Inertial sensing estimation methods allows human motion tracking in the absence of optical tracking and joint encoders, but the methods are rather developed for quasistatic motion due to the limited motion capability of humanoids. This paper presents a new method that tracks highly dynamic motion using Inertial Measurement Unit (IMU) measurements. Unlike conventional methods dependent on quasistatic motion for inclination correction with the measured gravity vector, the proposed method uses accelerometers to correct the rotational rate. This is achieved by placing sensors on the ends of links, and converting the acceleration measured at the ends to angular rate based on centrifugal forces. Measuring human motions of low and high intensities is used to identify any strengths and weaknesses of the proposed method with different applications. The proposed technique maintains an acceptable error for both quasistatic and highly dynamic motions and can be used to accurately visualize measured motions.

This paper presents an approach for the localization of an Unmanned Underwater Vehicle (UUV) in a cooperative team with a tethered Unmanned Surface Vehicle (USV). For the localization, the UUV and the USV carry a camera and a sonar respectively to observe each other. The vehicle states are split between Extended Kalman Filter and grid-based estimators based on which sensors provide Gaussian or non-Gaussian observations of each state. Specifically, the horizontal position of the UUV is estimated using a grid-based method because the camera and sonar that observe these states provide non-Gaussian observations when they cannot detect their target. Additionally, the tether to the USV is treated as a non-Gaussian observation that prevents unbounded error growth. Validation of the technique was performed in simulations using sensor models developed based on testing in a lake and pool.

This paper presents a novel strategy for offline estimation of the spatial motion of a multi-link mechanism using Inertial Measurement Unit (IMU) sensors. Accelerometers, gyroscopes and magnetometers are strategically mounted and modeled to maximize measurement accuracy through the past work of inertial sensor fusion. The core contribution of this paper is the development of the Factor Graph Optimization (FGO) Preconditioned by the Extended Kalman Filter (EKF), which is termed FGOPreEKF in this paper, and its integration with the inertial sensor fusion. Since the online EKF efficiently derives the initial guess using the same motion and sensor models, the FGO estimates the motion of a multi-link mechanism efficiently and accurately. The proposed approach was experimentally validated on a two-link system mounted on a fast-moving linear axis, demonstrating superior accuracy compared to standalone EKF or FGO. These results demonstrate the potential of this approach for estimating multi-link motion in more complex scenarios.

This paper presents a method of motion tracking of highly dynamic multi-link systems with Inertial Measurement Units (IMU) embedded with magnetometers in spaces with nonuniform magnetic fields. As IMUs may accumulate error due to drift, magnetometers are used to correct orientation estimation. While magnetic distortion is typically compensated for at a point with magnetic field uniformity and assumed constant through spaces, this method introduces real-time calibration using two oppositely facing triaxial magnetometers and a triaxial accelerometer and gyroscope. Using the described array of magnetometers, local hard iron distortion can be measured at any position. Along with the inconsistent distortion throughout a nonuniform space, the measured direction of magnetic north may also change. To compensate for the inconsistency, a sensor model using the last measured magnetic north direction as the heading is used, rather than assuming a constant magnetic north direction like conventional methods. The proposed sensor suite and model reduces the error by half an order of magnitude less than conventional magnetometer usage. With the proposed method, drift is corrected for, even in nonuniform magnetic fields.

This paper presents a data-driven multiple model framework for estimating the intention of a target from observations. Multiple model (MM) state estimation methods have been extensively used for intention estimation by mapping one intention to one dynamic model assuming one-to-one relations. However, intentions are subjective to humans and it is difficult to establish the one-to-one relations explicitly. The proposed framework infers the multiple-to-multiple relations between intentions and models directly from observations that are labeled with intentions. For intention estimation, both the relations and model probabilities of an Interacting Multiple Model (IMM) state estimation approach are integrated into a recursive Bayesian framework. Taking advantage of the inferred multiple-to-multiple relations, the framework incorpo-rates more accurate relations and avoids following the strict one-to-one relations. Numerical and real experiments were performed to investigate the framework through the intention estimation of a maneuvered quadrotor. Results show higher estimation accuracy and superior flexibility in designing mod-els over the conventional approach that assumes one-to-one relations.

