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© 1963-2012 IEEE. Achieving high estimation accuracy is significant for semantic simultaneous localization and mapping (SLAM) tasks. Yet, the estimation process is vulnerable to several sources of error, including limitations of the instruments used to perceive the environment, shortcomings of the employed algorithm, environmental conditions, or other unpredictable noise. In this article, a novel stacked long short-term memory (LSTM)-based error reduction approach is developed to enhance the accuracy of semantic SLAM in presence of such error sources. Training and testing data sets were constructed through simulated and real-time experiments. The effectiveness of the proposed approach was demonstrated by its ability to capture and reduce semantic SLAM estimation errors in training and testing data sets. Quantitative performance measurement was carried out using the absolute trajectory error (ATE) metric. The proposed approach was compared with vanilla and bidirectional LSTM networks, shallow and deep neural networks, and support vector machines. The proposed approach outperforms all other structures and was able to significantly improve the accuracy of semantic SLAM. To further verify the applicability of the proposed approach, it was tested on real-time sequences from the TUM RGB-D data set, where it was able to improve the estimated trajectories.

Research studies on quadrotors have recently drawn significant interest from academia and industry. Faults and failures handling are the major weaknesses of conventional quadrotor platforms; therefore, an innovative actuation mechanism was introduced to allow tilting the rotors. Tilting rotors of multirotor platforms provide high dexterity for flying between adjacent obstacles and assist the platforms in dealing with various failure scenarios. This paper reviews the state of the research on tilt-quadrotor platforms. Several platforms, software and hardware architectures, were discussed in the literature. Most of the latest developments were focused on conventional quadrotor modelling, combined with rotor tilting dynamics. On the other hand, controlling such platform was mainly studied using two types of controllers: Feedback Linearisation technique and Control Allocator. Recovery strategy in case of fault or failure has been covered extensively for conventional quadrotors, but very limited known work for tilt-quadrotor. This review concludes that the system dynamic modelling is relatively well covered compared to exploring new control techniques for more stringent requirements. However, recovery strategies as the main advantage of tilt-quadrotor platforms are not explored extensively and require more research attention.

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.

Urban search and rescue missions require rapid intervention to locate victims and survivors in the affected environments. To facilitate this activity, Unmanned Aerial Vehicles (UAVs) have been recently used to explore the environment and locate possible victims. In this paper, a UAV equipped with multiple complementary sensors is used to detect the presence of a human in an unknown environment. A novel human localization approach in unknown environments is proposed that merges information gathered from deep-learning-based human detection, wireless signal mapping, and thermal signature mapping to build an accurate global human location map. A next-best-view (NBV) approach with a proposed multi-objective utility function is used to iteratively evaluate the map to locate the presence of humans rapidly. Results demonstrate that the proposed strategy outperforms other methods in several performance measures such as the number of iterations, entropy reduction, and traveled distance.

This paper explores the application of a real-time dynamic programming (RTDP) algorithm to transport a payload using a multi-rotor unmanned aerial vehicle (UAV) in order to optimize journey time and energy consumption. The RTDP algorithm is developed by discretizing the journey into distance interval horizons and applying the RTDP sweep to the current horizon to get the optimal velocity decision. RTDP sweep requires the current state of the UAV to generate the next best velocity decision. To the best of the authors knowledge, this is the first time that such real-time optimization algorithm is applied to multi-rotor based transportation. The algorithm was first tested in simulations and then experiments were performed. The results show the effectiveness and applicability of the proposed algorithm.

This paper presents a strategy to autonomously explore unknown indoor environments, focusing on 3D mapping of the environment and performing grid level semantic labeling to identify all available objects. Unlike conventional exploration techniques that utilize geometric heuristics and information gain theory on an occupancy grid map, the work presented in this paper considers semantic information, such as the class of objects, in order to gear the exploration towards environmental segmentation and object labeling. The proposed approach utilizes deep learning to map 2D semantically segmented images into 3D semantic point clouds that encapsulate both occupancy and semantic annotations. A next-best-view exploration algorithm is employed to iteratively explore and label all the objects in the environment using a novel utility function that balances exploration and semantic object labeling. The proposed strategy was evaluated in a realistically simulated indoor environment, and results were benchmarked against other exploration strategies.

In this paper, we introduce a coverage path planning algorithm with adaptive viewpoint sampling to construct accurate 3D models of complex large structures using Unmanned Aerial Vehicle (UAV). The developed algorithm, Adaptive Search Space Coverage Path Planner (ASSCPP), utilizes an existing 3D reference model of the complex structure and the onboard sensors' noise models to generate paths that are evaluated based on the traveling distance and the quality of the model. The algorithm generates a set of viewpoints by performing adaptive sampling that directs the search towards areas with low accuracy and low coverage. The algorithm predicts the coverage percentage obtained by following the generated coverage path using the reference model. A set of experiments were conducted in real and simulated environments with structures of different complexities to test the validity of the proposed algorithm.

