하여 소개합니다.
광주과학기술원 인공지능 스터디 A-GIST 모임에서 발표했습니다.
발표영상(유튜브, 한국어): https://youtu.be/l-gKjzWKuHA
Simulation for autonomous driving at uber atgYu Huang
Testing Safety of SDVs by Simulating Perception and Prediction
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
Recovering and Simulating Pedestrians in the Wild
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
SceneGen: Learning to Generate Realistic Traffic Scenes
TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Appendix: (Waymo)
SurfelGAN: Synthesizing Realistic Sensor Data for Autonomous Driving
2. Path Planning
• Compute motion strategies, e.g.,
– Geometric paths
– Time-parameterized trajectories
• Achieve high-level goals, e.g.,
– To build a collision free path from start point
to the desired destination
– Assemble/disassemble the engine
– Map the environment
3.
4. Objective
• To Compute a collision-free path for a mobile robot among static
obstacles.
• Inputs required
– Geometry of the robot and of obstacles
– Kinematics of the robot (d.o.f)
– Initial and goal robot configurations (positions & orientations)
• Expected Result
Continuous sequence of collision-free robot configurations
connecting the initial and goal configurations
5. Some of the existing Methods
• Visibility Graphs
• Roadmap
• Cell Decomposition
• Potential Field
6. Visibility Graph Method
• If there is a collision-free path between
two points, then there is a polygonal
path that bends only at the obstacles
vertices.
• A polygonal path is a piecewise linear
curve.
7.
8. Visibility Graph
• A visibility graph is a graph such that
–Nodes: qinit, qgoal, or an obstacle
vertex.
–Edges: An edge exists between
nodes u and v if the line segment
between u and v is an obstacle edge
or it does not intersect the obstacles.
20. Road mapping Technique
• Visibility graph
• Voronoi Diagram
Introduced by computational
geometry researchers.
Generate paths that maximizes
clearance
Applicable mostly to 2-D
configuration spaces
21. Slide 21
Cell-decomposition Methods
• Exact cell decomposition
• Approximate cell decomposition
– F is represented by a collection of non-
overlapping cells whose union is contained
in F.
– Cells usually have simple, regular shapes,
e.g., rectangles, squares.
– Facilitate hierarchical space
decomposition
25. Slide 25
Potential Fields
• Initially proposed for real-time collision avoidance
[Khatib 1986].
• A potential field is a scalar function over the free
space.
• To navigate, the robot applies a force proportional
to the negated gradient of the potential field.
• A navigation function is an ideal potential field that
– has global minimum at the goal
– has no local minima
– grows to infinity near obstacles
– is smooth
28. Slide 28
Algorithm Outline
• Place a regular grid G over the configuration space
• Compute the potential field over G
• Search G using a best-first algorithm with potential
field as the heuristic function
Note: A heuristic function or simply a heuristic is
a function that ranks alternatives in various search
algorithms at each branching step basing on an
available information in order to make a decision
which branch is to be followed during a search.
29. Slide 29
Use the Local Minima Information
– Identify the local minima
– Build an ideal potential field – navigation
function – that does not have local minima
– Using the calculations done so far, we can create
a PATH PLANNER which would give us the
optimum path for a set of inputs.
30. Active Sensing
The main question to answer:”Where to move next?”
Given a current knowledge about the robot state and the
environment, how to select the next sensing action or sequence
of actions. A vehicle is moving autonomously through an
environment gathering information from sensors. The sensor
data are used. to generate the robot actions
Beginning from a starting configuration (xs,ys,s) to a goal
configuration (xg,yg,g) in the presence of a reference
trajectory and without it; With and without obstacles; Taking into
account the constraints on the velocity, steering angle, the
obstacles, and other constraints …
31. Active Sensing of a WMR
k
k
y
k
x
k
k
k
k
k
k
k
k
k
k
k
k
k
k
L
T
v
T
v
y
T
v
x
y
x
,
,
,
1
1
1
sin
)
sin(
)
cos(
Robot model
y
y
x
k
k
k
y
k
x
L
Beacon
B
x
B
y
32. Trajectory optimization
• Between two points there are an infinite number of possible
trajectories. But not each trajectory from the configuration space
represents a feasible trajectory for the robot.
