Nathan Wallace

About

Nathan Wallace is a robotics researcher whose work sits at the intersection of autonomous systems, energy-aware planning, and agricultural robotics. His research addresses one of the most pressing challenges in field robotics: enabling ground robots to operate autonomously over extended periods in complex, unstructured real-world environments such as farms and undulating off-road terrains. Wallace's most influential contributions focus on energy-aware mission planning and resource-constrained path planning for wheeled mobile robots (WMRs). His 2019 and 2022 papers on energy-aware planning and long-term agricultural autonomy have each garnered 26 citations, reflecting strong community interest in practical robot deployment strategies. His work on motion cost characterisation and slip compensation demonstrates a rigorous experimental approach to understanding how terrain conditions affect robot performance and control reliability. Beyond individual robot navigation, Wallace has tackled higher-level challenges including the Orienteering Problem with Replenishment—extending classical logistics problems to resource-limited robotic agents—and hierarchical frameworks for large-scale farm deployment. His more recent research expands into digital twins, Eco-Cyber-Physical Systems, and Gaussian process-based adaptive sampling, signalling a growing ambition to connect autonomous robotics with smart farming ecosystems. With over 90 cumulative citations, Wallace represents an emerging voice in making agricultural robots genuinely practical and long-lasting in the field.

Research Focus

Key Achievements

Top Papers

1. 1
   Energy Aware Mission Planning for WMRs on Uneven Terrains

   26 citations · 2019
2. 2
   Resource and Response Aware Path Planning for Long-Term Autonomy of Ground Robots in Agriculture

   26 citations · 2022
3. 3
   Motion Cost Characterisation of an Omnidirectional WMR on Uneven Terrains

   11 citations · 2019
4. 4
   Experimental Validation of Structured Receding Horizon Estimation and Control for Mobile Ground Robot Slip Compensation

   9 citations · 2021
5. 5
   The orienteering Problem with Replenishment

   6 citations · 2020
6. 6
   A Framework to Enable Eco‐Cyber‐Physical Systems for Robotics‐Focused Digital Twins in Smart Farming

   4 citations · 2025
7. 7
   Multivariate Active Learning and Adaptive Sampling With Multi-Kernel Gaussian Processes

   3 citations · 2025
8. 8
   Receding horizon estimation and control with structured noise blocking for mobile robot slip compensation

   2 citations · 2019
9. 9
   A Hierarchical Framework for Long-term and Robust Deployment of Field Ground Robots in Large-Scale Farming

   2 citations · 2020
10. 10
    Energy-aware Planning and Control of Off-road Wheeled Mobile Robots

    2 citations · 2020

Key Collaborators

Contact & Links