3 research outputs found
Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping
This paper focuses on real-time occupancy mapping and collision checking
onboard an autonomous robot navigating in an unknown environment. We propose a
new map representation, in which occupied and free space are separated by the
decision boundary of a kernel perceptron classifier. We develop an online
training algorithm that maintains a very sparse set of support vectors to
represent obstacle boundaries in configuration space. We also derive conditions
that allow complete (without sampling) collision-checking for piecewise-linear
and piecewise-polynomial robot trajectories. We demonstrate the effectiveness
of our mapping and collision checking algorithms for autonomous navigation of
an Ackermann-drive robot in unknown environments.Comment: Accepted to ICRA 202
Synthesis of Control Barrier Functions Using a Supervised Machine Learning Approach
Control barrier functions are mathematical constructs used to guarantee
safety for robotic systems. When integrated as constraints in a quadratic
programming optimization problem, instantaneous control synthesis with
real-time performance demands can be achieved for robotics applications.
Prevailing use has assumed full knowledge of the safety barrier functions,
however there are cases where the safe regions must be estimated online from
sensor measurements. In these cases, the corresponding barrier function must be
synthesized online. This paper describes a learning framework for estimating
control barrier functions from sensor data. Doing so affords system operation
in unknown state space regions without compromising safety. Here, a support
vector machine classifier provides the barrier function specification as
determined by sets of safe and unsafe states obtained from sensor measurements.
Theoretical safety guarantees are provided. Experimental ROS-based simulation
results for an omnidirectional robot equipped with LiDAR demonstrate safe
operation.Comment: Submitted to the 2020 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS
Autonomous Navigation in Unknown Environments with Sparse Bayesian Kernel-based Occupancy Mapping
This paper focuses on online occupancy mapping and real-time collision
checking onboard an autonomous robot navigating in a large unknown environment.
Commonly used voxel and octree map representations can be easily maintained in
a small environment but have increasing memory requirements as the environment
grows. We propose a fundamentally different approach for occupancy mapping, in
which the boundary between occupied and free space is viewed as the decision
boundary of a machine learning classifier. This work generalizes a kernel
perceptron model which maintains a very sparse set of support vectors to
represent the environment boundaries efficiently. We develop a probabilistic
formulation based on Relevance Vector Machines, allowing robustness to
measurement noise and probabilistic occupancy classification, supporting
autonomous navigation. We provide an online training algorithm, updating the
sparse Bayesian map incrementally from streaming range data, and an efficient
collision-checking method for general curves, representing potential robot
trajectories. The effectiveness of our mapping and collision checking
algorithms is evaluated in tasks requiring autonomous robot navigation and
active mapping in unknown environments.Comment: Submitted to TR