Dynamic coverage path planning and obstacle avoidance for cleaning robot based on behavior

2011 ◽  
Author(s):  
Liang Yuming ◽  
Wen Ruchun ◽  
Zhang Zhenli ◽  
Zhu Junlin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Coverage Path Planning ◽  
Cleaning Robot

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 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

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.

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.

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 Realization Energy Optimization of Complete Path Planning in Differential Drive Based Self-Reconfigurable Floor Cleaning Robot

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1136 ◽  
Author(s):  
Anh Le ◽  
Ping-Cheng Ku ◽  
Thein Than Tun ◽  
Nguyen Huu Khanh Nhan ◽  
Yuyao Shi ◽  
...  
Keyword(s):  
Path Planning ◽  
Complete Coverage ◽  
Energy Aware ◽  
Coverage Path Planning ◽  
Differential Drive ◽  
Reconfigurable Robots ◽  
Cleaning Robot ◽  
Sequencing Strategy ◽  
Planning Strategies ◽  
Four Blocks

The efficiency of energy usage applied to robots that implement autonomous duties such as floor cleaning depends crucially on the adopted path planning strategies. Energy-aware for complete coverage path planning (CCPP) in the reconfigurable robots raises interesting research, since the ability to change the robot’s shape needs the dynamic estimate energy model. In this paper, a CCPP for a predefined workspace by a new floor cleaning platform (hTetro) which can self-reconfigure among seven tetromino shape by the cooperation of hinge-based four blocks with independent differential drive modules is proposed. To this end, the energy consumption is represented by travel distances which consider operations of differential drive modules of the hTetro kinematic designs to fulfill the transformation, orientation correction and translation actions during robot navigation processes from source waypoint to destination waypoint. The optimal trajectory connecting all pairs of waypoints on the workspace is modeled and solved by evolutionary algorithms of TSP such as Genetic Algorithm (GA) and Ant Optimization Colony (AC) which are among the well-known optimization approaches of TSP. The evaluations across several conventional complete coverage algorithms to prove that TSP-based proposed method is a practical energy-aware navigation sequencing strategy that can be implemented to our hTetro robot in different real-time workspaces. Moreover, The CCPP framework with its modulation in this paper allows the convenient implementation on other polynomial-based reconfigurable robots.

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