• How to move in the best way according to a criterion from the
starting to a goal configuration?
• The key idea is to use some parameterized family of possible
trajectories and thus to reduce the infinite-dimensional problem to a
finitely parametrized optimization problem. To characterize the robot
motion and to process the sensor information in efficient way, an
appropriate criterion is need. So, active sensing is a decision
making, global optimization problem subject to constraints.
33. Trajectory Optimization
• Let Q is a class of smooth functions. The problem of determining
the ‘best’ trajectory q with respect to a criterion J can be then
formulated as
q = argmin(J)
where the optimization criterion is chosen of the form
information part losses (time, traveled distance)
subject to constraints
l: lateral deviation, v: WMR velocity; : steering angle; d : distance
to obstacle
C
c
c
J
k
i
A
2
1
,
min
I
,
,
,
, min
,
,
max
max
max
,
, o
k
o
k
k
y
k
y d
d
v
v
l
l
34. Trajectory Optimization
The class Q of harmonic functions is chosen,
Q = Q(p),
p: vector of parameters obeying to preset constraints;
Given N number of harmonic functions, the new modified robot
trajectory is generated on the basis of the reference trajectory by a
lateral deviation as a linear superposition
35. Why harmonic functions?
• They are smooth periodic functions;
• Gives the possibility to move easily the robot to the desired final
point;
• Easy to implement;
• Multisinusoidal signals are reach excitation signals and often used in
the experimental identification. They have proved advantages for
control generation of nonholonomic WMR (assure smooth
stabilization). For canonical chained systems Brockett (1981)
showed that optimal inputs are sinusoids at integrally related
frequencies, namely 2, 2. 2, …, m/2. 2.
36. Optimality Criterion
I = trace(WP),
I is computed at the goal configuration or on the the whole
trajectory (part of it, e.g. in an interval )
where W = MN;
M: scaling matrix; N: normalizing matrix
P: estimation error covariance matrix (information matrix
or entropy) from a filter (EKF);
]
,
[ b
a k
k
C
c
c
J
k
i
A
2
1
,
min
I
39. Lyudmila Mihaylova, Katholieke
Universiteit Leuven, Division PMA
Implementation
• Using Optimization Toolbox of MATLAB,
• fmincon finds the constrained minimum of a function
of several variables
• With small number of sinusoids (N<5) the
computational complexity is such that it is easily
implemented on-line. With more sinusoidal terms
(N>10), the complexity (time, number of
computations) is growing up and a powerful computer
is required or off-line computation. All the performed
experiments prove that the trajectories generated
even with N=3 sinusoidal terms respond to the
imposed requirements.
40. Conclusions
An effective approach for trajectories optimization
has been considered :
• Appropriate optimality criteria are defined. The
influence of the different factors is decoupled;
• The approach is applicable in the presence of
and without obstacles.
41. Few Topics with related videos of work done by people
around the world in the field Robot navigation
• 3-D path planning and target trajectory prediction :
http://www.youtube.com/watch?v=pr1Y21mexzs&feature=related
• Path Planning : http://www.youtube.com/watch?v=d0PluQz5IuQ
• Potential Function Method – By Leng Feng Lee :
http://www.youtube.com/watch?v=Lf7_ve83UhE
• Real-Time Scalable Motion Planning for Crowds -
http://www.youtube.com/watch?v=ifimWFs5-hc&NR=1
• Robot Potential with local minimum avoidance -
http://www.youtube.com/watch?v=Cr7PSr6SHTI&feature=related
42. References
• Tracking, Motion Generation and Active Sensing of
Nonholonomic Wheeled Mobile Robots -Lyudmila
Mihaylova & Katholieke Universiteit Leuven
• Robot Path Planning - By William Regli
Department of Computer Science
(and Departments of ECE and MEM)
Drexel University
• Part II-Motion Planning- by Steven M. LaValle
(University of Illinois)
• Wikipedia
• www.mathworks.com