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This paper studies a class of distributed attack strategies against state estimation of wireless sensor networks. The attack objective is to corrupt the least numbers of sensors so that the state estimate error approaches to a target bias while avoiding being detected and a high attacking lost. First, with precise knowledge of measurement model and applying a sparsity projection operation with average linear complexity rather than the popular brute force search and approximation techniques, a distributed optimization scheme is proposed which provides to an exact and low-complexity algorithmic solution to finding the optimal attack strategy. Furthermore, by introducing dead-zone type projection and region projection operators, a distributed optimization algorithm for robust sparse undetectable attacks is proposed with imprecise model knowledge. A distinguishing point of the proposed algorithms is that a trade-off between computational complexity and attacks impact can be achieved by properly designing a cost function. Simulation results on an unmanned ground vehicle are presented to substantiate the algorithm.

The driving dynamics of skid-steered vehicles are difficult to model due to their inherent need for loss of traction for curvilinear motion, which leads to complex wheel-ground interactions. However, such vehicles represent well suited platforms for automated robots with their robust, cost-effective, low maintenance construction and their great off-road performance. One usecase of them is mobile mapping. Such systems can greatly benefit from precise driving dynamic models for pose estimation for both georeferencing measurements and navigation as well as for system control using model predictive control methods.System identification is a field of applied mathematics for estimating models of dynamical systems based on measured input and output data of the system. The SINDY (Sparse Identification of Nonlinear DYnamics) algorithm is a method that utilizes sparse regression to identify interpretable, parsimonious models in state-space representation, that balance model performance with complexity.In this thesis the suitability of the SINDY algorithm to conduct system dentification for the driving dynamics of the Clearpath Husky A200 based on geodetic measurements was ascertained. The Husky A200 is a medium sized robot for research and prototyping and represents an example of skid-steered unmanned ground vehicle (UGV) well suited for tasks such as mobile mapping. A measurement setup around two laser trackers for collecting the necessary data was created, addressing the challenges of time synchronisation of the different system components and maintaining an uninterrupted line of sight between the laser trackers and their target prism during driving operations.A preprocessing pipeline to calculate the system identification input data was established, accomplishing time synchronisation, pose calculation and interpolation as well as state vector calculation and transformation. The system identification was successfully conducted, utilizing the integral notation of SINDY and employing an extensive hyperparameter tuning. The point position estimation uncertainty of the best performing model was 14 cm after a 5 second integration period, with the heading estimation uncertainty being 4.7◦. These results demonstrate the suitability of identified systems for example for certain applications of state estimation. Potential shortcomings and areas for improvements of the presented measurement setup and methodology were identified and discussed.

Unmanned Aerial Vehicle/Unmanned Ground Vehicle heterogeneous collaborative system can complete complex tasks. In order to monitor and control the two agents simultaneously, a comprehensive ground station is designed. First, the preliminary work is introduced. Then, the architecture of the ground station of the system is proposed. In data communication module, since the amount of data from the system is large, the data of structure and multi-threaded processing are designed in detail. In data display module, the ground station provides various ways for displaying data. In image display module, in order to balance the accuracy and real-time performance of the system, an Efficient Convolution Operators tracking algorithm is improved for tracking moving target. In digital map module, a three-dimensional path planning method based on improved Ant Colony is proposed. The experiment results show that the ground station works well in real-time performance and accuracy.

This article proposes a robust and precise localization scheme for unmanned ground vehicle (UGV) in global positioning system (GPS)-denied and GPS-challenged environments via multisensor fusion approach. The localization scheme is proposed to be under an available point-cloud map. First, initialization in localization module is designed to calculate the initial position of UGV in map using the Gaussian projection approach and obtain the frame transformation between the 3-D lidar and the inertial measurement unit (IMU). Second, the best alignment between each scan frame and the available submap is obtained, and the pose of vehicle relative to the origin of map is calculated. Third, the precise pose of UGV is well predicted by integrating the data from 3-D lidar and IMU. Fourth, in order to visually verify the proposed localization scheme, the motion of vehicle is visualized by designing the visualization module. Note that the preprocessing module aims to process the raw scan data. The availability of our proposed localization scheme is verified by conducting experiments in our campus.

