Combining a hierarchical task network planner with a constraint satisfaction solver for assembly operations involving routing problems in a multi-robot context

This work addresses the combination of a symbolic hierarchical task network planner and a constraint satisfaction solver for the vehicle routing problem in a multi-robot context for structure assembly operations. Each planner has its own problem domain and search space, and the article describes how both planners interact in a loop sharing information in order to improve the cost of the solutions. The vehicle routing problem solver gives an initial assignment of parts to robots, making the distribution based on the distance among parts and robots, trying also to maximize the parallelism of the future assembly operations evaluating during the process the dependencies among the parts assigned to each robot. Then, the hierarchical task network planner computes a scheduling for the given assignment and estimates the cost in terms of time spent on the structure assembly. This cost value is then given back to the vehicle routing problem solver as feedback to compute a better assignment, closing the loop and repeating again the whole process. This interaction scheme has been tested with different constraint satisfaction solvers for the vehicle routing problem. The article presents simulation results in a scenario with a team of aerial robots assembling a structure, comparing the results obtained with different configurations of the vehicle routing problem solver and showing the suitability of using this approach.Unión Europea ARCAS FP7-ICT-287617Unión Europea H2020-ICT-644271Unión europea H2020-ICT-73166

The AEROARMS Project: Aerial Robots with Advanced Manipulation Capabilities for Inspection and Maintenance

This article summarizes new aerial robotic manipulation technologies and methods—aerial robotic manipulators with dual arms and multidirectional thrusters—developed in the AEROARMS project for outdoor industrial inspection and maintenance (I&M). Our report deals with the control systems, including the control of the interaction forces and the compliance the teleoperation, which uses passivity to tackle the tradeoff between stability and performance the perception methods for localization, mapping, and inspection the planning methods, including a new control-aware approach for aerial manipulation. Finally, we describe a novel industrial platform with multidirectional thrusters and a new arm design to increase the robustness in industrial contact inspections. In addition, the lessons learned in applying the platform to outdoor aerial manipulation for I&M are pointed out

Introduction to the Special Issue on Aerial Manipulation

The papers in this special section focus on aerial manipulation which is intended as grasping, positioning, assembling and disassembling of mechanical parts, measurement instruments and any other kind of objects, performed by a flying robot equipped with arms and grippers. Aerial manipulators can be helpful in those industrial and service applications that are considered very dangerous for a human operator. For instance, think of tasks like the inspection of a bridge, the inspection and the fixing-up of high-voltage electric lines, the repairing of rotor blades and so on. These tasks are both very unsafe and expensive because they require the performance of professional climbers and/or specialists in the field. A drone with manipulation capabilities can instead assist the human operator in these jobs or, at least, in the most hazardous and critical situations. As a matter of fact, such devices can indeed operate in dangerous tasks like reaching the bottom of the deck of a bridge or the highest places of a plant or a building; they can avoid dangerous work at height; aerial platforms can increase the total number of inspections of a plant, monitoring the wear of the components. Without doubts, aerial manipulation will improve the quality of the job of many workers

Sampling-based Motion Planning for Active Multirotor System Identification

This paper reports on an algorithm for planning trajectories that allow a multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown parameters. In many problems like self calibration or model parameter identification some states are only observable under a specific motion. These motions are often hard to find, especially for inexperienced users. Therefore, we consider system model identification in an active setting, where the vehicle autonomously decides what actions to take in order to quickly identify the model. Our algorithm approximates the belief dynamics of the system around a candidate trajectory using an extended Kalman filter (EKF). It uses sampling-based motion planning to explore the space of possible beliefs and find a maximally informative trajectory within a user-defined budget. We validate our method in simulation and on a real system showing the feasibility and repeatability of the proposed approach. Our planner creates trajectories which reduce model parameter convergence time and uncertainty by a factor of four.Comment: Published at ICRA 2017. Video available at https://www.youtube.com/watch?v=xtqrWbgep5

