Samplingbased Motion Planning for Aerial PickandPlace This paper presents a motion planning approach for an aerial pickandplace task where an aerial manipulator is supposed to pick up or place an object at locations specified as waypoints. In particular, we focus on situations where such waypoint constraints are imposed on certain partial state variables, rather than on full state variables. Our proposed framework, based on rapidly exploring random trees star (RRT*) in a bidirectional manner, enables an aerial manipulator to find an optimal trajectory that satisfies waypoint constraints with only partial specifications. Here, we suggest an extra merging process to integrate the trees, each originated from the start and goal point. In the merging process, we search various candidate points satisfying a given condition that partially constrains state variables, and select a waypoint with full specifications optimal in the perspective of the entire trajectory. Simulation and experiment results are included to validate the proposed framework.

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.