Robust Motion Planning for Hopping Robots PhD

Hopping robots are gaining significant attention for their ability to traverse rugged terrains, cover long distances efficiently, and overcome large obstacles in microgravity environments. They are particularly valuable for planetary surface exploration in cluttered and disconnected regions, such as craters, boulder fields, and valleys. However, planning hopping sequences is challenging due to positional drift caused by robot model uncertainties, Navigational inaccuracies, and undesired post-landing motions (e.g., slipping, rolling, or bouncing).

This PhD research will address these challenges and propose a robust and safe dynamic hopping. The problem will be formulated in an optimal control framework which will consider all challenges and provide an optimised solution which can be implemented in real-time. The research will be validated through numerical simulations and experiments in extreme environments.

Aim: To develop a real-time, robust motion planning framework for hopping robots ensuring safe and adaptive navigation in uncertain environments.

Objectives:

1. Uncertainty Modelling: Identify and quantify key sources of positional drift (e.g., model uncertainties, navigational inaccuracies, post-landing motions).

2. Safe trajectory generation: To reduce the error statistics (due to uncertainty) of the hopping robot during a hop to an acceptable value which gives safe trajectories under these uncertainties.

3. Real-Time Planning: Integrate the safe trajectory generation into a dynamic motion planning framework to generate safe hopping sequences.

4. Simulation & Validation: Test and refine the approach through simulation studies and real-world experiments on hopping robots navigating cluttered and disconnected regions.

5. Scalability & Generalisation: Extend the framework to accommodate varying terrain conditions and different hopping robot platforms.

At a glance

Application deadline02 Apr 2025
Award type(s)PhD
Start date02 Jun 2025
Duration of award3 years full-time and 6 years part-time
EligibilityUK, EU, Rest of world
Reference numberSATM552

Entry requirements

We seek highly motivated candidates with:

1. A strong background in robotics, optimal control theory with understanding of uncertainty modelling, nonlinear dynamics, and convex optimisation.

2. Proficiency in Python, MATLAB, or C++ and simulation tools like ROS, Gazebo.

3. Experience with experimental validation of robotic systems.

Funding

This is a self-funded opportunity.

This studentship is open to both UK and international applications.

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