Implementation of an automated eye-in-hand scanning system using Best-Path planning

In this thesis we implemented an automated scanning system for 3D object reconstruction. This system is composed of a KUKA LWR 4+ arm with Microsoft Kinect cameras placed on its extreme and thus, in an eye-in-hand con guration. We implemented the system in ROS using Kinect Fusion software with extra features added by R. Monica's previous work [16] and MoveIt! ROS libraries [29] to control the robot movement with motion planning. To connect these nodes, we have coded a suite using ROS and MATLAB to easily operate them as well as including new features, such as an original view planner that outperforms the commonly used Next-Best-View planner. This suite incorporates a Graphical User Interface that allows new users to easily perform the reconstruction tasks. The new view planner developed in this work, called Best-Path planner, o ers a new approach using a modi ed Dijkstra algorithm. Among its bene ts, Best-Path planner o ers an optimized way to scan the objects preventing the camera to cross again the areas which have already been scanned. Moreover, viewpoint location and orientation have been studied in depth in order to obtain the most natural movements and get the best results. For this reason, this new planner makes the scanning procedure more robust as it assures trajectories through these optimized viewpoints, so the camera is always looking towards the object maintaining the optimal sensing distances. As this project is focused on its later utility in the Intelligent Robotics Laboratory, we uploaded all the source code in the Aalto GitLab repositories [37] with installation instructions and user guides to show the di erent features that the suite o ers

Task planning using physics-based heuristics on manipulation actions

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In order to solve mobile manipulation problems, the efficient combination of task and motion planning is usually required. Moreover, the incorporation of physics-based information has recently been taken into account in order to plan the tasks in a more realistic way. In the present paper, a task and motion planning framework is proposed based on a modified version of the Fast-Forward task planner that is guided by physics-based knowledge. The proposal uses manipulation knowledge for reasoning on symbolic literals (both in offline and online modes) taking into account geometric information in order to evaluate the applicability as well as feasibility of actions while evaluating the heuristic cost. It results in an efficient search of the state space and in the obtention of low-cost physically-feasible plans. The proposal has been implemented and is illustrated with a manipulation problem consisting of a mobile robot and some fixed and manipulatable objects.Peer ReviewedPostprint (author's final draft

Automated sequence and motion planning for robotic spatial extrusion of 3D trusses

While robotic spatial extrusion has demonstrated a new and efficient means to fabricate 3D truss structures in architectural scale, a major challenge remains in automatically planning extrusion sequence and robotic motion for trusses with unconstrained topologies. This paper presents the first attempt in the field to rigorously formulate the extrusion sequence and motion planning (SAMP) problem, using a CSP encoding. Furthermore, this research proposes a new hierarchical planning framework to solve the extrusion SAMP problems that usually have a long planning horizon and 3D configuration complexity. By decoupling sequence and motion planning, the planning framework is able to efficiently solve the extrusion sequence, end-effector poses, joint configurations, and transition trajectories for spatial trusses with nonstandard topologies. This paper also presents the first detailed computation data to reveal the runtime bottleneck on solving SAMP problems, which provides insight and comparing baseline for future algorithmic development. Together with the algorithmic results, this paper also presents an open-source and modularized software implementation called Choreo that is machine-agnostic. To demonstrate the power of this algorithmic framework, three case studies, including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure

Planning with Incomplete Information

Planning is a natural domain of application for frameworks of reasoning about actions and change. In this paper we study how one such framework, the Language E, can form the basis for planning under (possibly) incomplete information. We define two types of plans: weak and safe plans, and propose a planner, called the E-Planner, which is often able to extend an initial weak plan into a safe plan even though the (explicit) information available is incomplete, e.g. for cases where the initial state is not completely known. The E-Planner is based upon a reformulation of the Language E in argumentation terms and a natural proof theory resulting from the reformulation. It uses an extension of this proof theory by means of abduction for the generation of plans and adopts argumentation-based techniques for extending weak plans into safe plans. We provide representative examples illustrating the behaviour of the E-Planner, in particular for cases where the status of fluents is incompletely known.Comment: Proceedings of the 8th International Workshop on Non-Monotonic Reasoning, April 9-11, 2000, Breckenridge, Colorad