3 accepted papers
Achieving efficient and uniform coverage in obstacle-laden unknown environments is essential for au- tonomous robots in cleaning, inspection and agricultural op- erations. Unlike most existing approaches that prioritize path length and time optimality, we propose the SHIFT planner framework, which i…
Unmanned ground vehicles operating in complex environments must adaptively adjust to modeling uncertainties and external disturbances to perform tasks such as wall following and obstacle avoidance. This paper introduces an adaptive control approach based on spiking neural networks for wall fitting a
Bayesian nonparametric models, such as the Dirichlet Process Gaussian Process (DPGP), have been shown very effective at learning models of dynamic targets exclusively from data. Previous work on batch DPGP learning and inference, however, ceases to be efficient in multi-sensor applications that requ…