Continuous learning route map for robot navigation using a growing-on-demand self-organizing neural network

Abstract

This article proposes an experience-based route map continuous learning method and applies it into robot planning and navigation. First of all, the framework for robot route map learning and navigation is designed, which incorporates the four cyclic processes of planning, motion, perception, and extraction, enabling robot to constantly learn the information of the road experience and to obtain and improve the route map of the environment. Besides, a growing-on-demand selforganizing neural network learning algorithm is also proposed. This algorithm is based on growing neural gas algorithm, but it does not require presetting of network scale, and under the condition of dynamically growing input data, it can regulate the increase scale of network online in a self-adaptive and self-organized manner to obtain stable learning results. Finally, with robot roaming in an environment, this algorithm is used to conduct continuous learning of dynamically increasing route information, extract the topological structure of the raw road data in feature space, and ultimately obtain the route map of the environment. Mobile robot utilizes the route map to plan a suitable route and guides robot to move to the destination along the route and complete navigation task. Through physical experiments in outdoor environment, its feasibility and validity are verified.

Continuous reinforcement learning to adapt multi-objective optimization online for robot motion

“…Lifelong or continuous learning has been a long-standing challenge for machine learning and autonomous systems. [8][9][10] Mimicking humans and animals that continuously acquire new knowledge and transfer them to new tasks throughout their lifetime, continuous learning builds an adaptive system that is capable of learning from a continuous stream of information. However, dilemma between plasticity and catastrophic forgetting 11,12 is the main challenge due to inefficiency and poor performances when relearning from scratch for new tasks.…”

Machine Autonomy: Definition, Approaches, Challenges and Research Gaps

“…Some applications of unsupervised learning in autonomous agent design. Unsupervised learning algorithms like PCA, SOM, ICA and k-means algorithms are often applied in dimensionality reduction tasks in multimodal sensors tasks (50).Furthermore, some attempts have been made in implementing the forward kinematics of a robot using unsupervised learning algorithms [51,52]. Chaput in [51] implemented a self-recovery mechanism, using a hierarchy of SOMs, which enables a robot to fall back to a lower level of knowledge if its higher-level knowledge cannot handle a situation in the environment.…”

Smart Navigation of Humanoid Robots Using DAYKUN-BIP Virtual Target Displacement and Petri-Net Strategy

“…Van Nguyen et al 14 applied fuzzy-based reactive behaviours on a robotic platform and tested it successfully in an unknown environment. Zhong et al 15 used a self-organizing neural network-based approach for navigation of a mobile robot in a complex environment. Sierakowski and dos Santos Coelho 16 used a bacterial colony-based navigational technique in smooth movement of a mobile robot in simulation platforms.…”