The Second Stage Experiments on Long Term Activity of Autonomous Mobile Robots - Repetitive Navigation for One Week in a Corridor

2002 ◽  
Vol 14 (4) ◽  
pp. 375-381
Author(s):  
Yasushi Hada ◽  
◽  
Shin'ichi Yuta
Keyword(s):  
Mobile Robots ◽  
Experimental System ◽  
Autonomous Mobile Robots ◽  
Second Stage ◽  
Real Robot

Our goal is to enhance the autonomy of mobile robots, which must perform meaningful tasks for a long-term with regular maintenance at intervals of a week or month. Since we started this research, we recognize not only complexity but duration as indications of autonomy, which we call ""Long Term Activity"". We are studying such autonomy using an experimental robotics approach, which constructs a real robot and develops required technologies. Our experimental system, still in work, navigates a corridor environment autonomously for one week. In this paper, we present the system and some results of experiments.

scholarly journals The Quest For "Always-On" Autonomous Mobile Robots

2019 ◽  
Author(s):  
Joydeep Biswas
Keyword(s):  
Mobile Robots ◽  
State Machine ◽  
Autonomous Mobile Robots ◽  
Human In The Loop ◽  
The World ◽  
Feature Based ◽  
Markov Localization

Building ``always-on'' robots to be deployed over extended periods of time in real human environments is challenging for several reasons. Some fundamental questions that arise in the process include: 1) How can the robot reconcile unexpected differences between its observations and its outdated map of the world? 2) How can we scalably test robots for long-term autonomy? 3) Can a robot learn to predict its own failures, and their corresponding causes? 4) When the robot fails and is unable to recover autonomously, can it utilize partially specified, approximate human corrections to overcome its failures? We summarize our research towards addressing all of these questions. We present 1) Episodic non-Markov Localization to maintain the belief of the robot's location while explicitly reasoning about unmapped observations; 2) a 1,000km challenge to test for long-term autonomy; 3) feature-based and learning-based approaches to predicting failures; and 4) human-in-the-loop SLAM to overcome robot mapping errors, and SMT-based robot transition repair to overcome state machine failures.

scholarly journals An Adaptive Memory Model for Long-Term Navigation of Autonomous Mobile Robots

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
M. Hentschel ◽  
B. Wagner
Keyword(s):  
Mobile Robots ◽  
Indoor Environment ◽  
Human Memory ◽  
Autonomous Mobile Robots ◽  
Memory Model ◽  
Adaptive Memory ◽  
Variant Information ◽  
Environmental Representation ◽  
Over Time

This paper introduces an environmental representation for autonomous mobile robots that continuously adapts over time. The presented approach is inspired by human memory information processing and stores the current as well as past knowledge of the environment. In this paper, the memory model is applied to time-variant information about obstacles and driveable routes in the workspace of the autonomous robot and used for solving the navigation cycle of the robot. This includes localization and path planning as well as vehicle control. The presented approach is evaluated in a real-world experiment within changing indoor environment. The results show that the environmental representation is stable, improves its quality over time, and adapts to changes.

A PSO-Inspired Multi-Robot Map Exploration Algorithm Using Frontier-Based Strategy

2013 ◽  
Vol 2 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Yi Zhou ◽  
Kai Xiao ◽  
Yiheng Wang ◽  
Alei Liang ◽  
Aboul Ella Hassanien
Keyword(s):  
Mobile Robots ◽  
Distributed Algorithm ◽  
Fundamental Problem ◽  
Local Area ◽  
Autonomous Mobile Robots ◽  
Swarm Optimization ◽  
Exploration Time ◽  
Real Robot ◽  
Exploration Task ◽  
Unknown Region

Map exploration is a fundamental problem in mobile robots. This paper presents a distributed algorithm that coordinates a team of autonomous mobile robots to explore an unknown environment. The proposed strategy is based on frontiers which are the regions on the boundary between open and unexplored space. With this strategy, robots are guided to move constantly to the nearest frontier to reduce the size of unknown region. Based on the Particle Swarm Optimization (PSO) model incorporated in the algorithm, robots are navigated towards remote frontier after exploring the local area. The exploration completes when there is no frontier cell in the environment. The experiments implemented on both simulated and real robot scenarios show that the proposed algorithm is capable of completing the exploration task. Compared to the conventional method of randomly selecting frontier, the proposed algorithm proves its efficiency by the decreased 60% exploration time at least. Additional experimental results show the decreased coverage time when the number of robots increases, which further suggests the validity, efficiency and scalability.

