3 research outputs found
Homography-Based State Estimation for Autonomous Exploration in Unknown Environments
This thesis presents the development of vision-based state estimation algorithms to enable a quadcopter UAV to navigate and explore a previously unknown GPS denied environment. These state estimation algorithms are based on tracked Speeded-Up Robust Features (SURF) points and the homography relationship that relates the camera motion to the locations of tracked planar feature points in the image plane. An extended Kalman filter implementation is developed to perform sensor fusion using measurements from an onboard inertial measurement unit (accelerometers and rate gyros) with vision-based measurements derived from the homography relationship. Therefore, the measurement update in the filter requires the processing of images from a monocular camera to detect and track planar feature points followed by the computation of homography parameters. The state estimation algorithms are designed to be independent of GPS since GPS can be unreliable or unavailable in many operational environments of interest such as urban environments. The state estimation algorithms are implemented using simulated data from a quadcopter UAV and then tested using post processed video and IMU data from flights of an autonomous quadcopter. The homography-based state estimation algorithm was effective, but accumulates drift errors over time due to the relativistic homography measurement of position
Maximizing Safety and Efficiency for Cooperative Lane-Changing: A Minimally Disruptive Approach
This paper addresses cooperative lane-changing maneuvers in mixed traffic,
aiming to minimize traffic flow disruptions while accounting for uncooperative
vehicles. The proposed approach adopts controllers combining Optimal control
with Control Barrier Functions (OCBF controllers) which guarantee
spatio-temporal constraints through the use of fixed-time convergence.
Additionally, we introduce robustness to disturbances by deriving a method for
handling worst-case disturbances using the dual of a linear programming
problem. We present a near-optimal solution that ensures safety, optimality,
and robustness to changing behavior of uncooperative vehicles. Simulations
demonstrate the effectiveness of the proposed approach in enhancing efficiency
and safety
Minimally Disruptive Cooperative Lane-change Maneuvers
A lane-change maneuver on a congested highway could be severely disruptive or
even infeasible without the cooperation of neighboring cars. However,
cooperation with other vehicles does not guarantee that the performed maneuver
will not have a negative impact on traffic flow unless it is explicitly
considered in the cooperative controller design. In this letter, we present a
socially compliant framework for cooperative lane-change maneuvers for an
arbitrary number of CAVs on highways that aims to interrupt traffic flow as
minimally as possible. Moreover, we explicitly impose feasibility constraints
in the optimization formulation by using reachability set theory, leading to a
unified design that removes the need for an iterative procedure used in prior
work. We quantitatively evaluate the effectiveness of our framework and compare
it against previously offered approaches in terms of maneuver time and incurred
throughput disruption.Comment: 6 pages, 2 figure