RESUMEN
Inspired by rodents' ability to navigate freely in a given space, bionavigation systems provide alternatives to traditional probabilistic solutions. This paper proposed a bionic path planning method based on RatSLAM to provide a novel viewpoint for robots to make a more flexible and intelligent navigation scheme. A neural network fusing historic episodic memory was proposed to improve the connectivity of the episodic cognitive map. It is biomimetically important to generate an episodic cognitive map and establish a one-to-one correspondence between the events generated by episodic memory and the visual template of RatSLAM. The episodic cognitive map can be improved by imitating the rodents' behavior of memory fusion to produce better path planning results. The experimental results of different scenarios illustrate that the proposed method identified the connectivity between way points, optimized the result of path planning, and improved the flexibility of the system.
RESUMEN
Simultaneous localization and mapping (SLAM) refers to techniques for autonomously constructing a map of an unknown environment while, at the same time, locating the robot in this map. RatSLAM, a prevalent method, is based on the navigation system found in rodent brains. It has served as a base algorithm for other bioinspired approaches, and its implementation has been extended to incorporate new features. This work proposes xRatSLAM: an extensible, parallel, open-source framework applicable for developing and testing new RatSLAM variations. Tests were carried out to evaluate and validate the proposed framework, allowing the comparison of xRatSLAM with OpenRatSLAM and assessing the impact of replacing framework components. The results provide evidence that the maps produced by xRatSLAM are similar to those produced by OpenRatSLAM when they are fed with the same input parameters, which is a positive result, and that implemented modules can be easily changed without impacting other parts of the framework.
Asunto(s)
Robótica , Robótica/métodos , Algoritmos , EncéfaloRESUMEN
Localization and mapping has been a long standing area of research, both in neuroscience, to understand how mammals navigate their environment, as well as in robotics, to enable autonomous mobile robots. In this paper, we treat navigation as inferring actions that minimize (expected) variational free energy under a hierarchical generative model. We find that familiar concepts like perception, path integration, localization and mapping naturally emerge from this active inference formulation. Moreover, we show that this model is consistent with models of hippocampal functions, and can be implemented in silico on a real-world robot. Our experiments illustrate that a robot equipped with our hierarchical model is able to generate topologically consistent maps, and correct navigation behaviour is inferred when a goal location is provided to the system.
Asunto(s)
Robótica , Algoritmos , Animales , Simulación por Computador , HipocampoRESUMEN
This paper describes an improved brain-inspired simultaneous localization and mapping (RatSLAM) that extracts visual features from saliency maps using a frequency-tuned (FT) model. In the traditional RatSLAM algorithm, the visual template feature is organized as a one-dimensional vector whose values only depend on pixel intensity; therefore, this feature is susceptible to changes in illumination intensity. In contrast to this approach, which directly generates visual templates from raw RGB images, we propose an FT model that converts RGB images into saliency maps to obtain visual templates. The visual templates extracted from the saliency maps contain more of the feature information contained within the original images. Our experimental results demonstrate that the accuracy of loop closure detection was improved, as measured by the number of loop closures detected by our method compared with the traditional RatSLAM system. We additionally verified that the proposed FT model-based visual templates improve the robustness of familiar visual scene identification by RatSLAM.
RESUMEN
We have developed a Hierarchical Look-Ahead Trajectory Model (HiLAM) that incorporates the firing pattern of medial entorhinal grid cells in a planning circuit that includes interactions with hippocampus and prefrontal cortex. We show the model's flexibility in representing large real world environments using odometry information obtained from challenging video sequences. We acquire the visual data from a camera mounted on a small tele-operated vehicle. The camera has a panoramic field of view with its focal point approximately 5 cm above the ground level, similar to what would be expected from a rat's point of view. Using established algorithms for calculating perceptual speed from the apparent rate of visual change over time, we generate raw dead reckoning information which loses spatial fidelity over time due to error accumulation. We rectify the loss of fidelity by exploiting the loop-closure detection ability of a biologically inspired, robot navigation model termed RatSLAM. The rectified motion information serves as a velocity input to the HiLAM to encode the environment in the form of grid cell and place cell maps. Finally, we show goal directed path planning results of HiLAM in two different environments, an indoor square maze used in rodent experiments and an outdoor arena more than two orders of magnitude larger than the indoor maze. Together these results bridge for the first time the gap between higher fidelity bio-inspired navigation models (HiLAM) and more abstracted but highly functional bio-inspired robotic mapping systems (RatSLAM), and move from simulated environments into real-world studies in rodent-sized arenas and beyond.