Adaptive Deep Path: Efficient Coverage of a Known Environment under Various Configurations Coverage path planning of a known environment sees a variety of applications, including cleaning, surveillance, agriculture and 3D printing. Most approaches employ hardcoded heuristics or other applicatonspecific requirements, making them hard to extend to other problem scenarios or xe2x80x9cconfigurations,xe2x80x9d such as different motion strategies or robot size. This work presents a unifying, general, and adaptive framework, called adaptive deep path (AD Path), for coverage path planning problems under a variety of configurations. It can improve path efficiency with respect to both path length and number of turns, and can flexibly accommodate different problem configuration options. We evaluate AD Path against a stateoftheart baseline in four complex environments with different configurations. We show that our approach can produce efficient paths; our experimental results show that AD Path can reduce the path length by 21.8% and the number turns by 38.6% on average compared with the baseline.

ContextDependent Search for Generating Paths for Redundant Manipulators in Cluttered Environments We present a contextdependent bidirectional treesearch framework for pointtopoint path planning for manipulators. Conceptually, our framework is composed of six modules: tree selection, focus selection, node selection, target selection, extend selection and connection type selection. Each module consists of a set of interchangeable strategies. By exploiting synergistic interaction between these strategies and selecting appropriate strategies based the contextual cues from the search state, we show an instance of our framework that computes highquality solutions in a variety of complex scenarios with a low failure rate. We also show that some popular path planning methods in the literature can be easily represented in our framework. We compare our approach with these popular methods in a diverse set of test scenarios. We report a 15fold reduction in failure rate coupled with at least a 26% drop in solution suboptimality when compared to the best of the alternative methods.