In this paper, we analyze the periodic transmission masks for MASked Modulation (MASM) in half-duplex integrated sensing and communication (ISAC), and derive their closed-form expected range-Doppler response $\mathbb{E}\{r(k,l,ν)\}$. We show that range sidelobes ($k\neq l$) are Doppler-invariant, extending the range-sidelobe optimality to the 2-D setting. For the range mainlobe ($k=l$), periodic masking yields sparse Doppler sidelobes: Cyclic difference sets (CDSs) (in particular Singer CDSs) are minimax-optimal in a moderately dynamic regime, while in a highly dynamic regime the Doppler-sidelobe energy is a concave function of the mask autocorrelation, revealing an inevitable tradeoff with mainlobe fluctuation.

With the rising focus on quadrupeds, a generalized policy capable of handling different robot models and sensor inputs becomes highly beneficial. Although several methods have been proposed to address different morphologies, it remains a challenge for learning-based policies to manage various combinations of proprioceptive information. This paper presents Masked Sensory-Temporal Attention (MSTA), a novel transformer-based mechanism with masking for quadruped locomotion. It employs direct sensor-level attention to enhance the sensory-temporal understanding and handle different combinations of sensor data, serving as a foundation for incorporating unseen information. MSTA can effectively understand its states even with a large portion of missing information, and is flexible enough to be deployed on physical systems despite the long input sequence.

Quadrupeds have gained rapid advancement in their capability of traversing across complex terrains. The adoption of deep Reinforcement Learning (RL), transformers and various knowledge transfer techniques can greatly reduce the sim-to-real gap. However, the classical teacher-student framework commonly used in existing locomotion policies requires a pre-trained teacher and leverages the privilege information to guide the student policy. With the implementation of large-scale models in robotics controllers, especially transformers-based ones, this knowledge distillation technique starts to show its weakness in efficiency, due to the requirement of multiple supervised stages. In this paper, we propose Unified Locomotion Transformer (ULT), a new transformer-based framework to unify the processes of knowledge transfer and policy optimization in a single network while still taking advantage of privilege information. The policies are optimized with reinforcement learning, next state-action prediction, and action imitation, all in just one training stage, to achieve zero-shot deployment. Evaluation results demonstrate that with ULT, optimal teacher and student policies can be obtained at the same time, greatly easing the difficulty in knowledge transfer, even with complex transformer-based models.

Continuum robots have gained widespread popularity due to their inherent compliance and flexibility, particularly their adjustable levels of stiffness for various application scenarios. Despite efforts to dynamic modeling and control synthesis over the past decade, few studies have incorporated stiffness regulation into their feedback control design; however, this is one of the initial motivations to develop continuum robots. This paper addresses the crucial challenge of controlling both the position and stiffness of underactuated continuum robots actuated by antagonistic tendons. We begin by presenting a rigid-link dynamical model that can analyze the open-loop stiffening of tendon-driven continuum robots. Based on this model, we propose a novel passivity-based position-and-stiffness controller that adheres to the non-negative tension constraint. Comprehensive experiments on our continuum robot validate the theoretical results and demonstrate the efficacy and precision of this approach. Note to Practitioners—This paper is motivated by our experience and practical needs in building continuum robotic platforms. Stiffness, flexibility, and accurate configuration regulation are practically important properties for this class of robots. Even though simultaneous position-and-stiffness control is a mature topic for rigid and softly-actuated robots, it remains an open problem for continuum robots with theoretically guaranteed performance. The intention of this paper is to address this situation by proposing a model-based solution for a class of underactuated tendon-driven continuum robots. Hence, we propose an energy-based model and a passivity-based controller to regulate stiffness and configuration concurrently. We believe that this work can be beneficial for academic and industrial research in the context of control algorithms for continuum robots.

