TY - GEN
T1 - Modeling and path planning of the city-climber robot part I
T2 - 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009
AU - Yue, Ronggang
AU - Xiao, Jizhong
AU - Wang, Shaoping
AU - Joseph, Samleo L.
PY - 2009
Y1 - 2009
N2 - This is the first part of a series papers on modeling and path planning of the City-Climber robot, a novel wall-climbing robot which can climb walls, move on ceilings and transit between floor and walls. In order to provide the City-Climber with 3D path planning capability to carry out tasks such as cleaning, painting, and building inspection in 3D environments, we use mixed integer linear programming (MILP) as a tool to incorporate logical constraints such as obstacle avoidance and waypoint selection with basic dynamic constraints. In this paper, we derive the dynamic model of the City-Climber robot in different cases, i.e., on the floor, walls, and ceiling, respectively. Non-dimensional variables are introduced to simplify the models. Simulation results verified the correctness of the model since the trajectories match the expected practical motion of the robot. MILP-based 3D path planning will be presented in part 2 which account for the dynamic constraints and obstacle avoidance.
AB - This is the first part of a series papers on modeling and path planning of the City-Climber robot, a novel wall-climbing robot which can climb walls, move on ceilings and transit between floor and walls. In order to provide the City-Climber with 3D path planning capability to carry out tasks such as cleaning, painting, and building inspection in 3D environments, we use mixed integer linear programming (MILP) as a tool to incorporate logical constraints such as obstacle avoidance and waypoint selection with basic dynamic constraints. In this paper, we derive the dynamic model of the City-Climber robot in different cases, i.e., on the floor, walls, and ceiling, respectively. Non-dimensional variables are introduced to simplify the models. Simulation results verified the correctness of the model since the trajectories match the expected practical motion of the robot. MILP-based 3D path planning will be presented in part 2 which account for the dynamic constraints and obstacle avoidance.
UR - https://www.scopus.com/pages/publications/77951483520
U2 - 10.1109/ROBIO.2009.5420829
DO - 10.1109/ROBIO.2009.5420829
M3 - 会议稿件
AN - SCOPUS:77951483520
SN - 9781424447756
T3 - 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009
SP - 2385
EP - 2390
BT - 2009 IEEE International Conference on Robotics and Biomimetics, ROBIO 2009
Y2 - 19 December 2009 through 23 December 2009
ER -