Edge devices often have to processdata at low power and would benefit from being adaptable. Given that the data coming into these devices is generally unlabeled, unsupervised training on these devices is beneficial. This paper examines a low power approach to implement the winner takes all algorithm, for self-organizing maps through a memristor crossbar based circuit. A novel neuron circuit is designed for the winning neuron detection and lateral inhibition operations. Our experimental results show that the proposed system can self-organize based on unlabeled training data. The proposed design was around 0.002mm$^{\mathbf{2\, $textbf{{in area and consumed about 0.2mW of power. When compared to a CPU, the design had a higher error rate, but was 100 times faster and consumed much lower area and power. Thus when area or power reduction are crucially important, this approach is quite viable.

Custom low power hardware for network intrusion detection is in great demand, as this would allow for efficient security in battery-powered network devices. This paper presents a memristor crossbar based Multilayer Perceptron (MLP) capable of performing network intrusion detection. The MLP is trained based on the Knowledge Discovery and Datamining (KDD) dataset. Once the network is trained, the weight values are programmed into a simulated memristor crossbar hardware system to show that custom circuitry can be designed to perform on-chip intrusion detection. We show the proposed memristor system is capable of achieving greater than 99% intrusion detection accuracy if the memristors utilized are able to store at least 4 unique values (essentially acting as 2-bit devices). Furthermore, we present a number of studies that quantify the amount of classification accuracy lost due to analog processing and amplification in the signal path. This circuit is capable of tolerating significant amplifier gain error (up to 30%) before classification accuracy drops below 99%.

Nowadays, multi-rotor Unmanned Aerial Vehicles (UAVs) are deployed widely in different fields to serve multiple purposes. Safety and reliability of multi-rotor system are rapidly growing research area, and enhancing the control design to account for system failure. In this paper, the Fault-Tolerance Control (FTC) is introduced as a method for recovery from failure in quad-rotor UAV actuator in hovering mode. The ultimate aim is to land safely with minimal damage. This research focuses on designing a FTC method that is capable of handling one and/or two rotors failures. Two different controllers are designed and implemented in simulation for controlling the UAV which are Proportional-Integral-Derivative (PID) control and Linear Quadratic Regulator (LQR). LQR shows its effectiveness after comparing both methods responses. Furthermore, a state observer is designed using Linear Quadratic Estimator (LQE). Gain scheduling (GS) method is developed to switch between the controllers of the system in the event of failure. The GS-PID control design is validated in ROS/Gazebo using the new Robotics System Toolbox showing the effectiveness of the proposed method.

Advancements in robotics and autonomous systems are being deployed nowadays in many application domains such as search and rescue, industrial automation, domestic services and healthcare. These systems are developed to tackle tasks in some of the most challenging, labour intensive and dangerous environments. Inspecting structures (e.g. bridges, buildings, ships, wind turbines and aircrafts) is considered a hard task for humans to perform and of critical importance since missing any details could affect the structure’s performance and integrity. Additionally, structure inspection is time and resource intensive and should be performed as efficiently and accurately as possible. Inspecting various structures has been reported in the literature using different robotic platforms to: inspect difficult to reach areas and detect various types of faults and anomalies. Typically, inspection missions involve performing three main tasks: coverage path planning, shape, model or surface reconstruction and the actual inspection of the structure. Coverage path planning ensures the generation of an optimized path that guarantees the complete coverage of the structure of interest in order to gather highly accurate information to be used for shape/model reconstruction. This article aims to provide an overview of the recent work and breakthroughs in the field of coverage path planning and model reconstruction, with focus on 3D reconstruction, for the purpose of robotic inspection.

In this paper, we introduce a coverage path planning algorithm for inspecting large structures optimized to generate highly accurate 3D models. Robotic inspection of structures such as aircrafts, bridges and buildings, is considered a critical task since missing any detail could affect the performance and integrity of the structures. Additionally, it is a time and resource intensive task that should be performed as efficiently and accurately as possible. The method we propose is a model based coverage path planning approach that generates an optimized path that passes through a set of admissible waypoints to cover a complex structure. The coverage path planning algorithm is developed with a heuristic reward function that exploits our knowledge of the structure mesh model, and the UAV's onboard sensors' models to generate optimal paths that maximizes coverage and accuracy, and minimizes distance travelled. Moreover, we accelerated critical components of the algorithm utilizing the Graphics Processing Unit (GPU) parallel architecture. A set of experiments were conducted in a simulated environment to test the validity of the proposed algorithm.