Yizhai Zhang

Rider Trunk and Bicycle Pose Estimation With Fusion of Force/Inertial Sensors

Estimation of human pose in physical human-machine interactions such as bicycling is challenging because of highly-dimensional human motion and lack of inexpensive, effective motion sensors. In this paper, we present a computational scheme to estimate both the rider trunk pose and the bicycle roll angle using only inertial and force sensors. The estimation scheme is built on a rider-bicycle dynamic model and the fusion of the wearable inertial sensors and the bicycle force sensors. We take advantages of the attractive properties of the robust force measurements and the motion-sensitive inertial measurements. The rider-bicycle dynamic model provides the underlying relationship between the force and the inertial measurements. The extended Kalman filter-based sensor fusion design fully incorporates the dynamic effects of the force measurements. The performance of the estimation scheme is demonstrated through extensive indoor and outdoor riding experiments.

Autonomous motorcycles for agile maneuvers, part I: Dynamic modeling

Single-track vehicles, such as motorcycles, provide an agile mobile platform. Modeling and control of motorcycles for agile maneuvers, such as those by professional racing riders, are challenging due to motorcycles unstable platform and complex tire/road interaction. As a first step attempting to understand how racing riders drive a motorcycle, this twopart paper presents a modeling and tracking control design of an autonomous motorcycle. In this first-part paper, we discuss a new dynamics model for the autonomous motorcycle. We consider the existence of lateral sliding velocity at each wheel contact point. Because of the importance of the tire/road interaction for vehicle stability and maneuverability, the dynamic modeling scheme also includes the motorcycle tire models. The new nonlinear dynamic models are used for control systems design in the companion paper.

A Hybrid Physical-Dynamic Tire/Road Friction Model

We present a hybrid physical-dynamic tire/road friction model for applications of vehicle motion simulation and control. We extend the LuGre dynamic friction model by considering the physical model-based adhesion/sliding partition of the tire/road contact patch. Comparison and model parameters relationship are presented between the physical and the LuGre dynamic friction models. We show that the LuGre dynamic friction model predicts the nonlinear and normal load-dependent rubber deformation and stress distributions on the contact patch. We also present the physical interpretation of the LuGre model parameters and their relationship with the physical model parameters. The analysis of the new hybrid models properties resolves unrealistic nonzero bristle deformation and stress at the trailing edge of the contact patch that is predicted by the existing LuGre tire/road friction models. We further demonstrate the use of the hybrid model to simulate and study an aggressive pendulum-turn vehicle maneuver. The CARSIM simulation results by using the new hybrid friction model show high agreements with experiments that are performed by a professional racing car driver.

Whole-Body Pose Estimation in Human Bicycle Riding Using a Small Set of Wearable Sensors

Tracking whole-body human pose in physical human-machine interactions is challenging because of highly dimensional human motions and lack of inexpensive, nonintrusive motion sensors in outdoor environment. In this paper, we present a computational scheme to estimate the human whole-body pose with application to bicycle riding using a small set of wearable sensors. The estimation scheme is built on the fusion of gyroscopes, accelerometers, force sensors, and physical rider-bicycle interaction constraints through an extended Kalman filter design. The use of physical rider-bicycle interaction constraints helps not only eliminate the integration drifts of inertial sensor measurements but also reduce the number of the needed wearable sensors for pose estimation. For each set of the upper and the lower limb, only one tri-axial gyroscope is needed to accurately obtain the 3-D pose information. The drift-free, reliable estimation performance is demonstrated through both indoor and outdoor riding experiments.

Balance control and analysis of stationary riderless motorcycles

We present balancing control analysis of a stationary riderless motorcycle. We first present the motorcycle dynamics with an accurate steering mechanism model with consideration of lateral movement of the tire/ground contact point. A nonlinear balance controller is then designed. We estimate the domain of attraction (DOA) of motorcycle dynamics under which the stationary motorcycle can be stabilized by steering. For a typical motorcycle/bicycle configuration, we find that the DOA is relatively small and thus balancing control by only steering at stationary is challenging. The balance control and DOA estimation schemes are validated by experiments conducted on the Rutgers autonomous motorcycle. The attitudes of the motorcycle platform are obtained by a novel estimation scheme that fuses measurements from global positioning systems (GPS) and inertial measurement units (IMU). We also present the experiments of the GPS/IMU-based attitude estimation scheme in the paper.

