Programming by Demonstration (PbD) is a simple and efficient way to program robots without coding. PbD enables unskilled operators to demonstrate and guide robots to execute even the most complex tasks. This work aims to compare two approaches to PbD with a comprehensive user study, focusing on a common human skill. Each participant had to demonstrate to a robot how to draw a simple pattern both using a virtual marker and kinesthetic teaching. To evaluate differences between these demonstration approaches, we conducted a user study with 24 participants, benchmarking programmed trajectories, NASA raw task load index (rTLX), and system usability scale (SUS). We evaluated the similarity of the executed trajectories measuring difference between demonstrated and ideal trajectories. Our results show that human demonstration using a virtual marker is on average 8 times faster, superior in terms of quality and imposes 2 times less overall workload than kinesthetic teaching.

This paper proposes a unified dynamic model for aerial robots which encompasses all known actuation principles including tilting propellers and centroid variation methods such as moving masses or robotic manipulators. Of course, one can envision a wide variety of vehicles, all with different combinations of actuation principles. Therefore, a generalized modeling methodology for such vehicles is developed and presented in this paper. The modeling approach is verified through a comparative analysis of MATLAB simulations and laboratory experiments with a tilting propeller aerial manipulator vehicle, named Toucan. Finally, in order to fully explore and exploit the capabilities of the designed aerial manipulator, contact-based experiments with force tracking are performed using the proposed adaptive impedance controller. To demonstrate the advantages of such a vehicle, the adaptive impedance control method is used to generate position and orientation commands and achieve end-effector force tracking on a flat surface while the aerial manipulator maintains a neutral attitude. This greatly increases the stability and safety of contact-based operations, as the vehicle does not require changes in attitude to meet the force requirements. Note to Practitioners—This paper was motivated by a surge of different configurations of aerial vehicles emerging. A modeling method for generalized aerial vehicles is presented to determine which vehicle configurations are best suited for a specific application. This approach considers vehicles with additional actuators such as tilting propellers and robotic manipulators. The effectiveness of the proposed method is validated on a custom-designed quadrotor with four moving masses emulating centroid variation of a generic robotic manipulator. This original design is augmented with tilting propellers, yielding a truly generalized experimental platform. The generalized modeling algorithm and the experimental platform are tested both in free flight and in a scenario that demonstrates applying the desired force to a flat surface. Overall, we showcase the proposed generalized modeling is a useful tool for the rapid prototyping and design of aerial manipulators, paving the way for more advanced and capable systems in the future. With this approach, practitioners can gain a better understanding of the potential and limitations of their aerial vehicles, and ultimately improve their performance and effectiveness in specific applications.

This paper presents a novel approach for UAV battery management of the energy-aware mapping mission planning. The optimization strategy aims to reduce overall flight time by considering necessary battery replacements. Energy constraints are imposed as the capacities of available batteries are limited. The resulting mission plan reduces the unnecessary long flight distances from and to the base station. This strategy also considers the choice of the base location. Potential base locations are fixed and placed at the vertices of the mapping area for easy access. Iterative optimization results in choosing the location with the minimal total mission flight distance. The optimal solution of the proposed approach is compared to its initial solution, where the mapping area is decomposed proportionally to the capacities of batteries planned to be used in the mission. Simulation results for the realistic test case mapping area of the vineyard and olive orchard show that fewer battery replacements are needed and both total flight time and total flight distance are reduced when implementing the proposed optimization strategy. Moreover, mission safety is increased as the number of take-offs and landings, which pose high risk, are reduced.

This paper presents a comparative performance evaluation of three different teleoperation interfaces for very low weight (<3 kg) anthropomorphic dual arms intended to conduct complex manipulation tasks involving a certain level of dexterity, accuracy and agility, either in ground service or in aerial manipulation applications. A visual human pose estimation system is developed to obtain the Cartesian and joint values of the user, which are mapped to the corresponding pose of the dual arm manipulator exploiting the equivalent human-robot kinematics. A leader-follower scheme is also presented, using a reduced scale dual arm that can replicate directly the joint positions of the leader arms to the follower arms. A 6-DOF (degrees of freedom) joystick is proposed to generate linear motions more accurately. A total of 60 ground tests were conducted involving 10 participants to determine the accuracy and time performance in two benchmarks (box edges and S contour tracking). Finally, the visual and leader-follower interfaces were evaluated with the dual arm aerial manipulator on flight tests, reporting several findings derived from the system evaluation.

This paper presents a manipulator-equipped unmanned aerial vehicle (UAV) performing contact-based surface inspection. Using stereo camera information, a contact point is determined and an approach path is generated to ensure an autonomous workflow. The proposed inspection method uses an impedance controller to regulate the force applied by the end-effector to the contact surface. Due to the often adverse conditions in the real world, the aerial manipulator may fail to achieve contact after approaching the surface. Therefore, the impedance controller is coupled with an online stiffness estimation algorithm to increase the overall robustness of the inspection system and achieve stable surface contact. Finally, simulations in the Gazebo environment and experiments in the real world are performed to show the feasibility of this approach.

