226 research outputs found
A Comparison of LPV Gain Scheduling and Control Contraction Metrics for Nonlinear Control
Gain-scheduled control based on linear parameter-varying (LPV) models derived
from local linearizations is a widespread nonlinear technique for tracking
time-varying setpoints. Recently, a nonlinear control scheme based on Control
Contraction Metrics (CCMs) has been developed to track arbitrary admissible
trajectories. This paper presents a comparison study of these two approaches.
We show that the CCM based approach is an extended gain-scheduled control
scheme which achieves global reference-independent stability and performance
through an exact control realization which integrates a series of local LPV
controllers on a particular path between the current and reference states.Comment: IFAC LPVS 201
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
To safely and efficiently navigate in complex urban traffic, autonomous
vehicles must make responsible predictions in relation to surrounding
traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and
critical task is to explore the movement patterns of different traffic-agents
and predict their future trajectories accurately to help the autonomous vehicle
make reasonable navigation decision. To solve this problem, we propose a long
short-term memory-based (LSTM-based) realtime traffic prediction algorithm,
TrafficPredict. Our approach uses an instance layer to learn instances'
movements and interactions and has a category layer to learn the similarities
of instances belonging to the same type to refine the prediction. In order to
evaluate its performance, we collected trajectory datasets in a large city
consisting of varying conditions and traffic densities. The dataset includes
many challenging scenarios where vehicles, bicycles, and pedestrians move among
one another. We evaluate the performance of TrafficPredict on our new dataset
and highlight its higher accuracy for trajectory prediction by comparing with
prior prediction methods.Comment: Accepted by AAAI(Oral) 201
A Generative Human-Robot Motion Retargeting Approach Using a Single RGBD Sensor
The goal of human-robot motion retargeting is to let a robot follow the movements performed by a human subject. Typically in previous approaches, the human poses are precomputed from a human pose tracking system, after which the explicit joint mapping strategies are specified to apply the estimated poses to a target robot. However, there is not any generic mapping strategy that we can use to map the human joint to robots with different kinds of configurations. In this paper, we present a novel motion retargeting approach that combines the human pose estimation and the motion retargeting procedure in a unified generative framework without relying on any explicit mapping. First, a 3D parametric human-robot (HUMROB) model is proposed which has the specific joint and stability configurations as the target robot while its shape conforms the source human subject. The robot configurations, including its skeleton proportions, joint limitations, and DoFs are enforced in the HUMROB model and get preserved during the tracking procedure. Using a single RGBD camera to monitor human pose, we use the raw RGB and depth sequence as input. The HUMROB model is deformed to fit the input point cloud, from which the joint angle of the model is calculated and applied to the target robots for retargeting. In this way, instead of fitted individually for each joint, we will get the joint angle of the robot fitted globally so that the surface of the deformed model is as consistent as possible to the input point cloud. In the end, no explicit or pre-defined joint mapping strategies are needed. To demonstrate its effectiveness for human-robot motion retargeting, the approach is tested under both simulations and on real robots which have a quite different skeleton configurations and joint degree of freedoms (DoFs) as compared with the source human subjects
Learning Stable and Robust Linear Parameter-Varying State-Space Models
This paper presents two direct parameterizations of stable and robust linear
parameter-varying state-space (LPV-SS) models. The model parametrizations
guarantee a priori that for all parameter values during training, the allowed
models are stable in the contraction sense or have their Lipschitz constant
bounded by a user-defined value . Furthermore, since the
parametrizations are direct, the models can be trained using unconstrained
optimization. The fact that the trained models are of the LPV-SS class makes
them useful for, e.g., further convex analysis or controller design. The
effectiveness of the approach is demonstrated on an LPV identification problem.Comment: Accepted for the 62nd IEEE Conference on Decision and Control
(CDC2023
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