Autonomous robots need to recognize the environment by identifying the scene. Scan context is one of global descriptors, and it encodes the three-dimensional scan data of the scene for the identification in a matrix form. Scan context is in a matrix form that is simple to store, but the matching of scan contexts can require computational effort because the descriptor is orientation-dependent. Because a scan context of an omnidirectional LiDAR scan becomes periodic in azimuth, this paper proposes to compute the scan context matching efficiently incorporating the cross-correlation with fast Fourier transform, and, hence, the method is named fast scan context matching. The effectiveness of the proposed method for computation time, accuracy, and robustness are reported in this paper. It is also shown that the method was also tested as a loop closure detector of a SLAM package as a practical application and that the proposed method outperformed the conventional scan context matching.

This paper presents a technique that allows a robot to escort a human to their destination. Unlike tracking where the robot follows the human from behind, the proposed technique locates the robot in front of the human by incorporating human intention in addition to conventional motion prediction. Human head pose is used as an effective past-proven implicit indicator of intention. A particle filter allows accurate estimation and prediction of the non-Gaussian human trajectory. The predicted pose from both the human motion and intention determines the robot control action which leads to efficient autonomous escorting. Experimental analysis shows that the incorporation of the proposed human intention model reduces human position prediction error by approximately 40% when turning. Experimental validation with an omnidirectional mobile robotic platform shows successful and effective escorting compared to the conventional techniques.

This paper presents a technique for state estimation of a multi-link system having no joint encoders, which can only be partially observed by a camera. To fully observe the system without changing the current configuration, a gyroscope and an accelerometer are attached to each link as dead-reckoning sensors. Observations of the dead-reckoning sensors are associated with the states of the multi-link system such that the states are fully observable. The camera, which observes part of the system globally, is used as a global corrector in the framework of an extended Kalman filter to filter the dead-reckoning errors accumulated over time. Parametric studies in simulation have investigated and identified the efficacy of the proposed technique in estimating the state of the multilink system. Experimental validation using a two-link arm has demonstrated the applicability of the proposed technique to real-world multi-link systems.

This paper presents a vision-based technique and a system developed for the global reconstruction of three-dimensional (3-D) road surfaces. Using the system, the technique globally reconstructs 3-D road surfaces by estimating the global camera pose using the Adaptive Extended Kalman Filter (AEKF) and integrating it with existing local road surface reconstruction techniques. The AEKF adaptively updates the covariance of uncertainties such that the estimation works well even in environments with varying uncertainties. Numerical results show the efficacy of the proposed technique over the Extended Kalman Filter (EKF)-based technique by 50% in accuracy, and the on-road test has demonstrated the ability of the proposed technique for the real-world global 3-D road surface reconstruction.

This paper proposes a probabilistic framework for classification of evolving targets, leveraging the principles of recursive Bayesian estimation in a perception-oriented context. By implementing a Gaussian toroid prediction model of the perception target's evolution, the proposed recursive Bayesian classification (RBC) scheme provides probabilistically robust classification. Appropriate features are extracted from the target, which is then probabilistically represented in a belief space. This approach is capable of handling high-dimensional belief spaces, while simultaneously allowing for multi-Gaussian representation of belief without computational complexity that hinders real-time analysis. The proposed technique is validated over several parameter values by thousands of simulated experiments, where it is shown to outperform naıve classification when high observational uncertainty is present.

This paper proposes a belief-driven control policy of a drone with microphones for multiple sound source search. As the sound source localization by drones is uncertain because of the observation significantly distorted by noise such as rotor noise, the belief on the estimated targets may consist of multiple peaks that are spread over the bounded search area. The proposed control policy is formulated with a robust cost function so that the function encodes the search mission properly. A peak management mechanism is additionally introduced to keep tracking all targets by masking sufficiently observed and well estimated targets whose peaks normally become steep and high. The proposed control policy was evaluated by numerical simulations, and experiments, and those results have validated the efficacy of the proposed control policy.