In human-robot collaboration (HRC) settings, hand motion intention prediction (HMIP) plays a pivotal role in ensuring prompt decision-making, safety, and an intuitive collaboration experience. Precise and robust HMIP with low computational resources remains a challenge due to the stochastic nature of hand motion and the diversity of HRC tasks. This paper proposes a framework that combines hand trajectories and gaze data to foster robust, real-time HMIP with minimal to no training. A novel 3D vector field method is introduced for hand trajectory representation, leveraging minimum jerk trajectory predictions to discern potential hand motion endpoints. This is statistically combined with gaze fixation data using a weighted Naive Bayes Classifier (NBC). Acknowledging the potential variances in saccadic eye motion due to factors like fatigue or inattentiveness, we incorporate stationary gaze entropy to gauge visual concentration, thereby adjusting the contribution of gaze fixation to the HMIP. Empirical experiments substantiate that the proposed framework robustly predicts intended endpoints of hand motion before at least 50% of the trajectory is completed. It also successfully exploits gaze fixations when the human operator is attentive and mitigates its influence when the operator loses focus. A real-time implementation in a construction HRC scenario (collaborative tiling) showcases the intuitive nature and potential efficiency gains to be leveraged by introducing the proposed HMIP into HRC contexts. The opens-ource implementation of the framework is made available at https://github.com/maleenj/hmip_ros.git.

The Rating Scale method has been long deemed the standard for measuring subjective perceptions. However, in the field of physical human-robot collaboration (pHRC), its aptness should be put under scrutiny due to inherent challenges such as response bias, between-subject variations, and the granularity nature.Individual variances can introduce significant bias in the rating scale results. A high granularity in the scale could overwhelm participants, leading to unclear and biased responses, while a low granularity may gloss over the fine nuances of human feelings. Additionally, there’s a notable risk of receiving careless responses, which compromise data reliability. Recognizing these challenges, this paper proposes the application of Pairwise Comparison (PC) in pHRC — an alternative survey technique that emphasizes direct comparisons between items on the defined criteria. By using the NASA Task Load Index (NASA-TLX) as a template, RS and PC questionnaires are designed and used in a series of pHRC experiments. Our preliminary findings suggest that PC is more precise and robust than the rating scale method. Compared to RS, PC fosters authentic participant interests in the experiment by intuitive question design and reducing the experimental duration. Besides, the accuracy and reliability of PC are also found to be consistent regardless of the variations in our experimental procedure design.

Continuum robots with variable stiffness have gained wide popularity in the last decade. Layer jamming (LJ) has emerged as a simple and efficient technique to achieve tunable stiffness for continuum robots. Despite its merits, the development of a control-oriented dynamical model1tailored for this specific class of robots remains an open problem in the literature. This paper aims to present the first solution, to the best of our knowledge, to close the gap. We propose an energy-based model that is integrated with the LuGre frictional model for LJ-based continuum robots. Then, we take a comprehensive theoretical analysis for this model, focusing on two fundamental characteristics of LJ-based continuum robots: shape locking and adjustable stiffness. To validate the modeling approach and theoretical results, a series of experiments using our OctRobot-I continuum robotic platform was conducted. The results show that the proposed model is capable of interpreting and predicting the dynamical behaviors in LJ-based continuum robots.

Obtaining the shape and size of a robot's workspace is essential for both its design and control. However, determining the accurate workspace of a multi-segment continuum robot by graphic or analytical methods is a challenging task due to its inherent flexibility and complex structure. Existing numerical methods have limitations when applied to a continuum robot. This paper presents an Equivalent Two Section (ETS) method for calculating the workspace of multi-segment continuum robots. This method is based on the forward kinematics and a piecewise constant curvature (PCC) model to determine the boundaries of the workspace. In order to verify the proposed method, simulation experiments are conducted using six different maximum bending angles and seven different number of segments. Results of the ETS method are compared to the true workspaces of these configurations estimated by an exhaustive approach. The results show that the proposed ETS method is both efficient and accurate, and has small estimation errors. Discussions on the advantages and limitations of the proposed ETS method are also presented.

Role arbitration in human-robot collaboration (HRC) is a dynamically changing process that is affected by many factors such as physical workload, environmental changes and trust. In order to address this dynamic process, a trust-based role arbitration method is studied in this research. A computational model of robot trust and self-confidence (TSC) in physical human-robot collaboration (pHRC) is proposed. The TSC model is defined as a function of objective robot and human co-worker performance. A role arbitration method is then proposed based on the TSC model presented. The human-in-the-loop experiments with a collaborative robot are conducted to verify the TSC-based role arbitration method. The results show that the proposed method could achieve superior human-robot combined performance, reduce human co-workers' workload, and improve subjective preference.

