Development and testing of the Mars Rover Mobility Platform for educational and research purposes

Mars exploration has a long history, but there were only four roving vehicles which successfully operated on its surface (e.g. [1]). Main reasons for this are the mission cost and complexity. This paper describes a Mars Rover Mobility Platform for educational and research purposes developed at Kingston University of London. This platform utilises off-the-shelf components to minimise the cost of the project, and is designed to allow for future improvement. The rover is targeted to meet university research and educational objectives. This paper describes the design, manufacturing and control system of a robotic vehicle. The emphasis of this paper is the implementation of the control system. The investigation in locomotive sub-system and its traction performance was done [4]. The rover was manufactured in-house and its manufacturing method and its main components will be described. The control of the vehicle was done using python programming language and implemented on Raspberry Pi 2B+ controller. The communication was done via Wi-Fi using socket connection stream to identify the TCP/IP of the server and connect to the client. Finally, the testing operation was conducted by producing a qualitative comparison between the actual performance and the specified requirements. The rover design reported here achieved climbing capability for the slopes of 23o, the turning radius of zero degrees. The final mass of the rover is 18 kg including allowance for the payload. The rover is able to reach a velocity of 5 cm/s

Pose-graph neural network classifier for global optimality prediction in 2D SLAM

The ability to decide if a solution to a pose-graph problem is globally optimal is of high significance for safety-critical applications. Converging to a local-minimum may result in severe estimation errors along the estimated trajectory. In this paper, we propose a graph neural network based on a novel implementation of a graph convolutional-like layer, called PoseConv, to perform classification of pose-graphs as optimal or sub-optimal. The operation of PoseConv required incorporating a new node feature, referred to as cost, to hold the information that the nodes will communicate. A training and testing dataset was generated based on publicly available bench-marking pose-graphs. The neural classifier is then trained and extensively tested on several subsets of the pose-graph samples in the dataset. Testing results have proven the model's capability to perform classification with 92 - 98% accuracy, for the different partitions of the training and testing dataset. In addition, the model was able to generalize to previously unseen variants of pose-graphs in the training dataset. Our method trades a small amount of accuracy for a large improvement in processing time. This makes it faster than other existing methods by up-to three orders of magnitude, which could be of paramount importance when using computationally-limited robots overseen by human operators

Indicated Torque.

Neural Networks Abstract — This paper presents an artificial neural networks approach to estimate the indicated torque of a single-cylinder diesel engine from crank shaft angular position and velocity measurements. Since these variables can be measured using low-cost sensors, the estimator may be useful in the implementation of the control or diagnostics strategies that require cylinder indicated torque, a variables that are not easily measured and need expensive sensors. The approach is to design indicated torque estimators using feedback and an artificial neural networks model as feedforward. Such an approach can offer the advantage of being amenable to real-time implementation. The estimated results of the engine indicated torque are presented, which compared with experimental data indicate a good agreement