This paper presents a framework for engaging a target while approaching it from a long distance, using observation from sensors on-board a mobile robot. The proposed framework consists of two multi-stage Bayesian approaches to reliably detect and accurately engage with the target under uncertainties. The multi-stage localization approach localizes the robot and the target in a global coordinate frame. Their locations are estimated sequentially when the robot is at a long distance from the target, whereas they are localized simultaneously when the target is in the close vicinity. In the multi-stage target observation approach, a level of confidence and the associated probability of detection of the sensor are defined to make the target detectable in maximal occasions. This allows the extended Kalman filter to be implemented for the target engagement. The proposed framework was implemented on an unmanned ground vehicle equipped with multiple sensors. Results show the effectiveness of the proposed framework in solving real-world problems.

This paper presents a technique for localizing road surface images acquired by a downward-facing monocular camera on a vehicle with sparse low-accuracy Global Positioning System (GPS) readings. Images are collected by reading vehicle speed through on-board diagnostics (OBD) such that distance between two neighboring images is constant. The images are then stitched to create the road surface of arbitrary length. Lastly, the three-dimensional (3-D) road surface is created and globally corrected by using the GPS and the elevation map as well as the Adaptive Extended Kalman Filter (AEKF). The advantage of this technique is the possible deployment of a sparse low-accuracy GPS due to the use of the adaptive version of Extended Kalman Filter (EKF). The proposed technique was used for localization of local roads and highways of 6.9 km total length in Blacksburg, VA. The results of the localization show the reconstructed 3-D differ from the satellite imagery data only by 7.97%.

This paper presents a generalized multi-stage Bayesian approach for an unmanned aerial vehicle to estimate the location of a mobile target. The major hardware components of the proposed approach are a camera with a fisheye lens and another camera with a normal lens and a pan/tilt unit. With wide angle of view (AOV), the fisheye lens camera first detects the bearing of the target, and the PT camera next captures the target in its AOV. The recursive Bayesian estimation steadily locates the target in a globally defined space. The paper also proposes a multi-stage detection method for the fisheye lens camera. The level of confidence is defined in association with the probability of detection (POD) for each detection technique, and the fisheye lens enables continuous detection by gradually increasing the POD. The observation likelihood is finally derived from the POD in a generalized manner. The proposed approach was applied to the detection of a mobile target by a multi-rotor helicopter, and results have demonstrated the effectiveness of both the proposed multi-stage Bayesian approach and multi-stage fisheye lens detection method.

This paper presents an algorithm for localizing an unmanned aerial vehicle (UAV) in GPS denied environments. Localization is performed with respect to a pre-built map of the environment represented using the distance function of a binary mosaic, avoiding the need for extraction and explicit matching of visual features. Edges extracted from images acquired by an on-board camera are projected to the map to compute an error metric that indicates the misalignment between the predicted and true pose of the UAV. A constrained extended Kalman filter (EKF) framework is used to generate an estimate of the full 6-DOF location of the UAV by enforcing the condition that the distance function values are zero when there is no misalignment. Use of an EKF also makes it possible to seamlessly incorporate information from any other system on the UAV, for example, from its auto-pilot, a height sensor or an optical flow sensor. Experiments using a hexarotor UAV both in a simulation environment and in the field are presented to demonstrate the effectiveness of the proposed algorithm.

This paper describes a non-field-of-view (NFOV) localization approach for a mobile robot in an unknown environment based on an acoustic signal combined with the geometrical information from an optical sensor. The approach estimates the location of a target through the mobile robot's sensor observation frame, which consists of a combination of diffraction and reflection acoustic signals and a 3-D environment geometrical description. This fusion of audio-visual sensor observation likelihoods allows the robot to estimate the NFOV target. The diffraction and reflection observations from the microphone array generate the acoustic joint observation likelihood. The observed geometry also determines far-field or near-field acoustic conditions to improve the estimation of the sound direction of arrival. A mobile robot equipped with a microphone array and an RGB-D sensor was tested in a controlled environment, an anechoic chamber, to demonstrate the NFOV localization capabilities. This resulted in +/- 18 degrees, and less than 0.75 m error in angle and distance estimation, respectively.

