https://www.cogsci.ucsd.edu/~sereno/107B-201/readings/08.10-ConjunctiveRepEnto.pdf

Conjunctive Representation of Position, Direction, and Velocity in Entorhinal Cortex

Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This article reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey.

Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

Biological organisms learn from interactions with their environment throughout their lifetime. For artificial systems to successfully act and adapt in the real world, it is desirable to similarly be able to learn on a continual basis. This challenge is known as lifelong learning, and remains to a large extent unsolved. In this Perspective article, we identify a set of key capabilities that artificial systems will need to achieve lifelong learning. We describe a number of biological mechanisms, both neuronal and non-neuronal, that help explain how organisms solve these challenges, and present examples of biologically inspired models and biologically plausible mechanisms that have been applied to artificial systems in the quest towards development of lifelong learning machines. We discuss opportunities to further our understanding and advance the state of the art in lifelong learning, aiming to bridge the gap between natural and artificial intelligence. It is an outstanding challenge to develop intelligent machines that can learn continually from interactions with their environment, throughout their lifetime. Kudithipudi et al. review neuronal and non-neuronal processes in organisms that address this challenge and discuss pathways to developing biologically inspired approaches for lifelong learning machines. 

https://figshare.com/articles/journal_contribution/Biological_underpinnings_for_lifelong_learning_machines/19453778/1/files/34557773.pdf

Biological underpinnings for lifelong learning machines

The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights.

/pdf/a-comprehensive-review-of-coverage-path-planning-in-robotics-6u94w2far3.pdf

A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

SLAM; definition and evolution

Exploration is a critical function for autonomous mobile robots. Traditionally, the entire map has to be processed to extract frontiers and perform path planning. However, as the robot explores the environment, the map grows over time, and increasing computational resources are required, especially for large-scale environments. Moreover, only a few methods focus on the exploration on point cloud maps. Here, I present a new practical method to autonomous mobile robot exploration based on a sparse, relatively small-size point cloud local map, which combines Rapidly-exploring Random Tree (RRT) and dynamic window approach (DWA) algorithm together. The local map is built from the consecutive inputs of raw point clouds using an inexpensive 3D sensor, i.e. Kinect V2. Frontiers are effectively detected and local path planning is performed by RRT algorithm directly on unordered point cloud local maps. Motion planning is performed online by DWA to avoid obstacles and direct a nonholonomic mobile robot towards frontiers separating known environments from unknown environments. Embedded with simultaneous localization and mapping (SLAM) system of my previous research, the performance of the proposed method is evaluated in a large-scale customized virtual environment with a size of $33 \times 29 \times 6\mathrm{m}$ using Gazebo as the robotic simulator. The results suggest that the proposed algorithm can accurately direct the nonholonomic mobile robot to unexplored environments in real time. Also, it successfully helps build a coherent semi-metric topological map. The proposed algorithm shows great efficient performance and is suitable for both static and dynamic environments. Combination of RRT and DWA algorithm on point clouds, as a general approach, can be extended to generic 3D nonplanar physical environments. Videos of the experiments can be found at https://youtu.be/0i766fhs9Ds.

Mobile Robot Exploration Based on Rapidly-exploring Random Trees and Dynamic Window Approach

In many simultaneous localization and mapping (SLAM) systems, the map of the environment grows over time as the robot explores the environment. The ever-growing map prevents long-term mapping, especially in large-scale environments. In this paper, we develop a compact cognitive mapping approach inspired by neurobiological experiments. Mimicking the firing activities of neighborhood cells, neighborhood fields determined by movement information, i.e. translation and rotation, are modeled to describe one of the distinct segments of the explored environment. The vertices with low neighborhood field activities are avoided to be added into the cognitive map. The optimization of the cognitive map is formulated as a robust non-linear least squares problem constrained by the transitions between vertices, and is numerically solved efficiently. According to the cognitive decision-making of place familiarity, loop closure edges are clustered depending on time intervals, and then batch global optimization of the cognitive map is performed to satisfy the combined constraint of the whole cluster. After the loop closure process, scene integration is performed, in which revisited vertices are removed subsequently to further reduce the size of the cognitive map. The compact cognitive mapping approach is tested on a monocular visual SLAM system in a naturalistic maze for a biomimetic animated robot. Our results demonstrate that the proposed method largely restricts the growth of the size of the cognitive map over time, and meanwhile, the compact cognitive map correctly represents the overall layout of the environment. The compact cognitive mapping method is well suitable for the representation of large-scale environments to achieve long-term robot navigation.

A brain-inspired compact cognitive mapping system

A theory of geometry representations for spatial navigation

We propose a neurobiologically inspired visual simultaneous localization and mapping (SLAM) system based on direction sparse method to real-time build cognitive maps of large-scale environments from a moving stereo camera. The core SLAM system mainly comprises a Bayesian attractor network, which utilizes neural responses of head direction (HD) cells in the hippocampus and grid cells in the medial entorhinal cortex (MEC) to represent the head direction and the position of the robot in the environment, respectively. Direct sparse method is employed to accurately and robustly estimate velocity information from a stereo camera. Input rotational and translational velocities are integrated by the HD cell and grid cell networks, respectively. We demonstrated our neurobiologically inspired stereo visual SLAM system on the KITTI odometry benchmark datasets. Our proposed SLAM system is robust to real-time build a coherent semi-metric topological map from a stereo camera. Qualitative evaluation on cognitive maps shows that our proposed neurobiologically inspired stereo visual SLAM system outperforms our previous brain-inspired algorithms and the neurobiologically inspired monocular visual SLAM system both in terms of tracking accuracy and robustness, which is closer to the traditional state-of-the-art one.

StereoNeuroBayesSLAM: A Neurobiologically Inspired Stereo Visual SLAM System Based on Direct Sparse Method

As the robot explores the environment, the map grows over time in the simultaneous localization and mapping (SLAM) system, especially for the large scale environment. The ever-growing map prevents long-term mapping. In this paper, we developed a compact cognitive mapping approach inspired by neurobiological experiments. Inspired from neighborhood cells, neighborhood fields determined by movement information, i.e. translation and rotation, are proposed to describe one of distinct segments of the explored environment. The vertices and edges with movement information below the threshold of the neighborhood fields are avoided adding to the cognitive map. The optimization of the cognitive map is formulated as a robust non-linear least squares problem, which can be efficiently solved by the fast open linear solvers as a general problem. According to the cognitive decision-making of familiar environments, loop closure edges are clustered depending on time intervals, and then parallel computing is applied to perform batch global optimization of the cognitive map for ensuring the efficiency of computation and real-time performance. After the loop closure process, scene integration is performed, in which revisited vertices are removed subsequently to further reduce the size of the cognitive map. A monocular visual SLAM system is developed to test our approach in a rat-like maze environment. Our results suggest that the method largely restricts the growth of the size of the cognitive map over time, and meanwhile, the compact cognitive map correctly represents the overall layer of the environment as the standard one. Experiments demonstrate that our method is very suited for compact cognitive mapping to support long-term robot mapping. Our approach is simple, but pragmatic and efficient for achieving the compact cognitive map.

A Brain-Inspired Compact Cognitive Mapping System

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