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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Stable Autonomous Robotic Wheelchair Navigation in the Environment with Slope Way
AU - Wang, C.
AU - Xia, M.
AU - Meng, M.Q.-H.
N1 - ©2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2020/10/22
Y1 - 2020/10/22
N2 - In this article, we present a path planning approach that is capable of generating a feasible trajectory for stable robotic wheelchair navigation in the environment with slope way. Firstly, the environment is modeled by a lightweight navigation map, with which the proposed sampling-based path planning scheme with a modified extension function can generate a feasible path. Then, the path is further optimized by the proposed utility function involving the human comfort and the path cost. To improve the searching efficiency of an optimal trajectory, we present an adaptive weighting Gaussian Mixture Model (GMM) based sampling strategy. Particularly, the weights of the components in GMM are adjusted adaptively in the planning process. It is also worth noting that the proposed sampling-based planning paradigm can indicate the unsafe regions in the navigation map, which forms a traversable map and further guarantees the safety of the wheelchair robot navigation. Furthermore, the effectiveness and the efficiency of the proposed path planning method are verified in both simulation and real-world experiments. © 1967-2012 IEEE.
AB - In this article, we present a path planning approach that is capable of generating a feasible trajectory for stable robotic wheelchair navigation in the environment with slope way. Firstly, the environment is modeled by a lightweight navigation map, with which the proposed sampling-based path planning scheme with a modified extension function can generate a feasible path. Then, the path is further optimized by the proposed utility function involving the human comfort and the path cost. To improve the searching efficiency of an optimal trajectory, we present an adaptive weighting Gaussian Mixture Model (GMM) based sampling strategy. Particularly, the weights of the components in GMM are adjusted adaptively in the planning process. It is also worth noting that the proposed sampling-based planning paradigm can indicate the unsafe regions in the navigation map, which forms a traversable map and further guarantees the safety of the wheelchair robot navigation. Furthermore, the effectiveness and the efficiency of the proposed path planning method are verified in both simulation and real-world experiments. © 1967-2012 IEEE.
KW - autonomous vehicle
KW - navigation
KW - Path planning
KW - robot motion
KW - Air navigation
KW - Efficiency
KW - Gaussian distribution
KW - Navigation
KW - Robotics
KW - Robots
KW - Wheelchairs
KW - Gaussian Mixture Model
KW - Optimal trajectories
KW - Path planning method
KW - Real world experiment
KW - Robotic wheelchairs
KW - Sampling strategies
KW - Sampling-based planning
KW - Searching efficiency
KW - Robot programming
U2 - 10.1109/TVT.2020.3009979
DO - 10.1109/TVT.2020.3009979
M3 - Journal article
VL - 69
SP - 10759
EP - 10771
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
SN - 0018-9545
IS - 10
ER -