Unmanned Aerial Vehicles (UAV) are a potential solution to fast and cost efficient package delivery services. There are two types of UAVs, namely fixed wing (UAV-FW) and rotor wing (UAV-RW), which have their own advantages and drawbacks. In this paper we aim at providing different solutions to a collaborating multi-agent scenario combining both UAVs types. We show the problem can be reduced to the facility location problem (FLP) and propose two local search algorithms to solve it: Tabu search and simulated annealing.

We address the problem of planning the placement of a grasped object with a robot manipulator. More specifically, the robot is tasked to place the grasped object such that a placement preference function is maximized. For this, we present an approach that uses in-hand manipulation to adjust the robot’s initial grasp to extend the set of reachable placements. Given an initial grasp, the algorithm computes a set of grasps that can be reached by pushing and rotating the object in-hand. With this set of reachable grasps, it then searches for a stable placement that maximizes the preference function. If successful it returns a sequence of in-hand pushes to adjust the initial grasp to a more advantageous grasp together with a transport motion that carries the object to the placement. We evaluate our algorithm’s performance on various placing scenarios, and observe its effectiveness also in challenging scenes containing many obstacles. Our experiments demonstrate that re-grasping with in-hand manipulation increases the quality of placements the robot can reach. In particular, it enables the algorithm to find solutions in situations where safe placing with the initial grasp wouldn’t be possible.

In this paper, we present a motion planning framework for humanoid robots that combines whole-body motions as well as footsteps under a quasi-static flat ground plane assumption. Traditionally, these two have been treated as separate research domains. One of the major challenges behind whole body motion planning is the high DoF (Degrees of Freedom) nature of the problem, in addition to strict constraints on obstacle avoidance and stability. On the other hand footstep planning on its own is a comparatively simpler problem due to the low DoF search space, but coalescing it into a larger framework that includes whole-body motion planning adds further complexity in reaching a solution within a suitable time frame that satisfies all the constraints. In this work, we treat motion planning as a graph search problem, and employ Shared Multi-heuristic A* (SMHA*) to generate efficient, stable and collision-free motion plans given only the starting state of the robot and the desired end-effector pose.