Virtual links and virtual joints can be appended to the kinematic chain of a robot arm to assist in modelling and control of certain tasks. Activities such as spray painting, sand blasting, or scanning with a laser or camera can be enhanced by modelling the fluid stream, light beam, or field of view using a virtual link. Virtual joints can be used to allow movement in semi-redundant degrees of freedom of the task space. This can can be exploited to optimize the control of the real robot. A prudent choice is to minimize the effort required by the manipulator to execute the task. This often requires the inversion of the inertia matrix. However, virtual links have no inertia so the inverse does not exist. This paper first explores methods of adding virtual mass or modifying the inertia matrix to allow inversion and the consequences. Then an optimal control problem is proposed that minimizes kinetic energy in the real manipulator and maximizes use of the virtual joints. In doing so, we only need the real inertia matrix which is always invertible. The method is validated in a case study for high pressure water blasting. It is shown to reduce the dynamic torque norm compared to a minimum velocity controller.

In physical human-robot collaboration (pHRC), singularity avoidance strategies are often critical to obtain stable interaction dynamics. It is hypothesised a predictable singularity avoidance strategy is preferred in pHRC as humans tend to maximise predictability when using complex systems. By using an electroencephalogram (EEG), it is possible to assess the predictability of a task through a feature found in event-related potentials (ERP) and called prediction-error negativity (PEN). In this paper, two research questions are addressed. Can a complex pHRC singularity avoidance strategy generate a detectable PEN? Are PEN and human preferences related when comparing different control settings in a singularity avoidance strategy? Fourteen participants compared two different sets of parameters (modes) in a singularity avoidance strategy based on the exponentially damped least-squared (EDLS) method. ERP results are presented in terms of power spectral density (PSD). ERP results were then compared with human preferences to see whether they are related. Results show that the mode that causes PEN is also the one that participants did not like, suggesting that a lack of predictability might have an impact on human preference.

This article proposes a predictive end-effector control method for manipulators operating on mobile platforms subjected to unwanted base motion. Time series is used to forecast the base motion using historical state information. Then, a trajectory specified in the inertial frame is transformed to a predicted trajectory with respect to the manipulator. By tracking this transformed trajectory, the manipulator negates the base motion. A model-predictive control problem is formulated via quadratic programming (QP) to track said trajectory over the prediction horizon. Only the first control action in the control sequence is constrained by kinematic feasibility. In this manner, QP can be swiftly solved with linear inequality constraints. It is shown that the actual joint trajectory executed by the manipulator is always kinematically feasible. Moreover, tracking error can still be reduced despite future predicted control actions being infeasible. The method is validated through both simulation and experiment. The proposed method can reduce pose error by over 60% compared to a proportional–integral feedback controller.

When a robot manipulator approaches a kinematic singular configuration, control strategies need to be employed to ensure safe and robust operation. If this manipulator is being controlled by a human through physical human-robot collaboration, the choice of strategy for handling singularities can have a significant effect on the feelings and impressions of the user. To date the preferences of humans during physical human-robot collaboration regarding strategies for managing kinematic singularities have yet to be thoroughly explored.This work presents an empirical study of a damping-based strategy for handling singularities with regard to the preferences of the human operator. Two different parameters, damping rate and damping asymmetry, are tested using a double-blind A/B pairwise comparison testing protocol. Participants included two cohorts made up of the general public (n=51) and people working within a robotic research centre (n=18). In total 105 individual trials were performed. Results indicate a preference for a faster, asymmetric damping behavior that slows motions towards singularities whilst allowing for faster motions away.

This paper presents an observer-integrated Reinforcement Learning (RL) approach, called Disturbance OB-server Network (DOB-Net), for robots operating in environments where disturbances are unknown and time-varying, and may frequently exceed robot control capabilities. The DOB-Net integrates a disturbance dynamics observer network and a controller network. Originated from conventional DOB mechanisms, the observer is built and enhanced via Recurrent Neural Networks (RNNs), encoding estimation of past values and prediction of future values of unknown disturbances in RNN hidden state. Such encoding allows the controller generate optimal control signals to actively reject disturbances, under the constraints of robot control capabilities. The observer and the controller are jointly learned within policy optimization by advantage actor critic. Numerical simulations on position regulation tasks have demonstrated that the proposed DOB-Net significantly outperforms conventional feedback controllers and classical RL policy.

