Multimodal sensing and active continuous closed‐loop feedback for achieving reliable manipulation in the outdoor physical world

Abstract

The use of field robots can greatly decrease the amount of time, effort, and associated risk compared to if human workers were to carryout certain tasks such as disaster response. However, transportability and reliability remain two main issues for most current robot systems. To address the issue of transportability, we have developed a lightweight modularizable platform named AeroArm. To address the issue of reliability, we utilize a multimodal sensing approach, combining the use of multiple sensors and sensor types, and the use of different detection algorithms, as well as active continuous closed‐loop feedback to accurately estimate the state of the robot with respect to the environment. We used Challenge 2 of the 2017 Mohammed Bin Zayed International Robotics Competition as an example outdoor manipulation task, demonstrating the capabilities of our robot system and approach in achieving reliable performance in the fields, and ranked fifth place internationally in the competition.

Design and development of drones to autonomously interact with objects in unstructured outdoor scenarios

“…As for this, we analyzed the articles published by the participants of the 2017 MBZIRC: interesting elements were presented in Beul et al (2019) and Spurný et al (2019) concerning hardware settings, navigation strategies based on state machines (Bähnemann et al, 2019;Tzoumanikas et al, 2019), as well as some potential issues typical of vision algorithms (Chan et al, 2019).…”

“…Such a design allows separating the different operations the drone needs to perform (e.g., takeoff, tracking, landing) so that each activity can be tested independently without being affected by side effects due to reciprocal unknown relationships. For this application, the State Machine is implemented with the SMACH module (SMACH, 2019) in the Robot Operating System platform (ROS), an open-source set of software libraries and tools created for robotic applications (Koubaa, 2017) and employed in previous similar works (Chan et al, 2019;Spurný et al, 2019). The HLC provides the flight commands and manages the procedural state of the robotic platform, using camera data, which are elaborated by Recognition Threads (RT), and information from the drone sensors and motors managed by the Low Level Controller (LLC) implemented in the FCB Pixhawk 1 through the open-source ArduCopter software -V3.6.12 Quad (ArduPilot, 2019).…”

Self-protective motion planning for mobile manipulators in a dynamic door-closing workspace

“…Unless dealt with in a proper way, they would deteriorate the performances of the following operations and even give rise to inconsistent task results, which leads to mission failure. It is very true, especially in the outdoor area (Chan et al, 2019); the sudden wind can also cause the unlocked door leaf untimely close during the robot occupied by the current task. For involving task such as opening the door to get handwork inside (Rühr et al, 2012), which generally limits the robot task space and keeps the robot in the unlocked door leaf's adverse influence range for a long time, we cannot ignore the uncertain disturbances (Kim, 2019) coming from the unlocked door leaf leading to a potential risk of collision damages.…”

Towards a balancing safety against performance approach in human–robot co-manipulation for door-closing emergencies

“…The aforementioned control and planning technologies are applied to a typical workspace released by door-opening [29,30], which has also received abundant attention during the last decades. When door-opening actions work in practice [31], the opened door matches external disturbances, such as uncontrolled rotational inertia to get closing trends, are unavoidably encountered. Unless dealt with in a proper way, they would deteriorate the performances of the following operations and even give rise to inconsistent task results, which leads to mission failure.…”