The vision-aided inertial navigation system is of vital importance and great urgency to guarantee accurate and reliable navigation information for unmanned ground vehicles (UGV) in Global Navigation Satellite System-challenged/denied environments. Yet, it is almost inevitable to occur unpredictable stochastic disturbances caused by ego-motion uncertainty due to uneven pavements, unstructured and dynamic scenes, which may lead to the divergence of estimation algorithms and the unreliability of positioning. In this article, we aim at developing a robust strategy for vision-aided inertial navigation to resist uncertainties, and modifying unscented Kalman filter by formulating a novel adaptive factor according to the degree of abnormity (DoA). Applying the robust strategy and algorithms, a monocular camera and an inertial measurement unit (IMU) are tightly fused to construct a robust vision-aided inertial navigation system. During the fusion process of integrated IMU and camera, the new method can automatically estimate the DoA of UGV ego-motion uncertainty online, considering the relationship between the factual and estimated innovation covariances. Thus, we can adaptively adjust the estimation error covariance matrix and the Kalman gain for different scenarios on the basis of DoA levels. As the modification is made in the framework of Kalman filter, the real-time performance, robustness, and high-accuracy positioning can be achieved using the proposed adjustment strategy. Experiments with the KITTI dataset are carried out under different road conditions. The experimental results show that the robust vision-aided inertial navigation system possesses good robustness and self-adaptation for protection against ego-motion uncertainty, and can improve the positioning accuracy of UGV.

Researchers around the world are developing autonomous robot systems to assist humans with tedious, hazardous, complex, and time-consuming tasks. Today, autonomous robots are used for a variety of applications, ranging from delivering food to operating on patients. A robotic system can be operated with or without human assistance, depending on whether it is manned or unmanned. Additionally, the robot may be classified as an Unmanned Aerial Vehicle (UAV), Unmanned Surface Vehicle (USV), or Unmanned Ground Vehicle (UGV), depending on its operational conditions. An autonomous robot may be equipped with manipulators and advanced sensors, such as a LiDAR camera, which can collect 3D spatial data. The present study reviews recent research and development of unmanned ground vehicles, including two nature-inspired designed robots called SPOT and DIGIT and presents its technical data. In addition, this study introduces the challenges and applications of these robots. A novel type of comparison was made in this study between some UGVs, human and animal-inspired designed robots, where researchers were able to get an idea of different robot basic specifications that could be used to select these robots for future research. As a result of this study, two more studies will be conducted in the future.

Inhalation therapy is one of the most popular treatments for many pulmonary conditions. The proposed Covid-19 aromatherapy robot is a type of Unmanned Ground Vehicle (UGV) mobile robot that delivers therapeutic vaporized essential oils or drugs needed to prevent or treat Covid-19 infections. It uses four omnidirectional wheels with a controlled speed to possibly move in all directions according to its trajectory. All motors for straight, left, or right directions need to be controlled, or the robot will be off-target. The paper presents omnidirectional four-wheeled robot trajectory tracking control based on PID and odometry. The odometry was used to obtain the robot's position and orientation, creating the global map. PID-based controls are used for three purposes: motor speed control, heading control, and position control. The omnidirectional robot had successfully controlled the movement of its four wheels at low speed on the trajectory tracking with a performance criterion value of 0.1 for the IAEH, 4.0 for MAEH, 0.01 for RMSEH, 0.00 for RMSEXY, and 0.06 for REBS. According to the experiment results, the robot's linear velocity error rate is 2%, with an average test value of 1.3 percent. The robot heading effective error value on all trajectories is 0.6%. The robot's direction can be monitored and be maintained at the planned trajectory. Doi: 10.28991/esj-2021-SPER-13 Full Text: PDF

Autonomous driving of unmanned ground vehicle (UGV) at road intersection is a challenging task due to the complicated traffic conditions. In this paper, an event-triggered model predictive adaptive dynamic programming (MPADP) algorithm is proposed for path planning of UGV at road intersection. Following the critic-actor scheme of adaptive dynamic programming (ADP), cost function approximation and control policy generation are combined to formulate MPADP. The infinite horizon cost function of ADP is stacked over predictive horizon of model predictive control (MPC), and then the infinite horizon cost function is converted to the finite horizon-stacked cost function in MPADP. By minimizing the approximation error within predictive horizon, the approximation accuracy is enhanced. Considering the limitation of energy consumption, the event-triggered mechanism is designed based on the mismatch of cost function approximation. Three triggering conditions are designed, and the corresponding boundedness of approximation error is proved. Simulation results illustrate the effectiveness, efficiency and feasibility in application of the event-triggered MPADP method for path planning at road intersection.

In this paper, the robust stability and disturbance rejection performance analysis of an energy-efficient control is addressed in the framework of Networked Control System (NCS). The control scheme under study integrates periodic event-triggered control, packet-based control, time-varying Kalman filter, dual-rate control and prediction techniques, whose design is aimed at reducing energy consumption and bandwidth usage. The robust stability against time-varying model uncertainties is analyzed by means of a sufficient condition based on Linear Matrix Inequalities (LMI). Finally, the effectiveness of the proposed approach is experimentally validated in a tracking control for an Unmanned Ground Vehicle (UGV), which is a battery-constrained mobile device with limited computation capacities.