Truss structures can be found in many buildings and civil infrastructure such as bridges and towers. But as these architectures age, their maintenance is required to keep them structurally sound. A legged robotic solution capable of climbing these structures for maintenance is sought, but determining the size and shape of such a robot to maximise structure coverage is a challenging task. This paper proposes a model in which the size of a multi-legged robot is optimised for coverage in a truss structure. A detailed representation of a truss structure is presented, which forms the novel framework for constraint modelling. With this framework, the overall truss structure coverage is modelled, given a robot's size and its climbing performance constraints. This is set up as an optimisation problem, such that its solution represents the optimum size of the robot that satisfies all constraints. Three case studies of practical climbing applications are conducted to verify the model. By intuitive analysis of the model's output data, the results show that the model accurately applies these constraints in a variety of truss structures.

Enabling multiple robots to collaboratively perform coverage path planning on complex surfaces embedded in R3 in the presence of moving obstacles is a challenging problem that has not received much attention from researchers. As robots start to be practically deployed, it is becoming important to address this problem. A novel decentralized multirobot coverage path planning approach is proposed that is adaptive to unexpected stationary and moving obstacles while aiming to achieve complete coverage with minimal cost. The approach is inspired by the predator-prey relation. For a robot (a prey), a virtual stationary predator enforces spatial ordering on the prey, and dynamic predators (other robots) cause the prey to be repelled resulting in better task allocation and collision-avoidance. The approach makes the best use of both worlds: offline global planning for tuning of model parameters based on a prior map of the surface, and real-time local planning for adaptive and swift decision making amid moving obstacles and other robots while preserving global behavior. Comparisons with other approaches and extensive testing and validation using different number of robots, different surfaces and obstacles, and various scenarios are conducted.

The SPIR, Submersible Pylon Inspection Robot, is developed to provide an innovative and practical solution to keep workers safe during maintenance of underwater structures in shallow waters, which involves working in dangerous water currents, and high-pressure water-jet cleaning. More advanced than work-class Remotely Operated Vehicles technology, the SPIR is automated and required minimum involvement of humans into the working process, thus effectively lowered the learning curve required to conduct work. To make SPIR operate effectively in poor visibility and highly disturbed environments, the multiple new technologies are developed and implemented into the system, including SBL-SONAR-based navigation, 6-DOF stabilisation, and vision-based 3D mapping. Extensive testing and field trials in various bridges are conducted to verify the robotic system. The results demonstrate the suitability of the SPIR in substituting humans for underwater hazardous tasks such as autonomous cleaning and inspection of bridge and wharf piles.

Most of the existing coverage path planning (CPP) algorithms do not have the capability of enabling a robot to handle unexpected changes in the coverage area of interest. Examples of unexpected changes include the sudden introduction of stationary or dynamic obstacles in the environment and change in the reachable area for coverage (e.g., due to imperfect base localization by an industrial robot). Thus, a novel adaptive CPP approach is developed that is efficient to respond to changes in real-time while aiming to achieve complete coverage with minimal cost. As part of the approach, a total reward function that incorporates three rewards is designed where the first reward is inspired by the predator-prey relation, the second reward is related to continuing motion in a straight direction, and the third reward is related to covering the boundary. The total reward function acts as a heuristic to guide the robot at each step. For a given map of an environment, model parameters are first tuned offline to minimize the path length while assuming no obstacles. It is shown that applying these learned parameters during real-time adaptive planning in the presence of obstacles will still result in a coverage path with a length close to the optimized path length. Many case studies with various scenarios are presented to validate the approach and to perform numerous comparisons.

