An autonomous agent navigating with a polarized light compass
Adaptive Behavior
Pure reactive behavior learning using Case Based Reasoning for a vision based 4-legged robot
Robotics and Autonomous Systems
Three 2D-warping schemes for visual robot navigation
Autonomous Robots
Landmark vectors with quantized distance information for homing navigation
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Case-based reasoning emulation of persons for wheelchair navigation
Artificial Intelligence in Medicine
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Previous work has shown that honeybees use a snapshot model to determine a local vector to find their way home. A simpler, average landmark vector model has since been proposed for biologically-inspired mobile robot homing. Previously, the authors have extended the model to address the problem of docking a unicycle-like vehicle smoothly using bearing-only information and without reconstructing the pose of the vehicle (Wei et al., 2003, 2004). Here, we extend further to consider weighted landmarks, allowing greater control over the shape of the trajectory that the robot will follow. This approach permits docking from a wider range of initial poses, while respecting the kinematic constraints of the robot. The proposed control method has been implemented on the Nomadic Technologies XR4000 robot at ANU using visual landmarks. Experimental results are presented which demonstrate the desired docking behaviour from a broad range of initial conditions.