Rapid Local Trajectory Planning for Fast-moving Autonomous Ground Vehicles in Unknown Environments

Research question: Can the computational efficiency of local trajectory planning algorithms for mobile robots be improved without compromising the safety, feasibility, or optimality of the generated trajectories?

Motivation: Mobile robots are being adopted to carry out a wide variety of tasks across industries including construction, agriculture, space exploration, mining, search and rescue, and logistics. Due
to the limited bandwidth of wireless communication and the impracticality of employing a
tether, it is desirable for mobile robots to perform computations relevant to their task using
on-board resources. However, this introduces a limit on the amount of computational power
available. Therefore, improving the computational efficiency of the robot navigation processes is desirable for on-board operation with limited computational resources.

Aim: The aim of this research is to develop a local trajectory planner for mobile robots that improves on the computational efficiency of the state-of-the-art without compromising the optimality, safety, or feasibility of the generated trajectories.

Proposed Application: This work will focus on the aggressive driving made possible by computationally efficient trajectory generation, and disaster response is a key area of application. It is a highly time-critical field, and robots must often operate in environments with widely varying levels of disruption to communication
Infrastructure.