EPSRC Centre for Doctoral Training in Agri-Food Robotics: AgriFoRwArdS - Roopika Ravikanna 3

Roopika Ravikanna

  • University of Lincoln in collaboration with Saga Robotics

Research Interests

Human-robot interaction.

Papers and Presentations

  • Ravikanna, R. (2020) Task Allocation in Multi Robot Systems using Dynamic Manipulative Bidding Strategy. Lincoln Conference on Intelligent Robots and Systems 2020, online.
  • Ravikanna, R. (2021) The perks in studying robotics. International Women in Engineering Day: Lincoln Institute of Technology, online. Watch here (talk starts at 58.00 minutes).
  • Ravikanna, R. (2021) Task Allocation using Manipulative Dynamic Auctioneer System, UKRAS21 Conference, online.
  • Ravikanna, R. (2021) Allocation of Parking Spaces for Autonomous Agricultural Robots. AgriFoRwArdS CDT Annual Conference 2021, online.
  • Aardweg, C., Hurst, B., Osmond, W., Ravikanna, R., and Roberts-Elliot, L. (2021) Automated Counting of Grapes, AgriFoRwArdS CDT Annual Conference 2021, online.
  • Ravikanna, R. (2021) Maximising availability of fruit transportation robots through intelligent allocation of parking spaces. Festival of Fresh 2021, online.

Extra-Curricular Activities

  • Member of the AgirFoRwArdS CDT Drink Outside the Box Organisation Committee.

MSc Project

Leaky Integrate Neuron Model for Multi Robot Task Allocation

The evolution of methodologies in Multi Robot Task Allocation is surveyed. Various classifications of Taxonomy in the domain is compared to the relevance of its application. An idea for a novel ‘Leaky Integrate and Fire’ Neuron Model for Multi Robot Task Allocation is presented along with the explanation of its biological theory. This is used as an inspiration to model a setup of heterogenous autonomous Multi Robot system in a farmland environment. The model would be simulated as a software using Python. Performance of the system will be evaluated and compared to that of a traditional Market based approach. Findings shall be drawn and Results are to be summarised.

PhD Project

Fleet Management of Autonomous Agricultural Robots with Human Awareness

Fleet management of autonomous agricultural robots is fundamental to fully autonomous farming practices as it addresses the issues of conflict resolution, resource and efficiency optimisation when dealing with a fleet of homogenous or heterogenous robots. The abstract of the proposal for this PhD would be integrating human behaviour models with the navigation and task allocation system of autonomous fleets for better path planning evading possible conflicts and deadlock situations between robot-robot and human-robot(s). The study will focus on the proxemics, human behaviour modelling using decision prediction and neuroscientific methods, path planning, fleet coordination and human robot ethics.