# Challenges in the Guidance, Navigation and Control of Autonomous and Transport Vehicles
## 2 Growing Research Areas in Vehicle Navigation and Control
In Contribution 6, a UAV-UGV multicast cluster-based routing approach is used to increase the communication throughput and reduce delays in landmine detection and message dissemination between different vehicles. Cluster maintenance algorithms are used to incorporate motion (speed, direction) information and update the record (group) of vehicles in the cluster. Control challenges are also presented in this book to address emerging challenges with advances in power, actuation and control technologies. Contribution 8 is focused on the use of nonlinear programming to optimise hybrid electric vehicle (HEV) power distributions. Contribution 15 focuses on torque vectoring strategies for hybrid vehicles with in-wheel rear motors and presents a comparison of different optimal and sliding-mode control strategies. In Contribution 10, a fuzzy logic rule-based approach is used to determine the torque of the powertrain system and manage regenerative braking and eco-approach, using a conventional pure pursuit lateral controller and realistic powertrain models. In Contribution 19, a new actuator that uses shape-memory alloys is used for idle air control, with PID control to manage actuation stroke delays. Path planning and trajectory optimisation issues are also covered in this book. In Contribution 14, an improved hybrid A* graph-based global path planning algorithm is used for a nonholonomic mobile robot using a robot operating system (ROS) implementation and model predictive control (MPC) for local path planning to avoid obstacles, with reduced path lengths and search times as compared to A* + DWA (dynamic window approach). A new path planning algorithm is also proposed in Contribution 18, using an improved bidirectional search Gaussian (BSG) A* algorithm for differential-drive mobile robots, with reduced path lengths and search times as compared to D* lite (dynamic A*), the genetic algorithm (GA) and ant colony optimisation (ACO). Machine learning is another research trend in this field, particularly for increasingly autonomous vehicles. Reinforcement learning is used in Contribution 20 for the dynamic trajectory optimisation of a self-driving vehicle. Other areas that are less strongly present in this Special Issue but where research will be essential include the handling of connectivity constraints in vehicle trajectory optimisation and control problems, particularly the ones requiring communication between vehicles, as in the case of unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) collaboration, but more generally in the case of vehicles navigating within sensed environments and obtaining information from a ground sensor network using advancesfrom the Internet of Things (IoT). The human-machine aspects of interfacing human operator input with automation remain important, and Contribution 2 of this Special Issue illustrates an example of the effect of onboard training in simulated maritime environments on the navigation performance of autonomous surface vessels. Contribution 9 focuses on scenario designs for advanced driver assistance systems (ADASs) and autonomous driving systems (ADSs) applied to longitudinal motion using MPC-based driver models. To construct the models, human drivers' characteristics were derived using a large driving database. Future systems are expected to allow both humans and machines to learn from each other, even though increasingly autonomous systems are also expected to operate with reduced human operator input.