경첩문을 여는 비행 매니퓰레이터에 대한 모델 예측 제어

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계항공공학부, 2020. 8. 김현진.From aerial pick-and-place to aerial transportation, aerial manipulation has been extensively studied in recent years thanks to its mobility and dexterity, each of which is a merit of an aerial vehicle and a robotic arm. However, to fully harness the concept of aerial manipulation, more complex tasks including interaction with movable structures should also be considered. Among various types of movable structures, this paper presents a multirotor-based aerial manipulator opening a daily-life moving structure, a hinged door. Two additional issues that would arise in interaction with a movable structure are addressed: 1) a constrained motion of the structure, and 2) collision avoidance with a moving structure. To handle these issues, we formulate a model predictive control (MPC) problem with a system dynamics constraint and state constraints for collision avoidance. A coupled system dynamics of a multirotor-based aerial manipulator and a hinged door is derived and later simplified for faster computation in MPC. State constraints for collision avoidance with itself, a door, and a doorframe are generated. By implementing a constrained version of differential dynamic programming (DDP), we can generate reference trajectories through MPC in real-time. The proposed method is validated through simulation results and experiments with a real-like hinged door in which a disturbance observer (DOB) based robust motion controller is employed.비행 매니퓰레이터는 3차원 공간 속에 빠르게 위치할 수 있는 비행체의 장점과 외부와의 상호작용이 가능한 로봇팔의 장점이 결합된 비행체로, 최근 물건 집고 옮기기부터 물품 운송까지 다양한 임무를 수행하기 위해 활발하게 연구되어 왔다. 그러나, 온전히 비행 매니퓰레이터의 가능성을 활용하기 위해서는 움직일 수 있는 외부 구조와의 상호작용과 같이 더욱 복잡한 임무 또한 수행할 수 있어야 할 것이다. 여러 종류의 움직일 수 있는 구조물 중 본 논문에서는 일상 속에서 쉽게 마주칠 수 있는 경첩문을 여는 멀티로터 기반의 비행 매니퓰레이터에 대해 제시한다. 정적인 구조물과의 상호작용과는 달리 동적인 구조물과의 상호작용에 있어서 발생할 수 있는 1) 구조물의 제약된 움직임, 그리고 2) 움직이는 구조물과의 충돌 회피의 2가지 추가적인 문제에 대해 다루었다. 이러한 문제를 다루기 위해 모델 예측 제어 (MPC)를 적용하였으며, 시스템 동역학에 대한 제약조건 및 충돌 회피에 대한 제약 조건을 부여하였다. 멀티로터 기반의 비행 매니퓰레이터와 경첩문의 결합 시스템에 대한 동역학을 유도하였으며, 이후 모델 예측 제어에서의 빠른 계산 속도를 위해 단순화되었다. 충돌 회피에 대한 제약 조건은 모두 상태 변수로 표현되었으며, 비행 매니퓰레이터의 멀티로터 프레임과 로봇팔 사이의 충돌 (자기 충돌), 문과의 충돌, 그리고 문틀과의 충돌을 고려하였다. 미분 기반의 동적 프로그래밍 기법 (differential dynamic programming)에 제약조건이 고려된 알고리즘을 구현함으로써 모델 기반 예측 제어를 통해 실시간으로 경로를 계획할 수 있다. 제안된 방법은 시뮬레이션과 실제 크기의 문을 활용한 실험을 통해 검증되었으며, 외란 관측기 기반의 강건 제어 기법이 실험에 활용되었다.1 Introduction 1 1.1 Literature review 2 1.2 Thesis contribution 3 1.3 Thesis outline 3 2 Equations of motion 4 2.1 Assumption 4 2.2 Kinematics 5 2.3 Dynamics 6 2.4 Simpli ed dynamics 8 3 Model predictive control 10 3.1 Problem formulation 10 3.1.1 Objective function 11 3.1.2 Hard constraints 11 3.2 Collision avoidance constraints 11 3.2.1 Self collision avoidance 13 3.2.2 Door collision avoidance 13 3.2.3 Doorframe collision avoidance 14 3.3 Optimal control solver 14 3.3.1 Differential dynamic programming 14 3.3.2 DDP with augmented Lagrangian method 15 4 Experimental setup 17 4.1 Door state estimation 17 4.2 Multirotor robust controller 18 4.3 Hardware setup 19 5 Results 20 5.1 Simulation results 20 5.2 Experimental results 25 6 Conclusion 29Maste