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 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 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.

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 LEO: Liquid Exploration Online

2020 ◽  
Vol 2 (1) ◽  
pp. 58-80
Author(s):  
Frank Hoeller
Keyword(s):  
Path Planning ◽  
State Of The Art ◽  
Simulation Experiments ◽  
Coverage Path Planning ◽  
Current State ◽  
Extensive Evaluation ◽  
Novel Approach ◽  
Local Navigation ◽  
Local Decision ◽  
Real Robot

This article introduces a novel approach to the online complete- coverage path planning (CCPP) problem that is specically tailored to the needs of skid-steer tracked robots. In contrast to most of the current state-of-the-art algorithms for this task, the proposed algorithm reduces the number of turning maneuvers, which are responsible for a large part of the robot's energy consumption. Nevertheless, the approach still keeps the total distance traveled at a competitive level. The algorithm operates on a grid-based environment representation and uses a 3x3 prioritization matrix for local navigation decisions. This matrix prioritizes cardinal di- rections leading to a preference for straight motions. In case no progress can be achieved based on a local decision, global path planning is used to choose a path to the closest known unvisited cell, thereby guaranteeing completeness of the approach. In an extensive evaluation using simulation experiments, we show that the new algorithm indeed generates competi- tively short paths with largely reduced turning costs, compared to other state-of-the-art CCPP algorithms. We also illustrate its performance on a real robot.

Complete Coverage Path Planning and Obstacle Avoidance Strategy of the Robot

2013 ◽  
Vol 756-759 ◽  
pp. 497-503 ◽  
Author(s):  
Jun Hui Wu ◽  
Tong Di Qin ◽  
Jie Chen ◽  
Hui Ping Si ◽  
Kai Yan Lin ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
High Efficiency ◽  
Control Flow ◽  
Single Chip ◽  
Single Chip Microcomputer ◽  
Complete Coverage ◽  
Coverage Path Planning ◽  
Avoidance Strategy ◽  
Traversal Algorithm

In order to solve the problems of complete coverage path and obstacle avoidance with the mobile robot, the complete coverage planning was described first, and then the algorithm of the complete coverage path planning was analyzed. The complete traversal algorithm and the obstacle avoidance strategy of the robot around the barrier were put forward. Finally, the traversal control flow chart of the traversal robot implemented in Single Chip Microcomputer (SCM) was obtained. After the above analysis, the algorithm was simple, practical, and low repeatability, and high efficiency. The algorithms could effectively solve the difficulty of complete coverage path and obstacle avoidance with the robot.

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