Tactile sensing in dexterous robot hands - Review

This review article analyzes state-of-the-art and future perspectives for harvesting robots in high-value crops. The objectives were to characterize the crop environment relevant for robotic harvesting, to perform a literature review on the state-of-the-art of harvesting robots using quantitative measures, and to reflect on the crop environment and literature review to formulate challenges and directions for future research and development. Harvesting robots were reviewed regarding the crop harvested in a production environment, performance indicators, design process techniques used, hardware design decisions, and algorithm characteristics. On average, localization success was 85%, detachment success was 75%, harvest success was 66%, fruit damage was 5%, peduncle damage was 45%, and cycle time was 33 s. A kiwi harvesting robot achieved the shortest cycle time of 1 s. Moreover, the performance of harvesting robots did not improve in the past three decades, and none of these 50 robots was commercialized. Four future challenges with R&D directions were identified to realize a positive trend in performance and to successfully implement harvesting robots in practice: 1 simplifying the task, 2 enhancing the robot, 3 defining requirements and measuring performance, and 4 considering additional requirements for successful implementation. This review article may provide new directions for future automation projects in high-value crops.

Harvesting Robots for High-value Crops: State-of-the-art Review and Challenges Ahead

Autonomous service robots will have to understand vaguely described tasks, such as âset the tableâ or âclean upâ. Performing such tasks as intended requires robots to fully, precisely, and appropriately parameterize their low-level control programs. We propose knowledge processing as a computational resource for enabling robots to bridge the gap between vague task descriptions and the detailed information needed to actually perform those tasks in the intended way. In this article, we introduce the KnowRob knowledge processing system that is specifically designed to provide autonomous robots with the knowledge needed for performing everyday manipulation tasks. The system allows the realization of âvirtual knowledge basesâ: collections of knowledge pieces that are not explicitly represented but computed on demand from the robot's internal data structures, its perception system, or external sources of information. This article gives an overview of the different kinds of knowledge, the different inference mechanisms, and interfaces for acquiring knowledge from external sources, such as the robot's perception system, observations of human activities, Web sites on the Internet, as well as Web-based knowledge bases for information exchange between robots. We evaluate the system's scalability and present different integrated experiments that show its versatility and comprehensiveness.

KnowRob: A knowledge processing infrastructure for cognition-enabled robots

As autonomous personal robots come of age, we expect certain applications to be executed with a high degree of repeatability and robustness. In order to explore these applications and their challenges, we need tools and strategies that allow us to develop them rapidly. Serving drinks (i.e., locating, fetching, and delivering), is one such application with well-defined environments for operation, requirements for human interfacing, and metrics for successful completion. In this paper we present our experiences and results while building an autonomous robotic assistant using the PR21 platform and ROS2. The system integrates several new components that are built on top of the PR2's current capabilities. Perception components include dynamic obstacle identification, mechanisms for identifying the refrigerator, types of drinks, and human faces. Planning components include navigation, arm motion planning with goal and path constraints, and grasping modules. One of the main contributions of this paper is a new task-level executive system, SMACH, based on hierarchical concurrent state machines, which controls the overall behavior of the system. We provide in-depth discussions on the solutions that we found in accomplishing our goal, and the implementation strategies that let us achieve them.

Towards autonomous robotic butlers: Lessons learned with the PR2

https://cyberleninka.org/article/n/926024.pdf

The Impacts of Robotics, Artificial Intelligence On Business and Economics

Robotic Visions to 2020 and beyond -- The strategic research agenda for robotics in Europe

In the past, the process of developing a new robot application has had more of the design of a piece of artwork or of an act of ingenious engineering than of a structured and formalized process. The prime objective of BRICS is to structure and formalize the robot development process itself and to provide tools, models, and functional libraries, which allow reducing the development time by a magnitude. BRICS is working together with academic as well as industrial providers of robotics “components” (hardware and software), to identify and document best practices in the development of complex robotics systems, to refactor (together) the existing components in order to achieve a much higher level of reusability and robustness, and to support the robot development process with a structured tool chain and code repository. BRICS is a joint research project funded by the European Commission ICT Challenge 2 under grant number 231940. First results include the analysis of existing robot development processes, the first steps towards harmonizing robot con-
trol interfaces and component models and the set-up of robot systems for best practice analyses.

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BRICS - Best practice in robotics

In the "Industrial Activities" column of the September 2009 issue of IEEE Robotics & Automation Magazine, recent world-wide road-mapping activities were compared with a focus such that the current European initiatives could only very briefly be described. This column is geared toward describing these activities in more detail.

The Strategic Research Agenda for Robotics in Europe

The Strategic Research Agenda for Robotics in Europe [Industrial Activities]

This paper presents a novel approach for safeguarding a mobile manipulator. To overcome the shortcomings of state-of-the-art safeguarding techniques, we present a dynamic approach capable of covering different situations and adapting itself to the situation at hand. The proposed approach is based on the continuous dynamic update of a safety field using both exteroceptive and proprioceptive data. The proprioceptive data are the platform velocity vector and the manipulator position; the exteroceptive data refers to the environment in which the mobile manipulator is moving, namely distances to obstacles acquired by laser scanners. The effectiveness of the proposed approach has been validated in an industrial test case scenario in which the robot had to execute a fetch and carry task while safely coexisting with human workers. Experimental results proved that the use of dynamic protection fields allowed the robot to carry out its tasks with a significant reduction (about 50%) of the execution time.

Tim Guhl

Papers

Robotic Visions to 2020 and beyond -- The strategic research agenda for robotics in Europe

BRICS - Best practice in robotics

The Strategic Research Agenda for Robotics in Europe

The Strategic Research Agenda for Robotics in Europe [Industrial Activities]

Safeguarding a mobile manipulator using dynamic safety fields