Physical Human-Robot Collaboration (pHRC) is about the interaction between one or more human operator(s) and one or more robot(s) in direct contact and voluntarily exchanging forces to accomplish a common task. In any pHRC, the intuitiveness of the interaction has always been a priority, so that the operator can comfortably and safely interact with the robot. So far, the intuitiveness has always been described in a qualitative way. In this paper, we suggest an objective way to evaluate intuitiveness, known as prediction error negativity (PEN) using electroencephalogram (EEG). PEN is defined as a negative deflection in event related potential (ERP) due to cognitive conflict, as a consequence of a mismatch between perception and reality. Experimental results showed that the forces exchanged between robot and human during pHRC modulate the amplitude of PEN, representing different levels of cognitive conflict. We also found that PEN amplitude significantly decreases (p <; 0.05) when a mechanical resistance is being applied smoothly and more time in advance before an invisible obstacle, when compared to a scenario in which the resistance is applied abruptly before the obstacle. These results indicate that an earlier and smoother resistance reduces the conflict level. Consequently, this suggests that smoother changes in resistance make the interaction more intuitive.

This paper presents a unified motion planning approach for an Intervention Autonomous Underwater Vehicle (I-AUV) in a cluttered environment with localization uncertainty. With the uncertainty being propagated by an information filter, a trajectory optimization problem closed by a Linear-Quadratic-Gaussian controller is formulated for a coupled design of optimal trajectory, localization, and control. Due to the presence of obstacles or complexity of the cluttered environment, a set of feasible initial I-AUV trajectories covering multiple homotopy classes are required by optimization solvers. Parameterized through polynomials, the initial base trajectories are from solving quasi-quadratic optimization problems that are linearly constrained by waypoints from RRTconnect, while the initial trajectories of the manipulator are generated by a null space saturation controller. Simulations on an I-AUV with a 3 DOF manipulator in cluttered underwater environments demonstrated that initial trajectories are generated efficiently and that optimal and collision-free I-AUV trajectories with low state uncertainty are obtained.

We present the ANBOT, an intelligent robotic coworker for physical human-robot collaboration. The ANBOT system assists workers performing industrial abrasive blasting, shielding them from the large forces experienced during this physically demanding task. The co-operative robotic system combines the strength and endurance of robots with the decision making of skilled workers. The inherent challenges in human-robot collaboration, combined with the difficult blasting environment required novel design decisions to be made and new solutions to be developed. These include an approach for handling kinematic singularities in a manner suitable for human-robot co-operation, estimating worker pose under poor visibility conditions, and an intuitive control scheme that adapts the robotic assistance based on the estimated strength of the worker. In this work we summarise the ANBOT system and present findings from preliminary site trials. The trials included several real industrial blasting tasks under the control of a skilled abrasive blasting worker who had no experience working alongside a robot. Results demonstrate the suitability of the ANBOT for practical industrial applications.

This paper presents an algorithm for calibrating a “3D range sensor” constructed using a two-dimensional laser range finder (LRF), that is rotated about an axis using a motor to obtain a three-dimensional point cloud. The sensor assembly is modelled as a two degree of freedom open kinematic chain, with one joint corresponding to the axis of the internal mirror in the LRF and the other joint set along the axis of the motor used to rotate the body of the LRF. In the application described in this paper, the sensor unit is mounted on a robot arm used for infrastructure inspection. The objective of the calibration process is to obtain the coordinate transform required to compute the locations of the 3D points with respect to the robot coordinate frame. Proposed strategy uses observations of a set of markers arbitrarily placed in the environment. Distances between these markers are measured and a metric multidimensional scaling is used to obtain the coordinates of the markers with respect to a local coordinate frame. Intensity associated with each beam point of a laser scan is used to locate the reflective markers in the 3D point cloud and a least squares problem is formulated to compute the relationship between the robot coordinate frame, LRF coordinate frame and the marker coordinate frame. Results from experiments using the robot, LRF combination to map a cavity inside a steel bridge structure are presented to demonstrate the effectiveness of the calibration process.

Marine growths that flourish on the surfaces of underwater structures, such as bridge pylons, make the inspection and maintenance of these structures challenging. A robotic solution, using an Intervention Autonomous Underwater Vehicle (I-AUV), is developed for removing marine growth. This paper presents a Deformable Spiral Coverage Path Planning (DSCPP) algorithm for marine growth removal. DSCPP generates smooth paths to prevent damage to the surfaces of the structures and to avoid frequent or aggressive decelerations and accelerations due to sharp turns. DSCPP generates a spiral path within a circle and analytically maps the path to a minimum bounding rectangle which encompasses an area of a surface with marine growth. It aims to achieve a spiral path with minimal length while preventing missed areas of coverage. Several case studies are presented to validate the algorithm. Comparison results show that DSCPP outperforms the popular boustrophedon-based coverage approach when considering the requirements for the application under consideration.

