Control of multivehicle systems in the presence of uncertain dynamics

In this paper, we present a cooperative control architecture for high-order multivehicle systems having non-identical nonlinear uncertain dynamics. The proposed methodology consists of a local cooperative controller and a vehicle-level controller for each vehicle. The former controller receives the relative output measurements of the neighbouring vehicles in order to solve a containment problem formulated on a leader–follower framework. Specifically, the leaders generate trajectories in which the vehicles (followers) converge to the convex hull formed by those of the leaders. For a special case with one leader, this controller synchronises the output of the vehicles with the output of the leader. The latter controller receives the internal-state measurements for suppressing the nonlinear uncertain dynamics of the vehicle by using a decentralised adaptive control approach. The interaction topology between vehicles is described by undirected graphs and extensions to directed graphs are further discussed. The stability and convergence properties of the proposed cooperative control architecture are analysed by using the results from linear algebra and the Lyapunov theory. Several numerical examples are provided to demonstrate the efficacy of the proposed cooperative control architecture.     

Deep neural networks to predict autonomous ground vehicle behavior on sloping terrain field

Conventional large agricultural machinery or implements are unsafe and unsuitable to operate on slopes $>6^{\circ }$ or 10%. Tractor rollovers are frequent on slopes, precluding farming on arable hills, uneven or highly sloped land. Therefore, a fleet of autonomous ground vehicles (AGV) is proposed to cultivate highly sloped land ($>6^{\circ }$). The fleet aims to expand agricultural land to the slopes and to strengths the human-robot collaboration in an unsafe sloped environment. However, the fleet's success largely depends on vehicle behavior models regarding traction, mobility, and energy consumption on varying slopes. The vehicle intelligent behavior models are essential and would solve multiple objectives ranging from simulations to path planning & navigation. Therefore, this study aimed to build a deep learning-based vehicle behavior models on sloping terrain. A standard drawbar test was performed on a single AGV operating on an actual sloped field at varying speeds and load conditions. The drawbar test quantified the AGV's behavior on slopes in metrics related to traction (traction efficiency), mobility (travel reduction), and energy consumption (power number). Deep learning-based models were developed from the experimental data to predict the AGV's behavior on slopes as a function of vehicle velocity, drawbar, and slope. A special model called the proposed model, which combined multiple deep neural networks with a mixture of Gaussians, was developed and trained with a hybrid training method. The proposed model consistently outperformed the other well-known machine learning models. This study explored the capabilities of machine learning algorithms to simulate the behavior of small-track vehicle or AGV on sloping terrain. The fleet aims to provide safer agriculture keeping human safety in focus, and the developed predictive vehicle behavior models would empower the fleet's operation on currently unsafe sloped terrain by assisting in vehicle path planning, route optimization, and decision making.