Embedded Flexible Force Sensor for In-Situ Tire–Road Interaction Measurements

In-situ sensing the tire–road interactions such as local contact friction force distributions provides crucial information for building accurate friction force models for vehicle safety control. In this paper, we report the development of an embedded, flexible local force sensor for measuring the tire local friction forces and their distributions. A new pressure-sensitive, electric conductive rubber (PSECR) sensor is used and embedded inside the tire rubber layer to extract the multi-dimensional local friction forces on the tire contact patch. The low-cost, flexible PSECR sensor is oriented in certain directions, and is sensitive to multiple compressive forces. To interpret the sensor measurements, we use a beam-spring model for the tire–road interactions to extract the local contact friction forces. The experimental results of stick–slip interaction testing on a motorcycle tire demonstrate the effective and good performance of the PSECR-based tire force sensor.

Dynamics Analysis and Controller Design for Maneuverable Tethered Space Net Robot

Space robots are considered as a promising solution to active space-debris capture and removal. In this paper, a brand new space robot system called the maneuverable tethered space net robot is proposed. In addition to the advantages inherited from the tethered space net, extra maneuverability in the tethered space net robot allows for wider possibilities for debris capture. The motion equations of the system are derived, and both symmetrical and asymmetrical configurations are analyzed. According to the specific vibration analysis, a modified adaptive supertwisting sliding-mode control scheme is proposed. The proposed adaption law is a function of the disturbance, which is considered as the sum of all the adjacent forces working on the controller plant: that is, the maneuverable unit. Both symmetrical and asymmetrical cases are simulated to verify that the tethered space net robot can fly toward active space debris steadily under the proposed control scheme.

Autonomous motorcycles for agile maneuvers, part II: Control systems design

In this paper, we present trajectory tracking and balancing of autonomous motorcycles for agile maneuvers. Based on the newly developed autonomous motorcycle dynamics in the companion paper, we present a nonlinear control design. The control systems design is based on the external/internal convertible (EIC) dynamical structure of the motorcycle dynamics. The control design of the EIC systems guarantees an exponential convergence of the motorcycle trajectory to a neighborhood of the desired profiles while the roll motion converges to a neighborhood of the desired equilibria that are estimated for a given desired trajectory. The effectiveness of the integrated control systems are demonstrated and validated by numerical examples based on a racing motorcycle prototype.

Stationary balance control of a bikebot

We present the development of the gyroscopic-balanced control of an autonomous bikebot. The bikebot is an actively controlled bicycle-based robotic platform with a gyro-balancer developed to study human dynamic postural balance motor skills through unstable physical human-robot interactions. We also present a dynamic model and analysis for stationary bikebot. A nonlinear balancing controller is designed to stabilize the underactuated stationary bikebot on an orbital trajectory around the unstable equilibrium point that is coupled with another orbit of the actuated gyro-balancer. We then demonstrate the analysis and control design with experimental validations. Finally, we present a set of human riding experiments to show how the bikebot can be used to perturb and excite human sensorimotor feedback loop for dynamic postural balance motor skills.

Rider/bicycle pose estimation with IMU/seat force measurements

Unstable bicycle provides an excellent platform to understand human sensorimotor mechanism for balancing control. We present an instrumented bicycle system to study the dynamic interactions between the human rider and the bicycle. A dynamics model is presented to capture the energetic interactions between the rider and the bicycle. We use and integrate the measurements from the seat force sensor and the rider body-mounted inertial measurement unit (IMU) to estimate both the riders and bicycles poses. Experimental results are presented to demonstrate the capability of the pose estimation development.