scholarly journals Adapting the definition of multimorbidity – development of a locality-based consensus for selecting included Long Term Conditions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Nasrin Hafezparast ◽  
Ellie Bragan Turner ◽  
Rupert Dunbar-Rees ◽  
Alice Vodden ◽  
Hiten Dodhia ◽  
...  
Keyword(s):  
Literature Review ◽  
Selection Process ◽  
Final Decision ◽  
Local Health ◽  
Urban Context ◽  
Long Term Conditions ◽  
Large Cities ◽  
Second Stage ◽  
Local Health Service

Abstract Background Defining multimorbidity has proved elusive in spite of attempts to standardise definitions. For national studies, a broad definition is required to capture national diversity. For locally based studies, the definition may need to reflect demographic and morbidity patterns. We aimed to define multimorbidity for an inner city, multi-ethnic, deprived, young age community typical of many large cities. Methods We used a scoping literature review to identify the international literature, standards and guidelines on Long Term Condition (LTC) definitions for inclusion in our multimorbidity definition. Consensus was categorised into high, medium or low consensus, depending on the number of literature sources citing each LTC. Findings were presented to a workshop consisting of local health service stakeholders who were asked to select LTCs for inclusion in a second stage review. In the second stage, each LTC was tested against seven evaluation domains: prevalence, impact, preventability, treatment burden, progression to multiple LTCs, impact on younger people, data quality. These domains were used to create 12 target criteria. LTC rankings according to consensus group and target criteria scores were presented to a second workshop for a final decision about LTC inclusion. Results The literature review identified 18 literature sources citing 86 LTCs: 11 were excluded because they were LTC clusters. The remainder were allocated into consensus groupings: 13 LTCs were ‘high consensus’ (cited by ≥ 11 sources); 15 were ‘medium consensus’ (cited by 5–10 sources); 47 were ‘low consensus’ (cited by < 5 sources). The first workshop excluded 31 LTCs. The remaining 44 LTCs consisted of: 13 high consensus LTCs, all with high target score (score 6–12); 15 medium consensus LTCs, 11 with high target scores; 16 low consensus LTCs, 6 with high target scores. The final workshop selected the 12 high consensus conditions, 12 medium consensus LTCs (10 with high target scores) and 8 low consensus LTCs (3 with high target scores), producing a final selection of 32 LTCs. Conclusions Redefining multimorbidity for an urban context ensures local relevance but may diminish national generalisability. We describe a detailed LTC selection process which should be generalisable to other contexts, both local and national.

scholarly journals Determining Shape and Size of Personal Space of a Human when Passed by a Robot

2021 ◽  
Author(s):  
Margot M. E. Neggers ◽  
Raymond H. Cuijpers ◽  
Peter A. M. Ruijten ◽  
Wijnand A. IJsselsteijn
Keyword(s):  
Mobile Robots ◽  
Humanoid Robot ◽  
Personal Space ◽  
Human Robot Interaction ◽  
Autonomous Mobile Robots ◽  
Robot Interaction ◽  
Human Comfort ◽  
Navigation Algorithms ◽  
Insight Into ◽  
Shape And Size

AbstractAutonomous mobile robots that operate in environments with people are expected to be able to deal with human proxemics and social distances. Previous research investigated how robots can approach persons or how to implement human-aware navigation algorithms. However, experimental research on how robots can avoid a person in a comfortable way is largely missing. The aim of the current work is to experimentally determine the shape and size of personal space of a human passed by a robot. In two studies, both a humanoid as well as a non-humanoid robot were used to pass a person at different sides and distances, after which they were asked to rate their perceived comfort. As expected, perceived comfort increases with distance. However, the shape was not circular: passing at the back of a person is more uncomfortable compared to passing at the front, especially in the case of the humanoid robot. These results give us more insight into the shape and size of personal space in human–robot interaction. Furthermore, they can serve as necessary input to human-aware navigation algorithms for autonomous mobile robots in which human comfort is traded off with efficiency goals.

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