This paper presents non-field-of-view (NFOV) target estimation incorporating an acoustic sensor, which consists of two microphones. The proposed approach derives the interaural level difference (ILD) of observations from the two microphones for different target positions and stores the ILDs as database a priori. Given a new acoustic observation on a target, an acoustic observation likelihood is created by calculating the correlation of the ILD of the new observation to the stored ILDs. A joint observation likelihood is then developed by fusing the optical and acoustic observation likelihoods, and the recursive Bayesian estimation updates and maintains belief on the target using the joint observation likelihood. The proposed approach detects a target positively using an acoustic sensor even if it is outside the field of view of the optical sensor and localizes the target accurately by estimating it within the RBE. The efficacy of the proposed approach was first validated by experimental studies. Further numerical demonstrations then show the applicability of the proposed approach to the NFOV target estimation.

This paper presents the parallelization of grid-based recursive Bayesian estimation (RBE) using a graphics processing unit (GPU) for real-time control of autonomous vehicles. Although the grid-based method has been effectively used for autonomous search due to its ability to represent search space explicitly, heavy computational load has been a bottleneck for real-time application similarly to other non-Gaussian RBE techniques. The proposed RBE, which parallelizes grid-wise computations using GPU upon the analysis of mathematical operations, removes sequential processes and accelerates RBE significantly. Numerical examples have first demonstrated the validation of the proposed RBE and investigated its performance through parametric studies. The proposed RBE was then applied to the cooperative search by autonomous unmanned ground vehicles (UGVs), and its real-time capability has been demonstrated.

Flapping wings have the potential to revolutionize the field of Micro Aerial Vehicles (MAVs), however the effect of flapping motion on the performance of such wings has not been studied in detail. This paper presents a parametric study of flapping wing propulsion, using two types of passive flapping wings and three flapping motions. Each combination of wing type and flapping motion was tested over a range of amplitudes and frequencies ranging from 1.4-36deg and 5-50 Hz respectively. Wing performance was evaluated by measuring lift force and mechanical efficiency for all tests. The performance of Wing A was found to be significantly higher than Wing B, with up to twice the maximum lift and efficiency. Overall, Wing A with the triangular flapping motion was found to be the most suitable for MAVs. This research has the potential to significantly improve the performance of flapping wing propulsion, resulting in new capabilities and applications for MAVs.

This paper presents the updateable probabilistic evacuation modeling (UPEM) technique, which allows sensor observation data to be included in the problem of estimating the state of an evacuating crowd, as the data are obtained. Each individual is modeled as a Newtonian particle which interacts with obstacles, such as walls and other individuals. The UPEM technique estimates not only the general trend of the crowd as a whole, but also the specific states of each of the evacuees in the crowd. Furthermore, an approach to cooperative autonomous searching in crowded urban emergencies is developed using UPEM. A number of simulated searches in emergency evacuations highlight the efficacy of the technique in reducing the time required to detect targets and in increasing the level of safety for human evacuees.

This paper presents the design and evaluation of a system for motion control and analysis of high speed passively twisting flapping wings. The developed system is capable of flapping the wing under test with several controlled waveforms at frequencies up to 30 Hz, while capturing data about wing motion, lift force, and angle of attack. Performance was tested with sinusoidal, triangular, trapezoidal and square waveforms, with average position error below 5% for all cases up to and including 25 Hz. Wing control and data capture were found to be of high accuracy, and measurements showed significant differences in wing performance with different flapping motions, indicating a need for continuation of this research.

This paper presents the element-based method, which can be used for recursive Bayesian estimation (RBE) in robotic operations such as search and tracking involving moving targets. The use of shape functions to define a set of irregularly shaped elements allows the target PDF to be continuously, and thus accurately, represented over the target space. A comparison with the grid-based method first shows that the element-based method requires less than 10% of the number of nodes to achieve the same accuracy. The application of the element-based method to marine search-and-rescue (SAR) scenarios then demonstrates its ability for effective SAR whilst maintaining collected information.

