scholarly journals Applied Robot Coverage Path Planning with Multiple Decision Making Capability under Uncertainty using Knowledge Inference with Hedge Algebras

2018 ◽  
Author(s):  
Hai Van Pham ◽  
Philip Moore
Keyword(s):  
Decision Making ◽  
Path Planning ◽  
Optimal Path ◽  
Self Awareness ◽  
Conceptual Approach ◽  
High Coverage ◽  
Coverage Path Planning ◽  
Knowledge Reasoning ◽  
Cleaning Robot ◽  
Context Of Use

Robotic decision-support systems must facilitate a robots interactions with their environment, this demands adaptability. Adaptability relates to awareness of the environment and `self-awareness', human behaviour exemplifies the concept of awareness to arrive at an optimal choice of action or decision based on reasoning and inference with learned preferences. A similar conceptual approach is required to implement awareness in autonomous robotic systems which must adapt to the current dynamic environment (the context of use). By incorporating `self-awareness' with knowledge of a Robot's preferences (in decision making) the decision maker interface should adapt to the current context of use. This paper proposes a novel approach to enable an autonomous robotics which implements path planning combining adaptation with knowledge reasoning techniques and hedge algebra to enable an autonomous robot to realise optimal coverage path planning under dynamic uncertainty. The results for a cleaning robot show that using our proposed approach demonstrated the capability to avoid both static and dynamic obstacles while achieving optimal path planning with increased efficiency. The proposed approach achieves the multiple decision-making objectives (path planning) with a high-coverage and low repetition rates. Compared to other current approaches, the proposed approach has demonstrated improved performance over the conventional robot control algorithms.

scholarly journals Hybrid Spiral STC-Hedge Algebras Model in Knowledge Reasonings for Robot Coverage Path Planning and Its Applications

2019 ◽  
Vol 9 (9) ◽  
pp. 1909 ◽  
Author(s):  
Hai Van Pham ◽  
Farzin Asadi ◽  
Nurettin Abut ◽  
Ismet Kandilli
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Research Effort ◽  
Dynamic Environments ◽  
Coverage Path Planning ◽  
Knowledge Reasoning ◽  
Cleaning Robot ◽  
Novel Approach ◽  
Proposed Model ◽  
Tree Coverage

Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.

scholarly journals Robot Coverage Path Planning under Uncertainty Using Knowledge Inference and Hedge Algebras

Machines ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 46 ◽  
Author(s):  
Hai Van Pham ◽  
Philip Moore
Keyword(s):  
Path Planning ◽  
Environmental Awareness ◽  
Robotic Systems ◽  
Self Awareness ◽  
Coverage Path Planning ◽  
Knowledge Reasoning ◽  
Operating Environments ◽  
Dynamic Operating ◽  
Planning Algorithms ◽  
Autonomous Robotic Systems

Human behaviour demonstrates environmental awareness and self-awareness which is used to arrive at decisions and actions or reach conclusions based on reasoning and inference. Environmental awareness and self-awareness are traits which autonomous robotic systems must have to effectively plan an optimal route and operate in dynamic operating environments. This paper proposes a novel approach to enable autonomous robotic systems to achieve efficient coverage path planning, which combines adaptation with knowledge reasoning techniques and hedge algebras to achieve optimal coverage path planning in multiple decision-making under dynamic operating environments. To evaluate the proposed approach we have implemented it in a mobile cleaning robot. The results demonstrate the ability to avoid static and dynamic (moving) obstacles while achieving efficient coverage path planning with low repetition rates. While alternative current coverage path planning algorithms have achieved acceptable results, our reported results have demonstrated a significant performance improvement over the alternative coverage path planning algorithms.

scholarly journals Modified A-Star Algorithm for Efficient Coverage Path Planning in Tetris Inspired Self-Reconfigurable Robot with Integrated Laser Sensor

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2585 ◽  
Author(s):  
Anh Le ◽  
Veerajagadheswar Prabakaran ◽  
Vinu Sivanantham ◽  
Rajesh Mohan
Keyword(s):  
Path Planning ◽  
Laser Sensor ◽  
Coverage Area ◽  
Operation System ◽  
Coverage Path Planning ◽  
Cleaning Robot ◽  
Simulated Environment ◽  
Planning Approach ◽  
Reconfigurable Robot ◽  
Set Up

