Ground Mobile Platforms Research Articles

Design of A Novel Wheel-Legged Robot with Rim Shape Changeable Wheels

The wheel-legged hybrid structure has been utilized by ground mobile platforms in recent years to achieve good mobility on both flat surfaces and rough terrain. However, most of the wheel-legged robots only have one-directional obstacle-crossing ability. During the motion, most of the wheel-legged robots’ centroid fluctuates violently, which damages the stability of the load. What’s more, many designs of the obstacle-crossing part and transformation-driving part of this structure are highly coupled, which limits its optimal performance in both aspects. This paper presents a novel wheel-legged robot with a rim-shaped changeable wheel, which has a bi-directional and smooth obstacle-crossing ability. Based on the kinematic model, the geometric parameters of the wheel structure and the design variables of the driving four-bar mechanism are optimized separately. The kinetostatics model of the mobile platform when climbing stairs is established to determine the body length and angular velocity of the driving wheels. A prototype is made according to the optimal parameters. Experiments show that the prototype installed with the novel transformable wheels can overcome steps with a height of 1.52 times of its wheel radius with less fluctuation of its centroid and performs good locomotion capabilities in different environments.

Agricultural Harvesting Robot Concept Design and System Components: A Review

Developing different robotic platforms for farm operations is vital to addressing the increasing world population. A harvesting robot significantly increases a farm’s productivity while farmers focus on other relevant farm operations. From the literature, it could be summarized that the design concepts of the harvesting mechanisms were categorized as grasping and cutting, vacuum suction plucking systems, twisting and plucking mechanisms, and shaking and catching. Meanwhile, robotic system components include the mobile platform, manipulators, and end effectors, sensing and localization, and path planning and navigation. The robotic system must be cost-effective and safe. The findings of this research could contribute to the design process of developing a harvesting robot or developing a harvesting module that can be retrofitted to a commercially available mobile platform. This paper provides an overview of the most recent harvesting robots’ different concept designs and system components. In particular, this paper will highlight different agricultural ground mobile platforms and their associated mechanical design, principles, challenges, and limitations to characterize the crop environment relevant to robotic harvesting and to formulate directions for future research and development for cotton harvesting platforms.

Rotorcraft aerial vehicle’s contact-based landing and vision-based localization research

AbstractA novel concept—the contact-based landing on a mobile platform—is proposed in this paper. An adaptive backstepping controller is designed to deal with the unknown disturbances in the interactive process, and the contact-based landing mission is implemented under the hybrid force/motion control framework. A rotorcraft aerial vehicle system and a ground mobile platform are designed to conduct flight experiments, evaluating the feasibility of the proposed landing scheme and control strategy. To the best of our knowledge, this is the first time a rotorcraft unmanned aerial vehicle has been implemented to conduct a contact-based landing. To improve system autonomy in future applications, vision-based recognition and localization methods are studied, contributing to the detection of a partially occluded cooperative object or at a close range. The proposed recognition algorithms are tested on a ground platform and evaluated in several simulated scenarios, indicating the algorithm’s effectiveness.

Target Association and Management Engineering Application of Photoelectric Detection of Ground Mobile Platform

On the basis of the current detection methods and the target information obtained of ground mobile platform, this paper constructed the architecture of target management of mobile platform, put forward the target association and management solution based on image recognition, tracking, laser ranging, and situation sharing, initially completed the engineering realization through position association algorithm, and installed on a special unmanned vehicle by computer simulation, verified this software, the verification results show that this architecture can complete the functions of multi-source target management in mobile platform, it could access the mobile platform software architecture of existing models, could realize the target position association and had good engineering application value.

Audio-Visual Self-Supervised Terrain Type Recognition for Ground Mobile Platforms

The ability to recognize and identify terrain characteristics is an essential function required for many autonomous ground robots such as social robots, assistive robots, autonomous vehicles, and ground exploration robots. Recognizing and identifying terrain characteristics is challenging because similar terrains may have very different appearances (e.g., carpet comes in many colors), while terrains with very similar appearance may have very different physical properties (e.g., mulch versus dirt). In order to address the inherent ambiguity in vision-based terrain recognition and identification, we propose a multi-modal self-supervised learning technique that switches between audio features extracted from a microphone attached to the underside of a mobile platform and image features extracted by a camera on the platform to cluster terrain types. The terrain cluster labels are then used to train an image-based real-time CNN (Convolutional Neural Network) to predict terrain types changes. Through experiments, we demonstrate that the proposed self-supervised terrain type recognition method achieves over 80% accuracy, which greatly outperforms several baselines and suggests strong potential for assistive applications.

3D Scene Modeling And Understanding From Image Sequences

A new method for 3D modeling is proposed, which generates a content-based 3D mosaic (CB3M) representation for long video sequences of 3D, dynamic urban scenes captured by a camera on a mobile platform. In the first phase, a set of parallel-perspective (pushbroom) mosaics with varying viewing directions is generated to capture both the 3D and dynamic aspects of the scene under the camera coverage. In the second phase, a unified patch-based stereo matching algorithm is applied to extract parametric representations of the color, structure and motion of the dynamic and/or 3D objects in urban scenes, where a lot of planar surfaces exist. Multiple pairs of stereo mosaics are used for facilitating reliable stereo matching, occlusion handling, accurate 3D reconstruction and robust moving target detection. The outcome of this phase is a CB3M representation, which is a highly compressed visual representation for a dynamic 3D scene, and has object contents of both 3D and motion information. In the third phase, a multi-layer based scene understanding algorithm is proposed, resulting in a planar surface model for higher-level object representations. Experimental results are given for both simulated and several different real video sequences of large-scale 3D scenes to show the accuracy and effectiveness of the representation. We also show the patch-based stereo matching algorithm and the CB3M representation can be generalized to 3D modeling with perspective views using either a single camera or a stereovision head on a ground mobile platform or a pedestrian. Applications of the proposed method include airborne or ground video surveillance, 3D urban scene modeling, traffic survey, transportation planning and the visual aid for perception and navigation of blind people.