<i>A high-level task�planning of autonomous robots carrying multiple herbicides and treating specific weed types</i>

2021 ◽  
Author(s):  
Georgios Adosoglou ◽  
Seonho Park ◽  
Yiannis Ampatzidis ◽  
Panos M. Pardalosa
Keyword(s):  
Autonomous Robots ◽  
Task Planning ◽  
High Level

scholarly journals The PETLON Algorithm to Plan Efficiently for Task-Level-Optimal Navigation

2020 ◽  
Vol 69 ◽  
pp. 471-500
Author(s):  
Shih-Yun Lo ◽  
Shiqi Zhang ◽  
Peter Stone
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Mobile Robots ◽  
Large Scale ◽  
Accurate Estimate ◽  
Indoor Environments ◽  
Task Planning ◽  
Trade Offs ◽  
High Level ◽  
Task Level

Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. In this process, mobile robots need the capabilities of both task and motion planning. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planning algorithms, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces. In this article, we focus on integrating task and motion planning (TMP) to achieve task-level-optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation), including two configurations with different trade-offs over computational expenses between task and motion planning, for everyday service tasks using a mobile robot. Experiments have been conducted both in simulation and on a mobile robot using object delivery tasks in an indoor office environment. The key observation from the results is that PETLON is more efficient than a baseline approach that pre-computes motion costs of all possible navigation actions, while still producing plans that are optimal at the task level. We provide results with two different task planning paradigms in the implementation of PETLON, and offer TMP practitioners guidelines for the selection of task planners from an engineering perspective.

scholarly journals RTPO: A Domain Knowledge Base for Robot Task Planning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1105 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Chen
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Task Planning ◽  
Specific Domain ◽  
Goal State ◽  
Ontology Language ◽  
Planning Algorithm ◽  
Domain Knowledge Base ◽  
Robot Task ◽  
High Level

Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.

Towards life-long adaptive agents: using metareasoning for combining knowledge-based planning with situated learning

2018 ◽  
Vol 33 ◽  
Author(s):  
Priyam Parashar ◽  
Ashok K. Goel ◽  
Bradley Sheneman ◽  
Henrik I. Christensen
Keyword(s):  
Situated Learning ◽  
Intelligent Agents ◽  
Physical World ◽  
Task Planning ◽  
Adaptive Agents ◽  
Hierarchical Task Network ◽  
Knowledge Based ◽  
Knowledge Based Planning ◽  
Occupancy Grids ◽  
High Level

AbstractWe consider task planning for long-living intelligent agents situated in dynamic environments. Specifically, we address the problem of incomplete knowledge of the world due to the addition of new objects with unknown action models. We propose a multilayered agent architecture that uses meta-reasoning to control hierarchical task planning and situated learning, monitor expectations generated by a plan against world observations, forms goals and rewards for the situated reinforcement learner, and learns the missing planning knowledge relevant to the new objects. We use occupancy grids as a low-level representation for the high-level expectations to capture changes in the physical world due to the additional objects, and provide a similarity method for detecting discrepancies between the expectations and the observations at run-time; the meta-reasoner uses these discrepancies to formulate goals and rewards for the learner, and the learned policies are added to the hierarchical task network plan library for future re-use. We describe our experiments in the Minecraft and Gazebo microworlds to demonstrate the efficacy of the architecture and the technique for learning. We test our approach against an ablated reinforcement learning (RL) version, and our results indicate this form of expectation enhances the learning curve for RL while being more generic than propositional representations.

On the Problem of Moving Objects With Autonomous Robots: A Unifying High-Level Planning Approach

2016 ◽  
Vol 1 (1) ◽  
pp. 469-476 ◽  
Author(s):  
Hamal Marino ◽  
Mirko Ferrati ◽  
Alessandro Settimi ◽  
Carlos Rosales ◽  
Marco Gabiccini
Keyword(s):  
Moving Objects ◽  
Autonomous Robots ◽  
Planning Approach ◽  
High Level

scholarly journals Phronetic Ethics in Social Robotics: A New Approach to Building Ethical Robots

2020 ◽  
Vol 63 (1) ◽  
pp. 165-183
Author(s):  
Paweł Polak ◽  
Roman Krzanowski
Keyword(s):  
Autonomous Robots ◽  
Social Robotics ◽  
High Level Architecture ◽  
Ceteris Paribus ◽  
New Approach ◽  
Moral Agents ◽  
Current Design ◽  
Human Ethics ◽  
High Level

