Metaheuristic based navigation of a reconfigurable robot through narrow spaces with shape changing ability

Samarakoon, S. M. Bhagya P.; Muthugala, M. A. Viraj J.; Elara, Mohan Rajesh

doi:10.1016/j.eswa.2022.117060

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…Swarm algorithms and evolutionary algorithms are widely adopted for solving the optimal path planning algorithms with multiple constraints [ 40 , 41 , 42 , 43 , 44 ]. Whale optimization (WO) based path planning for an underwater robot is mentioned in [ 45 ], where the optimization techniques are used for generating a path with safe and minimal fuel consumption.…”

Section: Related Workmentioning

confidence: 99%

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

Pathmakumar

Muthugala

Samarakoon

et al. 2022

Sensors

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Robot-aided cleaning auditing is pioneering research that uses autonomous robots to assess a region’s cleanliness level by analyzing the dirt samples collected from various locations. Since the dirt sample gathering process is more challenging, adapting a coverage planning strategy from a similar domain for cleaning is non-viable. Alternatively, a path planning approach to gathering dirt samples selectively at locations with a high likelihood of dirt accumulation is more feasible. This work presents a first-of-its-kind dirt sample gathering strategy for the cleaning auditing robots by combining the geometrical feature extraction and swarm algorithms. This combined approach generates an efficient optimal path covering all the identified dirt locations for efficient cleaning auditing. Besides being the foundational effort for cleaning audit, a path planning approach considering the geometric signatures that contribute to the dirt accumulation of a region has not been device so far. The proposed approach is validated systematically through experiment trials. The geometrical feature extraction-based dirt location identification method successfully identified dirt accumulated locations in our post-cleaning analysis as part of the experiment trials. The path generation strategies are validated in a real-world environment using an in-house developed cleaning auditing robot BELUGA. From the experiments conducted, the ant colony optimization algorithm generated the best cleaning auditing path with less travel distance, exploration time, and energy usage.

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Section: Related Workmentioning

confidence: 99%

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

Pathmakumar

Muthugala

Samarakoon

et al. 2022

Sensors

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“…Yim et al (2007) discussed the possible usage of reconfigurability for robots to improve their performance. On the basis of these inspirations, a class of hinged reconfigurable robots has been developed for enhancing cleaning coverage performance (Kee et al, 2014; Samarakoon et al, 2022b). The reconfiguration allows a robot to access narrow spaces that would not be possible by a fixed‐shape robot (Muthugala et al, 2022a; Samarakoon et al, 2022a).…”

Section: Introductionmentioning

confidence: 99%

Insights from autonomy trials of a self‐reconfigurable floor‐cleaning robot in a public food court

Wijegunawardana,

Muthugala,

Samarakoon

et al. 2024

Journal of Field Robotics

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As autonomous cleaning robots advance, we expect certain features, such as higher area coverage and robustness. To explore these features and their challenges, we need tools and strategies that allow us to develop them rapidly. In this paper, we present lessons learned and results while performing 3 months long field trials on the autonomy of a self‐reconfigurable floor‐cleaning robot. The autonomy here includes path planning and navigation of the robot for coverage in a public food court. The environment of the food court is often subjected to alterations due to human activities. One of the main contributions of this paper is proposing a task‐level execution system to deploy the cleaning robot in an actual use case scenario. We also provide in‐depth discussions on the solutions and the implementation strategies we found in accomplishing goals. These findings would be helpful in conducting autonomy development targeted for field deployments of self‐reconfigurable robots and cleaning robots.

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“…Specifically, these research efforts have focused on areas such as cleaning systems, [3] dirt detection, [4] and area coverage. [5] In the past decade, there has been significant research focused on enhancing the cleaning performance of robots by increasing their coverage. [6] Consequently, coverage path planning (CPP) has gained considerable attention in this field.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, these research efforts have focused on areas such as cleaning systems, [ 3 ] dirt detection, [ 4 ] and area coverage. [ 5 ]…”

Section: Introductionmentioning

confidence: 99%

FMEA‐Based Coverage‐Path‐Planning Strategy for Floor‐Cleaning Robots

Wijegunawardana,

Samarakoon,

Muthugala

et al. 2023

Advanced Intelligent Systems

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Floor‐cleaning robots play a crucial role in both industrial and domestic spaces. However, these robots often face challenges due to hazardous components in their environment, which can cause them to fail and prevent them from performing at their best. This situation necessitates continuous research in the field of floor‐cleaning robots. However, most of these efforts focus on improving the robots’ perception capabilities by incorporating additional sensors. Nevertheless, incorporating more sensors is an expensive solution for most cleaning robots. Alternatively, in this research, the feasibility of introducing a safe path on a predefined hazard map is explored. The proposed method aims to trade‐off between area coverage and the safety of the cleaning robot. Herein, the failure mode and effect analysis (FMEA) method is introduced as a tool to classify the hazards and implement a safety‐ensured coverage path‐planning process. In this approach, the risk factor defined for a point in the environment serves as the key parameter to assess the safety of the algorithm's suggested path. To validate and evaluate the proposed method, this article utilizes the hTetro mobile robot. In the experimental results, it is demonstrated that the proposed method can reduce high‐risk movements of the robot compared to existing methods.

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Metaheuristic based navigation of a reconfigurable robot through narrow spaces with shape changing ability

Cited by 9 publications

References 21 publications

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

A Novel Path Planning Strategy for a Cleaning Audit Robot Using Geometrical Features and Swarm Algorithms

Insights from autonomy trials of a self‐reconfigurable floor‐cleaning robot in a public food court

FMEA‐Based Coverage‐Path‐Planning Strategy for Floor‐Cleaning Robots

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