Anti-collision Static Rotation Local Planner for Four Independent Steering Drive Self-reconfigurable Robot

Yi, Lim; Le, Anh Vu; Hayat, Abdullah Aamir; Elangovan, Karthikeyan; Leong, Kristor; Povendhan, A.P.; Elara, Mohan Rajesh

doi:10.1109/icra46639.2022.9812445

Cited by 7 publications

(2 citation statements)

References 30 publications

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“…Similarly, reconfigurable robotic systems present innovative approaches to robot design and functionality. The limitations of fixed-morphology cleaning and maintenance robots can be addressed by introducing a heuristic framework for reconfigurable robotic system design such as the one in [50], while the use of sensors integrated for navigation has been highlighted in [51,52]. A taxonomy and evaluation framework for self-reconfigurable robots based on mechanism reconfigurability and autonomy levels was presented in [53], along with an evaluation of a staircase-climbing robot.…”

Section: Methods Advantages Limitationsmentioning

confidence: 99%

Deep Learning Based Fall Recognition and Forecasting for Reconfigurable Stair-Accessing Service Robots

Ong,

Hayat,

Gomez

et al. 2024

Mathematics

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This paper presents a comprehensive study on fall recognition and forecasting for reconfigurable stair-accessing robots by leveraging deep learning techniques. The proposed framework integrates machine learning algorithms and recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM), for fall detection of service robots on staircases. The reconfigurable stair-accessing robot sTetro serves as the platform, and the fall data required for training models are generated in a simulation environment. The two machine learning algorithms are compared and their effectiveness on the fall recognition task is reported. The results indicate that the BiLSTM model effectively classifies falls with a median categorical accuracy of 94.10% in simulation and 90.02% with limited experiments. Additionally, the BiLSTM model can be used for forecasting, which is practically valuable for making decisions well before the onset of a free fall. This study contributes insights into the design and implementation of fall detection systems for service robots used to navigate staircases through deep learning approaches. Our experimental and simulation data, along with the simulation steps, are available for reference and analysis via the shared link.

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Section: Methods Advantages Limitationsmentioning

confidence: 99%

Deep Learning Based Fall Recognition and Forecasting for Reconfigurable Stair-Accessing Service Robots

Ong,

Hayat,

Gomez

et al. 2024

Mathematics

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“…When multiple robots work in the same environment, decision making is needed for robots to work together. Some examples of path planners are seen in individual robots [1,2], robot teams [3,4], reconfigurable robots [5,6], and swarms [7,8].…”

Section: Introductionmentioning

confidence: 99%

Inter-Reconfigurable Robot Path Planner for Double-Pass Complete Coverage Problem

Sang,

Yang,

et al. 2024

Mathematics

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Recent advancements in autonomous mobile robots have led to significant progress in area coverage tasks. However, challenges persist in optimizing the efficiency and computational complexity of complete coverage path planner (CCPP) algorithms for multi-robot systems, particularly in scenarios requiring revisiting or a double pass in specific locations, such as cleaning robots addressing spilled consumables. This paper presents an innovative approach to tackling the double-pass complete coverage problem using an autonomous inter-reconfigurable robot path planner. Our solution leverages a modified Glasius bio-inspired neural network (GBNN) to facilitate double-pass coverage through inter-reconfiguration between two robots. We compare our proposed algorithm with traditional multi-robot path planning in a centralized system, demonstrating a reduction in algorithm iterations and computation time. Our experimental results underscore the efficacy of the proposed solution in enhancing the efficiency of area coverage tasks. Furthermore, we discuss the implementation details and limitations of our study, providing insights for future research directions in autonomous robotics.

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Self-reconfigurable robot vision pipeline for safer adaptation to varying pavements width and surface conditions

Gómez

Ramalingam

et al. 2022

Sci Rep

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This work presents the vision pipeline for our in-house developed autonomous reconfigurable pavement sweeping robot named Panthera. As the goal of Panthera is to be an autonomous self-reconfigurable robot, it has to understand the type of pavement it is moving in so that it can adapt smoothly to changing pavement width and perform cleaning operations more efficiently and safely. deep learning (DL) based vision pipeline is proposed for the Panthera robot to recognize pavement features, including pavement type identification, pavement surface condition prediction, and pavement width estimation. The DeepLabv3+ semantic segmentation algorithm was customized to identify the pavement type classification, an eight-layer CNN was proposed for pavement surface condition prediction. Furthermore, pavement width estimation was computed by fusing the segmented pavement region on the depth map. In the end, the fuzzy inference system was implemented by taking input as the pavement width and its conditions detected and output as the safe operational speed. The vision pipeline was trained using the DL provided with the custom pavement images dataset. The performance was evaluated using offline test and real-time field trial images captured through the reconfigurable robot Panthera stereo vision sensor. In the experimental analysis, the DL-based vision pipeline components scored 88.02% and 93.22% accuracy for pavement segmentation and pavement surface condition assessment, respectively, and took approximately 10 ms computation time to process the single image frame from the vision sensor using the onboard computer.

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Anti-collision Static Rotation Local Planner for Four Independent Steering Drive Self-reconfigurable Robot

Cited by 7 publications

References 30 publications

Deep Learning Based Fall Recognition and Forecasting for Reconfigurable Stair-Accessing Service Robots

Deep Learning Based Fall Recognition and Forecasting for Reconfigurable Stair-Accessing Service Robots

Inter-Reconfigurable Robot Path Planner for Double-Pass Complete Coverage Problem

Self-reconfigurable robot vision pipeline for safer adaptation to varying pavements width and surface conditions

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