This paper investigates the robust control of an underactuated brachiating robot. Inspired by the pendulumlike movements in gibbons' arboreal locomotion, the controllers are designed to synchronize the brachiator with a virtual oscillator. Two schemes are proposed: a model-dependent feedback linearization scheme and a sliding-mode scheme that is independent of the system model. The simulation results illustrate that the proposed schemes are robust to the arbitrary initial configurations of the brachiator and the limitation in the motor torque at the elbow joint. Furthermore, both controllers enable the underactuated robot to brachiate along a structural member with an upward slope.

Inspecting surface conditions in 3-D environments such as steel bridges is a complex, time-consuming, and often hazardous undertaking that is an essential part of tasks such as bridge maintenance. Developing an autonomous exploration strategy for a mobile climbing robot would allow for such tasks to be completed more quickly and more safely than is possible with human inspectors. The exploration strategy tested in this paper, called the nearest neighbors exploration approach (NNEA), aims to reduce the overall exploration time by reducing the number of sensor position evaluations that need to be performed. NNEA achieves this by first considering at each time step only a small set of poses near to the current robot as candidates for the next best view. This approach is compared with another exploration strategy for similar robots performing the same task. The improvements between the new and previous strategy are demonstrated through trials on a test rig, and also in field trials on a ferromagnetic bridge structure.

It is becoming increasingly achievable for steel bridge structures, which are normally both inaccessible and hazardous for humans, to be inspected and maintained by autonomous robots. Steel bridges have been traditionally constructed by securing plate members together with rivets. However, rivets present a challenge for robots both in terms of cleaning and surface traversal. This paper presents a novel approach to RGB-D image and point cloud analysis that enables rivets to be rapidly and robustly located using low cost, non-contact sensing devices that can be easily affixed to a robot. The approach performs classification based on: (a) high-intensity blobs in color images, (b) the non-linear perturbations in depth images, and (c) surface normal clusters in 3D point clouds. The predicted rivet locations from the three classifiers are combined using a probabilistic occupancy mapping technique. Experiments are conducted in several different lab and real-world steel bridge environments, where there is no external lighting infrastructure, and the sensors are attached to a mobile platform, i.e. a climbing inspection robot. The location of rivets within 2m of the robot can be robustly located within 10mm of their correct location. The state of voxels can be predicted with above 95% accuracy, in approximately 1 second per frame.

This paper presents a new configuration for single axis tactile sensor arrays molded in rubber to enable tri-axial force measurement. The configuration requires the sensing axis of each sensor in the array to be rotated out of alignment with respect to external forces. This angled sensor array measures shear forces along axes in a way that is different to a planar sensor array. Three sensors using the angled configuration (22.5°, 45° and 67.5°) and a fourth sensor using the planar configuration (0°) have been fabricated for experimental comparison. Artificial neural networks were trained to interpret the external force applied along each axis (X, Y and Z) from raw pressure sensor values. The results show that the angled sensor configuration is capable of measuring tri-axial external forces with a root mean squared error of 1.79N, less error in comparison to the equivalent sensor utilizing the planar configuration (4.52N). The sensors are then implemented to control a robotic arm. Preliminary findings show angled sensor arrays to be a viable alternative to planar sensor arrays for shear force measurement; this has wide applications in physical Human Robot Interaction (pHRI).

Autonomous Underwater Vehicle-Manipulator Systems (AUVMS) operating in shallow waters or near-surface environments may be exposed to wave disturbances which will cause undesired motion of the end effector. This paper presents a method to maneuver the manipulator joints and counteract undesired motion of the vehicle body, in order to maintain a steady end-effector position in the inertial frame. An Autoregressive (AR) model is used to predict vehicle motion, and then combined with Model Predictive Control (MPC) to optimize joint motion. Simulation was conducted using real data to verify the efficacy of this method.

