By Michael John Milford
This pioneering e-book describes the improvement of a robotic mapping and navigation process encouraged through versions of the neural mechanisms underlying spatial navigation within the rodent hippocampus. Computational types of animal navigation platforms have often had constrained functionality whilst carried out on robots. this can be the 1st study to check current types of rodent spatial mapping and navigation on robots in huge, difficult, genuine global environments.
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Extra info for Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning Based on Hippocampal Models (Springer Tracts in Advanced Robotics)
2001; Montemerlo, Thrun et al. 2003), there is a wide range of ways in which a probabilistic approach can be implemented. There are several types of probabilistic algorithms that have been the focus of much research, with no clear ‘best’ one. Techniques vary in their complexity, assumptions about the environment, sensor usage, computational requirements, flexibility across robot platforms, scalability, and many other characteristics. Some are explicitly probabilistic down to the core mathematical foundations, while others are more implicit in their probabilistic characteristics, having been developed in a more heuristic manner.
Rolls (2005) describes the mean half-amplitude width of the cells’ tuning curves as being 76º. Head direction cells are not affected by the location of the monkey within the environment, and will give similar responses for the same monkey head direction even if the monkey has been moved and the visual scene is completely different. Primates also possess spatial view cells. These cells have responses based on where the monkey is looking. The information regarding where the monkey is looking is encoded in allocentric coordinates, storing the absolute location of the place rather than the location relative to the monkey’s current position and head orientation.
When a goal is specified, the ELDEN system assigns links a cost that is inversely proportional to the robot’s confidence about the traversability of the link. Dijkstra’s algorithm is used to find the lowest cost path from each place unit to the goal location. Each place link is then assigned an orientation indicating the movement direction required to move closer to the goal. After this algorithm has been applied each place unit has only one link that it can traverse to move closer to the goal.
Robot Navigation from Nature: Simultaneous Localisation, Mapping, and Path Planning Based on Hippocampal Models (Springer Tracts in Advanced Robotics) by Michael John Milford