The aviation industry has proposed multiple solutions to reduce fuel consumption, air pollution, and noise at airports, one of which involves deploying electric trucks for aircraft towing between the stand and the runway. However, the introduction of tow trucks results in increased surface traffic, posing challenges from the perspective of air traffic controllers (ATCOs). Various solutions involving automated planning and execution have been proposed, but many are constrained by their inability to manage multiple active runways simultaneously, and their failure to account for the tow truck battery state of charge during assignments. This paper presents a novel system for taxi operations that employs autonomous tow trucks to enhance ground operations and address deficiencies in existing approaches. The system focuses on identifying conflict-free solutions that minimise taxi-related delays and route length while maximising the efficient use of the tow trucks. The algorithm operates at a strategic level and uses a centralised approach. It has the capacity to cater for multiple active runways and considers factors such as the tow truck battery state of charge and availability of charging stations. Furthermore, the proposed algorithm is capable of scheduling and routing tow trucks for aircraft taxiing without generating traffic conflicts.

To date, most of the interactions between pilots and avionic systems of commercial passenger aircraft are carried out using physical controls. This paper discusses the evaluation of a multimodal interface which allows pilots to control various avionic systems using a combination of touchscreen gestures (via a single touchscreen interface) and voice commands. This interface was evaluated by eight commercial airline pilots in a fixed-based flight simulator. Although various improvements can be made to the interface, the evaluations successfully demonstrated the feasibility and potential benefits of using a multimodal interface in commercial flight.

There is an ever-increasing urge to integrate unmanned air systems (UAS) into the same airspace as manned aircraft. A step towards achieving this goal is to develop Sense and Avoid (SAA) capabilities to provide UAS with the required technology to detect and maneuver around traffic. This paper details an algorithm which enables an unmanned vehicle to reach a target waypoint by following the shortest path and avoiding fixed obstacles and moving traffic. In this context, a multi-layer planner is developed to enable planning of trajectories in cluttered environment containing fixed and moving obstacles. The planner is segmented into three layers; a high-level planner using Voronoi techniques that enable the planner to segregate individual obstacles into broad no-fly zones; a mid-level planner for representing the no-fly zones as convex hulls and a low-level planner that solves the resulting planning problem using mixed-integer linear programming (MILP), taking into account the convex no-fly zones and all neighboring traffic. Preliminary performance results suggest that the multi-layer planner is able to consistently identify optimal conflict-free paths in cluttered environments with execution speeds that are suitable for fast off-line planning operations.