Uncertainties in base placements of mobile, autonomous industrial robots can cause incomplete coverage in tasks such as grit-blasting and spray painting. Sensing and localization errors can cause such uncertainties in robot base placements. This paper addresses the problem of collaborative complete coverage under uncertainties through appropriate base placements of multiple mobile and autonomous industrial robots while aiming to optimize the performance of the robot team. A mathematical model for complete coverage under uncertainties is proposed and then solved using a stochastic multi-objective optimization algorithm. The approach aims to concurrently find an optimal number and sequence of base placements for each robot such that the robot team's objectives are optimized whilst uncertainties are accounted for. Several case studies based on a real-world application using a real-world object and a complex simulated object are provided to demonstrate the effectiveness of the approach for different conditions and scenarios, e.g. various levels of uncertainties, different numbers of robots, and robots with different capabilities.

This paper presents an approach for selecting the next best base position for a climbing robot so as to observe the highest information gain about the environment. The robot is capable of adhering to and moving along and transitioning to surfaces with arbitrary orientations. This approach samples known surfaces, and takes into account the robot kinematics, to generate a graph of valid attachment points from which the robot can either move to other positions or make observations of the environment. The information value of nodes in this graph are estimated and a variant of A* is used to traverse the graph and discover the most worthwhile node that is reachable by the robot. This approach is demonstrated in simulation and shown to allow a 7 degree-of-freedom inchworm-inspired climbing robot to move to positions in the environment from which new information can be gathered about the environment.

There are many benefits for the deployment of multiple autonomous industrial robots to carry out a task, particularly if the robots act in a highly collaborative manner. Collaboration can be possible when each robot is able to autonomously explore the environment, localize itself, create a map of the environment and communicate with other robots. This paper presents an approach to the modeling of the collaboration problem of multiple robots determining optimal base positions and orientations in an environment by considering the team objectives and the information shared amongst the robots. It is assumed that the robots can communicate so as to share information on the environment, their operation status and their capabilities. The approach has been applied to a team of robots that are required to perform complete surface coverage tasks such as grit-blasting and spray painting in unstructured environments. Case studies of such applications are presented to demonstrate the effectiveness of the approach.

Compared to a robotic system that performs a task alone, a robot coworker performing tasks in collaboration with a human operator is subject to additional constraints which can limit the ability of the system to perform the task as required. This work presents a framework for analyzing the ability of a robotic coworker to perform specific tasks in collaboration with a human. The framework allows systematic evaluation of robotic systems based on traditional robot performance measures such as reachable workspace and payload capacity, as well as considering additional factors which arise due to the task being performed collaboratively with a human; such as the reach and strength of the human, human-robot collision, and satisfying desired assistance paradigms. Application of the framework is demonstrated in a case study analyzing a robot designed to assist a human during a materials handling task.

When multiple industrial robots are deployed in field applications such as grit blasting and spray painting of steel bridges, the environments are unstructured for robot operation and the robot positions may not be arranged accurately. Coordination of these multiple robots to maximize productivity through area partitioning and allocation is crucial. This paper presents a novel approach to area partitioning and allocation by utilizing multiobjective optimization and voronoi partitioning. Multiobjective optimization is used to minimize: (1) completion time, (2) proximity of the allocated area to the robot, and (3) the torque experienced by each joint of the robot during task execution. Seed points of the voronoi graph for voronoi partitioning are designed to be the design variables of the multiobjective optimization algorithm. Results of three different simulation scenarios are presented to demonstrate the effectiveness of the proposed approach and the advantage of incorporating robots' torque capacity.

This paper considers path planning for underwater robot in navigation tasks. The main challenge is how to deal with uncertainties in the underwater environment such as motion model error and sensing error. To overcome this challenge, two high level control methods have been presented and compared, which are based on the Model Predictive Control (MPC) strategy and the Partially Observable Markov Decision Process (POMDP) model, respectively. Navigation time, collision frequency, energy consumption and accuracy in localization are used as the assessment criteria for the two methods. It is shown that the MPC-based method is more efficient for our application scenarios while the POMDP-based method can provide more robust solutions.

