Lateral stability analysis of the tractor/full trailer combination vehicle
This paper presents a linear dynamics model of tractor/full trailer with six degrees of freedom, and a series of simulations are performed on obstacle avoidance manoeuvers in which the combination vehicle moves at a constant forward speed. It is found that the ratio of the peak lateral acceleration of the full trailer's center of gravity (CG) to that of the tractor is 2.3, meanwhile, the articulated angle of the trailer is larger than that of the dolly. This phenomena, defined as the ‘rearward amplification ratio’(RWA), leads vehicle to rollover easily. A larger wheelbase of trailer would be useful to improve the lateral stability of the combination vehicle but it is limited by width of the road. Acting a resistive torque on the turntable can be absolutely effective in improving the dynamics stability of the tractor/full trailer combination vehicle but if acted at hitch point it has little improvement.
CLOSED-LOOP DIRECTIONAL STABILITY OF CAR-TRAILER COMBINATIONS IN STRAIGHT-LINE MOTION
SUMMARY This work focuses on the interaction between a driver and a car-trailer combination. A model characterizing human operator behavior in regulation task is employed to study directional stability of the overall system. The vehicle-trailer model retains nonlinear cornering force and other kinematic nonlinearities. Linear stability of the straight line motion is analyzed by the application of Routh-Hurwitz criteria and stability boundaries in parameter space are constructed by setting appropriate Hurwitz determinant to zero. It is shown that two types of transition in stability are possible in the driver/car-trailer system. They correspond to one pair or two pairs of complex conjugate eigenvalues crossing the imaginary axis simultaneously. The implications in terms of resulting motions for the nonlinear system are also discussed. It is shown that stabilization of the combination can be achieved by adding a passive controller at the articulation point. Articulation damper turns out to be a more useful ...
Towards infield, live plant phenotyping using a reduced-parameter CNN
There is an increase in consumption of agricultural produce as a result of the rapidly growing human population, particularly in developing nations. This has triggered high-quality plant phenotyping research to help with the breeding of high-yielding plants that can adapt to our continuously changing climate. Novel, low-cost, fully automated plant phenotyping systems, capable of infield deployment, are required to help identify quantitative plant phenotypes. The identification of quantitative plant phenotypes is a key challenge which relies heavily on the precise segmentation of plant images. Recently, the plant phenotyping community has started to use very deep convolutional neural networks (CNNs) to help tackle this fundamental problem. However, these very deep CNNs rely on some millions of model parameters and generate very large weight matrices, thus making them difficult to deploy infield on low-cost, resource-limited devices. We explore how to compress existing very deep CNNs for plant image segmentation, thus making them easily deployable infield and on mobile devices. In particular, we focus on applying these models to the pixel-wise segmentation of plants into multiple classes including background, a challenging problem in the plant phenotyping community. We combined two approaches (separable convolutions and SVD) to reduce model parameter numbers and weight matrices of these very deep CNN-based models. Using our combined method (separable convolution and SVD) reduced the weight matrix by up to 95% without affecting pixel-wise accuracy. These methods have been evaluated on two public plant datasets and one non-plant dataset to illustrate generality. We have successfully tested our models on a mobile device.