This paper presents a search-and-capture (SAC) problem where multiple autonomous pursuer vehicles are deployed to capture evaders using the particle filter (PF). The PDFs of the evaders states are first represented as discrete sets of support vectors (particles). Using this representation, a coordinated SAC strategy is proposed by firstly defining the observation likelihoods for both detection and non-detection in the PF framework. Coordination is then achieved through the transmission of the likelihood function parameters of each pursuer to other pursuers to form combined observation likelihoods (COLs) followed by the derivation and sharing of weighted expected states (WESs) from the updated PDFs to provide control reference points for the pursuers. The proposed strategy is applied to two scenarios: first to multiple-pursuers single-evader and secondly to multiple-pursuer multiple-evaders. Results show that the proposed strategy allows the pursuers to successfully detect and capture the evaders in both scenarios

This paper proposes a control strategy based on forward reachable sets (FRSs) analysis for multiple pursuers to capture an evader with a higher maneuverability than the pursuers. The strategy first finds the pursuers' capture states at which their FRSs cover the evader's entire FRS so as to improve the possibility of capture. The pursuers approach these capture states from their initial states in feed-forward control at a low sampling rate. Upon reaching the capture states, the pursuers approach and capture the evader in a feedback manner. The proposed strategy was applied to two pursuit-evasion scenarios and compared to the generic motion tracking algorithm. Results show the efficacy of the proposed strategy and its superiority to the motion tracking algorithm

This paper presents a coordinated control technique that allows heterogeneous vehicles to autonomously search for and track multiple targets using recursive Bayesian filtering. A unified sensor model and a unified objective function are proposed to enable search-and-tracking (SAT) within the recursive Bayesian filter framework. The strength of the proposed technique is that a vehicle can switch its task mode between search and tracking while maintaining and using information collected during the operation. Numerical results first show the effectiveness of the proposed technique when a found target becomes lost and must be searched for again. The proposed technique was then applied to a practical marine search-and-rescue (SAR) scenario where heterogeneous vehicles coordinated to search for and track multiple targets. The result demonstrates the applicability of the technique to real search world scenarios

This paper presents a feedforward control strategy for a robotic manipulator based on a belief function. The belief about a target's next location, as described by a probability density function, is maintained by a recursive Bayesian process that fuses observations with a target motion model. A sensor model that incorporates positive and negative sensor readings allows the single belief function to be used to deliver both searching and tracking behaviors. Constrained non-linear optimization is used to search configuration space for the control action that maximizes the subsequent probability of detection. To demonstrate application of the technique, a simple example is elaborated for a searching and tracking task with an eye-in-hand sensor

This paper presents a Bayesian approach to the problem of searching for multiple lost targets in a dynamic environment by a team of autonomous sensor platforms. The probability density function (PDF) for each individual target location is accurately maintained by an independent instance of a general Bayesian filter. The team utility for the search vehicles trajectories is given by the sum of the `cumulative' probability of detection for each target. A dual-objective switching function is also introduced to direct the search towards the mode of the nearest target PDF when the utility becomes too low in a region to distinguish between trajectories. Simulation results for both clustered and isolated targets demonstrate the effectiveness of the proposed search strategy for multiple targets.

This paper provides a solution to the optimal trajectory planning problem in target localisation for multiple heterogeneous robots with bearing-only sensors. The objective here is to find robot trajectories that maximise the accuracy of the locations of the targets at a prescribed terminal time. The trajectory planning is formulated as an optimal control problem for a nonlinear system with a gradually identified model and then solved using nonlinear model predictive control (MPC). The solution to the MPC optimisation problem is computed through exhaustive expansion tree search (EETS) plus sequential quadratic programming (SQP). Simulations were conducted using the proposed methods. Results show that EETS alone performs considerably faster than EETS+SQP with only minor differences in information gain, and that a centralised approach outperforms a decentralised one in terms of information gain. We show that a centralised EETS provides a near optimal solution. We also demonstrate the significance of using a matrix to represent the information gathered.