Advancing an efficient coverage path planning in robots set up for application such as cleaning, painting and mining are becoming more crucial. Such drive in the coverage path planning field proposes numerous techniques over the past few decades. However, the proposed approaches were only applied and tested with a fixed morphological robot in which the coverage performance was significantly degraded in a complex environment. To this end, an A-star based zigzag global planner for a novel self-reconfigurable Tetris inspired cleaning robot (hTetro) presented in this paper. Unlike the traditional A-star algorithm, the presented approach can generate waypoints in order to cover the narrow spaces while assuming appropriate morphology of the hTtero robot with the objective of maximizing the coverage area. We validated the efficiency of the proposed planning approach in the Robot Operation System (ROS) Based simulated environment and tested with the hTetro robot in real-time under the controlled scenarios. Our experiments demonstrate the efficiency of the proposed coverage path planning approach resulting in superior area coverage performance in all considered experimental scenarios.

scholarly journals Proposed Smooth-STC Algorithm for Enhanced Coverage Path Planning Performance in Mobile Robot Applications

Robotics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 44 ◽  
Author(s):  
Hai Van Pham ◽  
Philip Moore ◽  
Dinh Xuan Truong
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Forest Monitoring ◽  
Planning Problem ◽  
Operational Environment ◽  
Uncertain Environments ◽  
Coverage Path Planning ◽  
Agricultural Robots ◽  
Cleaning Robots ◽  
Path Planning Problem

Robotic path planning is a field of research which is gaining traction given the broad domains of interest to which path planning is an important systemic requirement. The aim of path planning is to optimise the efficacy of robotic movement in a defined operational environment. For example, robots have been employed in many domains including: Cleaning robots (such as vacuum cleaners), automated paint spraying robots, window cleaning robots, forest monitoring robots, and agricultural robots (often driven using satellite and geostationary positional satellite data). Additionally, mobile robotic systems have been utilised in disaster areas and locations hazardous to humans (such as war zones in mine clearance). The coverage path planning problem describes an approach which is designed to determine the path that traverses all points in a defined operational environment while avoiding static and dynamic (moving) obstacles. In this paper we present our proposed Smooth-STC model, the aim of the model being to identify an optimal path, avoid all obstacles, prevent (or at least minimise) backtracking, and maximise the coverage in any defined operational environment. The experimental results in a simulation show that, in uncertain environments, our proposed smooth STC method achieves an almost absolute coverage rate and demonstrates improvement when measured against alternative conventional algorithms.

Coverage Path Planning for Cleaning Robot Adapted to Biased Dirt Distribution

2016 ◽  
Vol 2016 (0) ◽  
pp. 1A1-13b4 ◽  
Author(s):  
Takahiro SASAKI ◽  
Guillemo ENRIQUEZ ◽  
Takanobu MIWA ◽  
Shuji HASHIMOTO
Keyword(s):  
Path Planning ◽  
Coverage Path Planning ◽  
Cleaning Robot

scholarly journals Motion planner for a Tetris-inspired reconfigurable floor cleaning robot

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091444 ◽  
Author(s):  
Prabakaran Veerajagadheswar ◽  
Ku Ping-Cheng ◽  
Mohan Rajesh Elara ◽  
Anh Vu Le ◽  
Masami Iwase
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Robotic Systems ◽  
Planning Techniques ◽  
Essential Ingredient ◽  
Coverage Path Planning ◽  
Cleaning Robot ◽  
Simulated Environment ◽  
Navigation Path ◽  
Planning Technique

Coverage path planning technique is an essential ingredient in every floor cleaning robotic systems. Even though numerous approaches demonstrate the benefits of conventional coverage motion planning techniques, they are mostly limited to fixed morphological platforms. In this article, we put forward a novel motion planning technique for a Tetris-inspired reconfigurable floor cleaning robot named “hTetro” that can reconfigure its morphology to any of the seven one-sided Tetris pieces. The proposed motion planning technique adapts polyomino tiling theory to tile a defined space, generates reference coordinates, and produces a navigation path to traverse on the generated tile-set with an objective of maximizing the area coverage. We have summarized all these aspects and concluded with experiments in a simulated environment that benchmarks the proposed technique with conventional approaches. The results show that the proposed motion planning technique achieves significantly higher performance in terms of area recovered than the traditional methods.

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