This paper demonstrates the experimental validation of canonical peg-in-hole manipulation task using an aerial robot. The robot consists of a multirotor platform equipped with a dual arm multi degree of freedom manipulator. The paper discusses the introduced kinematic constraints which make sure the robot holds a bolt with both arms. We build our peg-in-hole approach using impedance control which is the foundation of compliant interaction with the environment. We utilize a finite state automaton to plan a multi stage strategy which relies on tactile perception in order to pin point the target. Finally, the whole body locomotion is considered, meaning both the degrees of freedom of multirotor base and the dual arm manipulator are considered.

In this paper, we present selected benchmark aerial manipulation tasks using an aerial vehicle endowed with multi-degree of freedom manipulators. The proposed tasks analyze environmental coupling and are broken into three general categories: momentary, loose, and strong coupling. A classical control structure is derived, tuned, and verified through experiments, conducted for benchmarking purposes to include pick-and-place, insertion, and valve-turning tasks. Although other nonlinear controllers may prove more effective, the classical control approach has been selected in order to analyze contact stability and provide benchmark results for future reference. An analysis of system stability is conducted and implemented into the controller. A vision-based high-level controller fuses motion tracking data in order to provide control of both the aircraft and the manipulators, allowing the system to become coupled to the environment and perform the required operation. We present recent results validating our framework using the proposed aircraft-arm system.

In this paper we present a study of a robotic system that consists of an unmanned aerial vehicle equipped with a pair of manipulator arms (MMUAV), and unmanned ground vehicles (UGVs). The envisioned application scenario includes autonomous packet transportation, where MMUAV is used for picking/placing packets, while both MMUAV and UGV can be used for packet transportation, with different energy consumption profiles. We propose a reactive method for decentralized task planning and coordination of robots using hierarchical task decomposition based on TÆMS framework. Our approach takes into account low-level motion-planning aspects of the system as well as high-level mission specification, making this a multi-layered system. For low-level planning we use sampling-based planner combined with obstacle-free trajectory generation. Methods are verified in simulations and on an experimental testbed, using 3D Robotics quadcopter and Pioneer 3DX mobile robots with the results showing stability and robustness of the presented methods.

This paper presents the results of our efforts to build a heterogeneous robotic system capable of executing complex disaster response and recovery tasks. We aim to explore high level task scheduling and mission planning algorithms that enable various types of robots to cooperate together, utilizing each others strengths to yield a symbiotic robotic system. In the proposed scenario, a ground vehicle and an aerial robot work together to close a valve in a disaster stricken industrial environment. To that end we use TAEMS framework in order to specify interrelationships between mission subtasks and develop an effective scheduling and coordination mechanism, inspired by Generalized Partial Global Planning. We present simulation results with two different outcomes that show cooperative capabilities of the system.

We propose a framework for valve turning using an aerial vehicle endowed with dual multi-degree of freedom manipulators. A tightly integrated control scheme between the aircraft and manipulators is mandated for tasks requiring aircraft to environmental coupling. Feature detection is well-established for both ground and aerial vehicles and facilitates valve detection and arm tracking. Force feedback upon contact with the environment provides compliant motions in the presence of position error and coupling with the valve. We present recent results validating the valve turning framework using the proposed aircraft-arm system during flight tests.

This paper presents a control scheme to achieve dynamic stability in an aerial vehicle with dual multi-degree of freedom manipulators. Arm movements assist with stability and recovery for ground robots, in particular humanoids and dynamically balancing vehicles. However, there is little work in aerial robotics where the manipulators themselves facilitate flight stability or the load mass is repositioned in flight for added control. We present recent results in arm motions that achieve increased flight stability without and with different load masses attached to the end-effectors. Our test flight results indicate that we can accurately model and control our aerial vehicle when both moving the manipulators and interacting with target objects.

Ongoing efforts toward mobile manipulation from an aerial vehicle are presented. Recent tests and results from a prototype rotorcraft have shown that our hybrid structure increases stability during flight and manipulation. Since UAVs require significant setup time, suitable testing locations, and have tendencies to crash, we developed an aerial manipulation test and evaluation environment that provides controllable and repeatable experiments. By using force feedback techniques, we have designed multiple, dexterous, redundant manipulators that can grasp objects such as tools and small objects. These manipulators are controlled in concert with an emulated aerial platform to provide hovering stability. The manipulator and aircraft flight control are tightly coupled to facilitate grasping without large perturbations in the end-effector.

In this article we present a mathematical model of a spincopter - an inherently stable and easy to control unmanned aerial vehicle (UAV). Although the devised model represents only the first approximation of spincopter's physics, it captures basic phenomena that are sufficient for derivation and testing of a simple control algorithm. Results obtained by simulations in Simulink®, presented herein, demonstrate efficient performance of the proposed simple controller structure. Further tests, including investigation of complex aerodynamics effects and hardware-specified limitations, have been done using X-Plane®software package. Finally, the proposed controller has been tested experimentally. Both, X-Plane®and experimental results confirmed the outcome from Simulink®.