Improving Autonomous Robotic Navigation Using Imitation Learning

Autonomous navigation to a specified waypoint is traditionally accomplished with a layered stack of global path planning and local motion planning modules that generate feasible and obstacle-free trajectories. While these modules can be modified to meet task-specific constraints and user preferences, current modification procedures require substantial effort on the part of an expert roboticist with a great deal of technical training. In this paper, we simplify this process by inserting a Machine Learning module between the global path planning and local motion planning modules of an off-the shelf navigation stack. This model can be trained with human demonstrations of the preferred navigation behavior, using a training procedure based on Behavioral Cloning, allowing for an intuitive modification of the navigation policy by non-technical users to suit task-specific constraints. We find that our approach can successfully adapt a robot’s navigation behavior to become more like that of a demonstrator. Moreover, for a fixed amount of demonstration data, we find that the proposed technique compares favorably to recent baselines with respect to both navigation success rate and trajectory similarity to the demonstrator.

Survey of pedestrian detection with occlusion

Pedestrian detection is widely applied in surveillance, autonomous robotic navigation, and automotive safety. However, there are many occlusion problems in real life. This paper summarizes the research progress of pedestrian detection technology with occlusion. First, according to different occlusion, it can be divided into two categories: inter-class occlusion and intra-class occlusion. Second, it summarizes the traditional method and deep learning method to deal with occlusion. Furthermore, the main ideas and core problems of each method model are analyzed and discussed. Finally, the paper gives an outlook on the problems to be solved in the future development of pedestrian detection technology with occlusion.

Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video with Applications for Virtual Reality

We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. Panoramic depth estimation is an important technology for applications such as virtual reality, 3D modeling, and autonomous robotic navigation. In contrast to previous approaches for applying convolutional neural networks to panoramic imagery, we use the cylindrical panoramic projection which allows for the use of the traditional CNN layers such as convolutional filters and max pooling without modification. Our evaluation of synthetic and real data shows that unsupervised learning of depth and ego-motion on cylindrical panoramic images can produce high-quality depth maps and that an increased field-of-view improves ego-motion estimation accuracy. We create two new datasets to evaluate our approach: a synthetic dataset created using the CARLA simulator, and Headcam, a novel dataset of panoramic video collected from a helmet-mounted camera while biking in an urban setting. We also apply our network to the problem of converting monocular panoramas to stereo panoramas.

A Brain-Inspired Goal-Oriented Robot Navigation System

Autonomous navigation in unknown environments is still a challenge for robotics. Many efforts have been exerted to develop truly autonomous goal-oriented robot navigation models based on the neural mechanism of spatial cognition and mapping in animals’ brains. Inspired by the Semantic Pointer Architecture Unified Network (SPAUN) neural model and neural navigation mechanism, we developed a brain-like biologically plausible mathematical model and applied it to robotic spatial navigation tasks. The proposed cognitive navigation framework adopts a one-dimensional ring attractor to model the head-direction cells, uses the sinusoidal interference model to obtain the grid-like activity pattern, and gets optimal movement direction based on the entire set of activities. The application of adaptive resonance theory (ART) could effectively reduce resource consumption and solve the problem of stability and plasticity in the dynamic adjustment network. This brain-like system model broadens the perspective to develop more powerful autonomous robotic navigation systems. The proposed model was tested under different conditions and exhibited superior navigation performance, proving its effectiveness and reliability.

Machine Vision and Global Positioning System for Autonomous Robotic Navigation in Agriculture: A Review

Interest on robotic agriculture system has led to the development of agricultural robots that helps to improve the farming operation and increase the agriculture productivity. Much research has been conducted to increase the capability of the robot to assist agricultural operation, which leads to development of autonomous robot. This development provides a means of reducing agriculture’s dependency on operators, workers, also reducing the inaccuracy caused by human errors. There are two important development components for autonomous navigation. The first component is Machine vision for guiding through the crops and the second component is GPS technology to guide the robot through the agricultural fields.