Finding an optimal trajectory from an initial point to a final point through closely packed obstacles, and controlling a Hilare robot through this trajectory, are challenging tasks. To serve this purpose, path planner...Finding an optimal trajectory from an initial point to a final point through closely packed obstacles, and controlling a Hilare robot through this trajectory, are challenging tasks. To serve this purpose, path planners and trajectory-tracking controllers are usually included in a control loop. This paper highlights the implementation of a trajectory-tracking controller on a stepper motor-driven Hilare robot, with a trajectory that is described as a set of waypoints. The controller was designed to handle discrete waypoints with directional discontinuity and to consider different constraints on the actuator velocity. The control parameters were tuned with the help of multi-objective particle swarm optimization to minimize the average cross-track error and average linear velocity error of the mobile robot when tracking a predefined trajectory. Experiments were conducted to control the mobile robot from a start position to a destination position along a trajectory described by the waypoints. Experimental results for tracking the trajectory generated by a path planner and the trajectory specified by a user are also demonstrated. Experiments conducted on the mobile robot validate the effectiveness of the proposed strategy for tracking different types of trajectories.展开更多
This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration spa...This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator.展开更多
文摘Finding an optimal trajectory from an initial point to a final point through closely packed obstacles, and controlling a Hilare robot through this trajectory, are challenging tasks. To serve this purpose, path planners and trajectory-tracking controllers are usually included in a control loop. This paper highlights the implementation of a trajectory-tracking controller on a stepper motor-driven Hilare robot, with a trajectory that is described as a set of waypoints. The controller was designed to handle discrete waypoints with directional discontinuity and to consider different constraints on the actuator velocity. The control parameters were tuned with the help of multi-objective particle swarm optimization to minimize the average cross-track error and average linear velocity error of the mobile robot when tracking a predefined trajectory. Experiments were conducted to control the mobile robot from a start position to a destination position along a trajectory described by the waypoints. Experimental results for tracking the trajectory generated by a path planner and the trajectory specified by a user are also demonstrated. Experiments conducted on the mobile robot validate the effectiveness of the proposed strategy for tracking different types of trajectories.
文摘This article describes a biologically inspired node generator for the path planning of serially connected hyper-redundant manipulators using probabilistic roadmap planners. The generator searches the configuration space surrounding existing nodes in the roadmap and uses a combination of random and deterministic search methods that emulate the behaviour of octopus limbs. The strategy consists of randomly mutating the states of the links near the end-effector, and mutating the states of the links near the base of the robot toward the states of the goal configuration. When combined with the small tree probabilistic roadmap planner, the method was successfully used to solve the narrow passage motion planning problem of a 17 degree-of-freedom manipulator.