Abstract Social robotics are autonomous robots or Artificial Moral Agents (AMA), that will interact respect and embody human ethical values. However, the conceptual and practical problems of building such systems have not yet been resolved, playing a role of significant challenge for computational modeling. It seems that the lack of success in constructing robots, ceteris paribus, is due to the conceptual and algorithmic limitations of the current design of ethical robots. This paper proposes a new approach for developing ethical capacities in robotic systems, one based on the concept of Aristotelian phronesis. Phronesis in principle reflexes closer human ethics than the ethical paradigms we employ today in ethical robotics. This paper describes the essential features of phronesis and proposes a high-level architecture for implementing phronetic principles in autonomous robots. Phronetic robotics is in its early stages of conceptualization, so many of the presented ideas are speculative and require further research.2

scholarly journals RoboPlanner : a pragmatic task planning framework for autonomous robots

2020 ◽  
Vol 2 (1) ◽  
pp. 12-22
Author(s):  
Ajay Kattepur ◽  
Balamuralidhar Purushotaman
Keyword(s):  
Autonomous Robots ◽  
Task Planning ◽  
Planning Framework

The Pransky interview: Brennard Pierce: world class robotics designer and serial entrepreneur

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Joanne Pransky
Keyword(s):  
Humanoid Robot ◽  
Autonomous Robots ◽  
Industrial Robot ◽  
Biped Robot ◽  
Interdisciplinary Teams ◽  
Mobile Platforms ◽  
Content Type ◽  
Serial Entrepreneur ◽  
World Class ◽  
High Level

Purpose The purpose of this paper is to provide a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry engineer-turned entrepreneur regarding his pioneering efforts in starting robotic companies and commercializing technological inventions. The paper aims to discuss these issues. Design/methodology/approach The interviewee is Brennard Pierce, a world-class robotics designer and serial entrepreneur. Pierce is currently consulting in robotics after exiting from his latest startup as cofounder and chief robotics officer of Bear Robotics. Pierce discusses what led him to the field of robotics, the success of Bear Robotics, the challenges he’s faced and his future goals. Findings Pierce received a Bachelor of Science in computer science from Exeter University. He then founded his first startup, 5TWO, a custom software company. Always passionate about robotics as a hobby and now wanting to pursue the field professionally, he sold 5TWO to obtain a Master of Science, Robotics degree from the newly formed Bristol Robotics Lab (BRL) at Bristol University. After BRL, where he designed and built a biped robot that learned to walk using evolutionary algorithms, he joined the Robotics Research team at Carnegie Mellon University where he worked on a full-size humanoid robot for a large electronics company, designing and executing simple experiments for balancing. He then spent the next six years as a PhD candidate and robotics researcher at the Technical University Munich (TUM), Institute for Cognitive Science, where he built a compliant humanoid robot and a new generation of field programmable gate array-based robotic controllers. Afterwards, Pierce established the robotic startup Robotise in Munich to commercialize the omni-directional mobile platforms that he had developed at TUM. A couple of years later, Pierce left Robotise to cofound Bear Robotics, a Silicon Valley based company that brings autonomous robots to the restaurant industry. He remained at Bear Robotics for four years as chief robotics officer. He is presently a robotics consultant, waiting for post-COVID before beginning his next robotic startup. Originality/value Pierce is a seasoned roboticist and a successful entrepreneur. He has 15+ years’ of unique experience in both designing robotic hardware and writing low level embedded and high level cloud software. During his career he has founded three companies, managed small to middle sized interdisciplinary teams, and hired approximately 100 employees of all levels. Pierce’s robotic startup in Munich, Robotise, was solely based on his idea, design and implementation for an autonomous mobile delivery system. The third company he cofounded, Bear Robotics, successfully raised a $32m Series A funding lead by SoftBank. Bear Robotics is the recipient of the USA’s National Restaurant Association Kitchen Innovation Award; Fast Company’s World Changing Ideas Awards; and the Hospitality Innovation Planet 2020 Award.

Integration of Symbolic Task Planning into Operations within an Unstructured Environment

2012 ◽  
Vol 2 (3) ◽  
pp. 38-57 ◽  
Author(s):  
Renxi Qiu ◽  
Alexandre Noyvirt ◽  
Ze Ji ◽  
Anthony Soroka ◽  
Dayou Li ◽  
...  
Keyword(s):  
Real Time ◽  
Time Perception ◽  
Domain Knowledge ◽  
Task Planning ◽  
Task Execution ◽  
Unstructured Environments ◽  
Planning Approach ◽  
The World ◽  
Unstructured Environment ◽  
High Level

To ensure a robot capable of robust task execution in unstructured environments, task planners need to have a high-level understanding of the nature of the world, reasoning for deliberate actions, and reacting to environment changes. Proposed is a practical task planning approach that seamlessly integrating deeper domain knowledge, real time perception and symbolic planning for robot operation. A higher degree of autonomy under unstructured environment will be endowed to the robot with the proposed approach.

Sign in / Sign up

Export Citation Format

Share Document