This paper deals with the problem of coordinating a team of mobile sensor platforms searching for a single mobile non-evading target. It follows the general Bayesian active sensor network approach introduced in [2] where each decision maker plans locally based on an equivalent representation of the target state probability density function (PDF). This paper focuses on the prediction stage of the decentralized Bayesian filter. It looks at how different types of realistic external constraints may affect the target motion and how they may be taken into account in the process model. Two general classes of constraints are identified soft and hard. A few constraint examples from each class are given to illustrate their impact on the evolution of the target state PDF. Multiple constraints of various types can be combined to increase the accuracy of the predicted PDF estimate, thus affecting the individual trajectories of the search platforms. The effectiveness of the framework is demonstrated for a team of airborne search vehicles looking for a drifting target lost in a storm at sea.

This paper presents the real-time control of cooperative unmanned air vehicles (UAV) that dynamically engage multiple targets in a time-optimal manner. Techniques to dynamically allocate vehicles to targets and to subsequently find the time-optimal control actions are proposed. The decentralization of the proposed control strategy is further presented such that the vehicles can be controlled in real-time without significant time delay. The proposed strategy is men applied to various practical battlefield problems, and numerical results show the efficiency of the proposed strategy.

This paper presents a control strategy for the pursuer in the pursuit-evasion game problem when the evader behaves intelligently. The pursuer in the proposed technique does not try to react to the evader's behavior instantaneously. The proposed technique therefore does not yield instantaneous optimality but capture the evader in a time-efficient and robust fashion even when the evader is intelligent. The proposed technique was applied to two numerical examples and the results were compared to those by the conventional motion tracking algorithms. The results and comparison show that the proposed technique could capture the evader faster than the conventional motion tracking algorithms in both the examples.

A special gripper has been designed to pick apples from trees in an apple orchard. It is a low cost gripper with highly capability to pick an apple without scratching its skin. The gripper has been designed in accordance to the apple orchard environment and robot specification. Furthermore, the experiments show that the gripper can complete all tasks properly. The gripper was mounted on a manipulator and tested in an indoor laboratory environment. Experimental results show that the gripper could successfully pick apples.

This paper addresses the problem of coordinating a team of multiple heterogeneous sensing platforms searching for a single lost target. In this approach, the utility of a control sequence is a function of the probability density function (PDF) of the target state. Each decision maker builds an equivalent estimate of this PDF by communicating and fusing the information from the other sensor nodes. Coupled utilities incite the agents to collaborate and to agree on the next best set of actions. Decentralized cooperative planning is achieved via anonymous negotiation based on communication of expected observed information. Simulation results demonstrate the efficiency of the cooperative trajectories for a team of autonomous airborne search vehicles.

This paper presents a formulation and solution of the time-optimal control of multiple cooperative robot vehicles. In particular, a group of robot vehicles reaching a terminal state in absolute and/or relative formations in minimum time is addressed. A canonical formulation of the problem is first derived and a numerical technique, which can effectively solve this class of problems, is then proposed. Numerical results are then presented to demonstrate the efficacy of the proposed formulation and method of solution. The techniques described offer a practical solution to the problem of building and controlling formations of cooperative autonomous vehicles in real-time.

This paper describes a decentralized Bayesian approach to coordinating multiple autonomous sensor platforms searching for a single non-evading target. In this architecture, each decision maker builds an equivalent representation of the target state PDF through a Bayesian DDF network enabling him or her to coordinate their actions without exchanging any information about their plans. The advantage of the approach is that a high degree of scalability and real time adaptability can be achieved. The effectiveness of the approach is demonstrated in different scenarios by implementing the framework for a team of airborne search vehicles looking for a stationary, and a drifting target lost at sea.

This paper presents a solution to the real-time control of cooperative unmanned air vehicles (UAVs) that engage multiple targets in a time-optimal manner. Techniques to dynamically allocate vehicles to targets and to find the time-optimal control actions of vehicles are proposed. The effectiveness of the time-optimal control technique is first demonstrated through numerical examples. The proposed strategy is then applied to a practical battlefield problem where ten vehicles are required to engage four targets, and numerical results show the efficiency of the proposed strategy.