-
UKAEA-RACE-PR(25)032024
When using deep reinforcement learning (DRL) to perform multi-robot exploration in unknown environments, the training model may produce actions that lead to unpredictable system behaviours due to the complexity and unpredictability of the surroundings. Therefore, ensuring safe exploration with DRL becomes critical. To tackle this issue, we propo…
-
UKAEA-RACE-CP(24)012023
In this paper we propose a multi-robot path planning algorithm that integrates a Vector Field Histogram (VFH) algorithm and fuzzy logic controller inspired by International Regulations for Preventing Collisions at Sea (COLREGs) traffic rules with a collision area membership function that identifies suitable traffic procedures for each robot to t…
-
UKAEA-RACE-CP(23)092023
When transferring a Deep Reinforcement Learning (DRL) model from simulation to the real world, the performance could be unsatisfactory since the simulation cannot imitate the real world well in many circumstances. This results in a long period of fine-tuning in the real world. This paper proposes a self-supervised vision-based DRL method that al…
-
UKAEA-RACE-CP(23)062023
Sim-and-real training is a promising alternative to sim-to-real training for robot manipulations. However, the current sim-and-real training is neither efficient, i.e., slow convergence to the optimal policy, nor effective, i.e., sizeable real-world robot data. Given limited time and hardware budgets, the performance of sim-and-real training is …
-
UKAEA-RACE-PR(22)062022
The maturation of Virtual Reality software introduces new avenues of nuclear decommissioning research. Digital Mockups are an emerging technology which provide a virtual representation of the environment, objects or processes, supporting the whole lifecycle of product development and operations. This paper provides a survey on currently available …
Showing 1 - 5 of 5 UKAEA Paper Results
Page 1 of 1