This paper investigates the finite-time cooperative formation control problem for a heterogeneous system consisting of an unmanned ground vehicle (UGV) - the leader and an unmanned aerial vehicle (UAV) - the follower. The UAV system under consideration is subject to modeling uncertainties, external disturbance as well as actuator faults simultaneously, which is associated with aerodynamic and gyroscopic effects, payload mass, and other external forces. First, a backstepping controller is developed to stabilize the leader system to track the desired trajectory. Second, a robust nonsingular fast terminal sliding mode surface is designed for UAV and finite-time position control is achieved using terminal sliding mode technique, which ensures the formation error converges to zero in finite time in the presence of actuator faults and other uncertainties. Furthermore, by combining the radial basis function neural networks (NNs) with adaptive virtual parameter technology, a novel NN-based adaptive nonsingular fast terminal sliding formation controller (NN-ANFTSMFC) is developed. By means of the proposed adaptive control strategy, both uncertainties and actuator faults can be compensated without the prior knowledges of the uncertainty bounds and fault information. By using the proposed control schemes, larger actuator faults can be tolerated while eliminating control chattering. In order to realize fast coordinated formation, the expected position trajectory of UAV is composed of the leader position information and the desired relative distance with UGV, based on local distributed theory, in the three-dimensional space. The tracking and formation controllers are proved to be stable by the Lyapunov theory and the simulation results demonstrate the effectiveness of proposed algorithms.

This article proposes a novel modeling and path planning framework to tackle a new problem, named road-network persistent surveillance problem (RPSP), in which the occurrence location and probability of the events are unknown. To capture such events, an unmanned ground vehicle (UGV) with certain detection ability must move along the road-network with different monitoring priorities to persistently monitor them. First, we presented an event-oriented modeling method to formulate the new problem. The monitoring effect of the road-network is quantified by uncertainty, which takes the detection ability of the sensor, the detection interval, and the monitoring weight into account. Based on the proposed model, we then designed a heuristic path planning algorithm from a decision-making perspective, which enables a UGV to persistently monitor the viewpoints of the road-network without traversing them. The simulation results and analysis demonstrate the feasibility and superiority of the proposed approach. Note to Practitioners —This article was motivated by developing an effective solution for unmanned ground vehicle (UGV) to execute road-network persistent surveillance tasks under outdoor circumstances. To the best of our knowledge, there is no other consideration dealing with this problem. To capture spatiotemporal random events, we proposed a new approach that enables UGV to persistently monitor an area with road-network performing in the weighted mode. Distinguished from the traditional path planning methods in the literature, the proposed approach considered the detection ability of the UGV. Consequently, the so-called “area-to-area” planning approach does not require traversing all the viewpoints. The approach can be used in road-network with any topology. Moreover, the difficulty in modeling road-network from the actual environment is degraded, and compared with the existing patrolling methods, the monitoring efficiency of the proposed approach is improved obviously.

Most approaches to robot visual localization rely on local, global visual or semantic information as observation. In this paper, the combination of global visual and semantic information is used as landmark in the observation model of Bayesian filters. Introducing the improved Gaussian Process into observation models with visual information, The GP-Localize algorithm is extended to high dimensional data, which means that all the historical data with spatiotemporal correlation to achieve constant time and memory for persistent outdoor robot localization can be considered by the Bayesian filters. The other contribution of this paper is we combine the all above parts into a system for robot visual localization and apply it to two real-world outdoor datasets including unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV). According to the experimental results, it's no difficult to find there is a higher accuracy while using global vision and semantic results rather than just using single feature. By using the combined features and improved Gaussian process approximation method in Bayes filters, our system is more robust and practical than existing localization systems such as ORB-SLAM.

We present a tracking controller for mobile multi-robot systems based on dual quaternion pose representations applied to formations of robots in a leader-follower configuration, by using a cluster-space state approach. The proposed controller improves system performance with respect to previous works by reducing steady-state tracking errors. The performance is evaluated through experimental field tests with a formation of an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV), as well as a formation of two UAVs.

