Author name cluster

Ty Nguyen

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4 papers

2 author rows

IROS Conference 2020 Conference Paper

Robust, Perception Based Control with Quadrotors

Laura Jarin-Lipschitz
Rebecca Li
Ty Nguyen
Vijay Kumar 0001
Nikolai Matni

Traditionally, controllers and state estimators in robotic systems are designed independently. Controllers are often designed assuming perfect state estimation. However, state estimation methods such as Visual Inertial Odometry (VIO) drift over time and can cause the system to misbehave. While state estimation error can be corrected with the aid of GPS or motion capture, these complementary sensors are not always available or reliable. Recent work has shown that this issue can be dealt with by synthesizing robust controllers using a data-driven characterization of the perception error, and can bound the system's response to state estimation error using a robustness constraint. We investigate the application of this robust perception-based approach to a quadrotor model using VIO for state estimation and demonstrate the benefits and drawbacks of using this technique in simulation and hardware. Additionally, to make tuning easier, we introduce a new cost function to use in the control synthesis which allows one to take an existing controller and "robustify" it. To the best of our knowledge, this is the first robust perception-based controller implemented in real hardware, as well as one utilizing a data-driven perception model. We believe this as an important step towards safe, robust robots that explicitly account for the inherent dependence between perception and control.

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ICRA Conference 2020 Conference Paper

Vision-based Multi-MAV Localization with Anonymous Relative Measurements Using Coupled Probabilistic Data Association Filter

Ty Nguyen
Kartik Mohta
Camillo J. Taylor
Vijay Kumar 0001

We address the localization of robots in a multi-MAV system where external infrastructure like GPS or motion capture systems may not be available. Our approach lends itself to implementation on platforms with several constraints on size, weight, and power (SWaP). Particularly, our framework fuses the onboard VIO with the anonymous, visual-based robot-to-robot detection to estimate all robot poses in one common frame, addressing three main challenges: 1) the initial configuration of the robot team is unknown, 2) the data association between each vision-based detection and robot targets is unknown, and 3) the vision-based detection yields false negatives, false positives, inaccurate, and provides noisy bearing, distance measurements of other robots. Our approach extends the Coupled Probabilistic Data Association Filter [1] to cope with nonlinear measurements. We demonstrate the superior performance of our approach over a simple VIO-based method in a simulation with the measurement models statistically modeled using the real experimental data. We also show how onboard sensing, estimation, and control can be used for formation flight.

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AAMAS Conference 2017 Conference Paper

Extending the Range of Delivery Drones by Exploratory Learning of Energy Models

Ty Nguyen
Tsz-Chiu Au

Delivery drones have a fairly short range due to their limited battery life. We propose new exploration strategies to generate paths for a drone to reach its destination while learning about the energy consumption on each edge on its path so as to optimize its range in future missions. As the energy consumption mostly depends on the payload, the wind direction, and the wind speed, we developed an energy model to estimate the energy consumption based on these factors. We evaluated our exploration strategies for learning the energy model in order to identify the set of all reachable destinations. We found that adding a small amount of perturbation to encourage exploration can increase the learning rate.

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IROS Conference 2017 Conference Paper

Learning of vehicular performance models for longitudinal motion planning to satisfy arrival requirements

Ty Nguyen
Dung Nguyen
Tsz-Chiu Au

Motion planning with predictable timing and velocity will enable a number of interesting applications such as autonomous intersection management (AIM). These planning algorithms depend on an accurate model of the performance of the vehicular controllers, which can be highly non-linear. Au et al. proposed a motion planning algorithm to satisfy the arrival requirements in AIM. However, they assumed that the performance models are given for every road and did not discuss how to learn these models. In this paper, we propose an instance-based learning approach to learn the performance models automatically, and argue that instance-based learning is suitable for this learning task because performance models for different roads can have a high correlation with each other. Moreover, an exploration strategy based on the principle of least effort is given to speed up the learning process. Our experiments showed that the instance-based learning method with distance-based exploration strategy offers a faster learning rate than the artificial neural network methods.

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