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The autonomous collision avoidance problem for multi-trailer vehicle maneuvering is investigated in this paper. Different from conventional vehicle systems that contain one single moving part or multi-parts that can be considered as one rigid body, the interconnection between the tractor and each trailer, and interactions between trailers in the multi-trailer system introduce a high dimensional and highly complex dynamic system for the controller design. The external disturbance and parametric uncertainties further increase the difficulty in system identification and state space formulation. To implement a real time control system for various scenarios where the locations and states of the obstacles are not known beforehand, a supervisory algorithm is designed to convert the control problem to a discrete event system. The model predictive control (MPC) using limited lookahead policy is employed in the proposed algorithm.

Research interests in autonomous driving have increased significantly in recent years. Several methods are being suggested for performance optimization of autonomous vehicles. However, weather conditions such as rain, snow, and fog may hinder the performance of autonomous algorithms. It is therefore of great importance to study how the performance/efficiency of the underlying scene understanding algorithms vary with such adverse scenarios. Semantic segmentation is one of the most widely used scene-understanding techniques applied to autonomous driving. In this work, we study the performance degradation of several semantic segmentation algorithms caused by rain for off-road driving scenes. Given the limited availability of datasets for real-world off-road driving scenarios that include rain, we utilize two types of synthetic datasets.

A dynamic modeling framework was established to predict status (position, displacement, velocity, acceleration, and shape) of a towed vehicle system with different driver inputs. This framework consists of three components: (1) a state space model to decide position and velocity for the vehicle system based on Newton’s second law; (2) an angular acceleration transferring model, which leads to a hypothesis that the each towed unit follows the same path as the towing vehicle; and (3) a polygon model to draw instantaneous polygons to envelop the entire system at any time point. Input parameters of this model include initial conditions of the system, real-time locations of a reference point (e.g. front center of the towing vehicle) that can be determined from a beacon and radar system, and instantaneous accelerations of this system, which come from driver maneuvers (accelerating, braking, steering, etc.) can be read from a data acquisition system installed on the towing vehicle.

A robot mining system was developed by the State Space Robotic undergraduate student design team from Mississippi State University (MSU) for the 2016 NASA Robotic Mining Competition. The mining robot was designed to traverse the Martian chaotic terrain, excavate a minimum of 10 kg of Martian regolith and deposit the regolith into a collector bin within 10 minutes as part of the competition. A Systems Engineering approach was followed in proceeding with this design project. The designed mining robot consisted of two major components: (1) mechanical system and (2) control system. This paper mainly focuses on the design and assessment process of the mechanical system but will also briefly mention the control system so as to evaluate the designed robotic system in its entirety. The final designed robot consisted of an aluminum frame driven by four motors and wheels. It utilized a scoop and lifting arm subsystem for collecting and depositing Martian regolith.