In a dynamic environment, moving to the destination safely and effectively is of paramount importance for an unmanned ground vehicle (UGV). This article presents a strategy of trajectory planning and tracking that aims to ensure the UGV’s safety in an uncertain environment. Specifically, based on the initial environment information, a global optimal trajectory connecting the start and the destination is predefined by an artificial fish swarm algorithm (AFSA). In the presence of unforeseen obstacles, a trial-based forward search (TFS) algorithm based on the Markov chain is proposed in the local trajectory planning module, while collision prediction is integrated as heuristic information. The vehicle’s current state is updated accordingly for the sake of avoiding entire state spaces involved in the computation. Therefore, the storage efficiency and convergence rate in local path planning are sufficiently enhanced in comparison to dynamic programming. Moreover, command signals can be calculated with the proposed multiconstrained model predictive controller (MMPC), ensuring the vehicle to track the reference trajectory and smoothen the motion. Finally, the results in both simulations and experiments reveal the effectiveness of the proposed algorithm in the presence of both static and dynamic obstacles. Note to Practitioners —This article is motivated by the unmanned ground vehicle (UGV) collision avoidance problem in practical missions, such as farming and emergency response. In recent years, various trajectory planning and tracking algorithms have been widely developed. However, the environmental complexity and the intruders’ unexpected movement pose difficulties in trajectory planning, especially in ensuring the computation time under the allowable threshold. Moreover, the UGV practical trajectory tracking is a challenging task which demands a desired response within various physical constraints. In this article, a two-stage conflict resolution system is proposed. First, a trial-based forward search (TFS) is used to generate a new trajectory deviating the UGV from the initially generated trajectory by the artificial fish swarm algorithm (AFSA), aiming to avoid the unforeseen intruders (unknown in prior) in real-time. Using these two trajectory planning algorithms alternatively, both global trajectory optimality in a cluttered environment and appropriate maneuvers with respect to unexpected intruders can be achieved. Subsequently, the UGV is modeled according to its kinematic characteristics, and thus a multiconstrained model predictive controller (MMPC) is designed to follow the reference trajectory. The physical constraints are respected by integrating them into the controller. Simulations and experimental results demonstrate that the proposed strategy can guide and control a UGV from the start to the destination safely and smoothly, even in the case of multiple obstacles with constant or varying velocities. Furthermore, the proposed collision avoidance strategy can be extended to other unmanned systems, including unmanned aerial vehicles and unmanned surface vehicles.

Traditional autonomy among robotic and other artificial agents was accomplished via automated planning methods that found a viable sequence of actions, which, if executed by an agent, would result in the successful completion of the given task(s). However, many tasks that we would like robotic agents to perform involve goals that are complex, poorly-defined, or hard to specify. Furthermore, significant amounts of data or computation are required for agents to reach reasonable performance. As a result, autonomous systems still rely on human operators to play a supervisory role to ensure that robotic operations are completed quickly and successfully. The presented work aims to improve the traditional methods of robot autonomy by developing an intuitive means for(human operators to adapt/mold the behaviors and decision making of autonomous agents) autonomous agents to leverage the flexibility and expertise of human end users. Specifically, this work shows the results of three machine learning-based approaches for modifying and extending established robot navigation behaviors and skills through human demonstration. Our first project combines Imitation learning with classical navigation software to achieve long-horizon planning and navigation that follows navigation rules specified by a human user. We show that this method can adapt a robot's navigation behavior to become more like that of a human demonstrator. Moreover, for a minimal amount of demonstration data, we find that this approach outperforms recent baselines in both navigation success rate and trajectory similarity to the demonstrator. In the second project, we introduce a method of communicating complex skills over a short-horizon task. Specifically, we explore using imitation learning to teach a robot the complex skill needed to safely navigate through negative obstacles in simulation. We find that this proposed method could imitate complex navigation behaviors and generalize to novel environments in simulation with minimal demonstration. Furthermore, we find that this method compares favorably to a classical motion planning algorithm which was modified to assign traversal cost based on the terrain slope local to the robot's current pose. Finally, we demonstrate a practical implementation of the second approach in a real-world environment. We show that the proposed method results in a policy that can generalize across differently shaped obstacles and across simulation and reality. Moreover, we show that the proposed method still outperforms the classical motion planning algorithm when tasked to navigate negative obstacles in the real world. Doctor of Philosophy With the rapid advancement of computing power and growing technical literacy of the general public, the tasks that robots should be able to accomplish have multiplied. Robots can, however, be limited by the human ability to effectively convey how tasks should be performed. For example, autonomous robot navigation to a specified path planning software suite that generates feasible and obstacle-free trajectories through a cluttered environment. 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. The desired tasks and skills are difficult to effectively convey in a machine legible format. These tasks often require technical expertise in multiple mechatronic disciplines and hours of hand tuning that the typical end user does not have. In this dissertation, we examine methods that directly leverage human users to teach robots how to perform tasks that are generally difficult to specify pragmatically. We focus on methods that allow human users to extend established robot navigation behaviors and skills by demonstrating their own preferred approaches. We evaluate the performances of our proposed approaches in terms of navigation success rate, adherence to the demonstrated behavior, and their ability to apply what they have learned to novel environments. Moreover, we showed that our approaches compare favorably to recent machine learning-based approaches to autonomous navigation, and classical navigation techniques with respect to these metrics.