This paper proposes an approach to improve surface-type classification of images containing inconsistently illuminated surfaces. When a mobile inspection robot is visually inspecting surface-types in a dark environment and a directional light source is used to illuminate the surfaces, the images captured may exhibit illumination variance that can be caused by the orientation and distance of the light source relative to the surfaces. In order to accurately classify the surface-types in these images, either the training image dataset needs to completely incorporate the illumination variance or a way to extract color features that can provide high classification accuracy needs to be identified. In this paper diffused reflectance values are extracted as new color features to classifying surface-types. In this approach, Red, Green, Blue-Depth (RGB-D) data is collected from the environment, and a reflectance model is used to calculate a diffused reflectance value for a pixel in each Red, Green, Blue (RGB) color channel. The diffused reflectance values can be used to train a multiclass support vector machine classifier to classify surface-types. Experiments are conducted in a mock bridge maintenance environment using a portable RGB-Depth sensor package with an attached light source to collect surface-type data. The performance of a classifier trained with diffused reflectance values is compared against classifiers trained with other color features including RGB and L*a*b* color spaces. Results show that the classifier trained with the diffused reflectance values can achieve consistently higher classification accuracy than the classifiers trained with RGB and L*a*b* features. For test images containing a single surface plane, diffused reflectance values consistently provide greater than 90% classification accuracy; and for test images containing a complex scene with multiple surface-types and surface planes, diffused reflectance values are shown to provide an increase in overall accuracy over RGB and L*a*b* by 49.24% and 13.66%, respectively.

This paper presents an approach for exploring a complex 3D environment with a sensor mounted on the end effector of a robot manipulator. In contrast to many current approaches which plan as far ahead as possible using as much environment information as is available, our approach considers only a small set of poses (vector of joint angles) neighbouring the robot's current pose in configuration space. Our approach is compared to an existing exploration strategy for a similar robot. Our results demonstrate a significant decrease in the number of information gain estimation calculations that need to be performed, while still gathering an equivalent or increased amount of information about the environment.

A multiobjective optimization model is presented in this paper for the Autonomous Straddle Carriers Scheduling (ASCS) problem in automated container terminals, which is more practical than the single objective model. The model considers three objectives [i.e., Straddle Carriers (SCs) traveling time, SC waiting time and finishing time of high-priority container-transferring jobs], and their weighted sum is investigated as the representative example. The presented model is formulated as a pickup and delivery problem with time windows in the form of binary integer programming. An exact algorithm based on Branch-and-Bound with Column Generation (BBCG) is employed for solving the multiobjective ASCS problem. Based on the map of an actual fully automated container terminal, simulation results are compared with the single-objective scheduling to demonstrate the effectiveness and flexibility of the presented multiobjective model, as well as the efficacy of the BBCG algorithm for autonomous SC scheduling.

In this paper, an optimisation model based on Pickup and Delivery Problem with Time Windows (PDPTW), and an exact algorithm based on Branch-and-Bound with Column Generation (BBCG), are presented for Autonomous Straddle Carriers Scheduling (ASCS) problem at automated container terminals. The ASCS problem is firstly modeled into a PDPTW, which is formulated as a Binary Integer Programming (BIP) and then solved by Column Generation (CG) in the Branch-and-Bound (BB) framework. The BBCG algorithm is also compared to another two exact algorithms [i.e., Binary integer Programming with Dynamic Programming (BPDP) and Exhaustive Search with Permutation and Combination (ESPC)] for the ASCS problem solving. Based on the map of an actual automated container terminal, simulation results and discussions are presented to demonstrate the effectiveness and efficiency of the presented model and algorithm for autonomous vehicle scheduling.

The main contribution of this paper is a mathematical model describing performance metrics for coordinating multiple mobile robots in a seaport container terminal. The scenario described here requires dealing with many difficult practical challenges such as the presence of multiple levels of container stacking and sequencing, variable container orientations, and vehicular dynamics that require finite acceleration and deceleration times. Furthermore, in contrast to the automatically guided vehicle planning problem in a manufacturing environment, the container carriers described here are free ranging. Although, the port structure imposes a set of “virtual” roadways along which the vehicles are allowed to travel, path planning is essential in preventing contention and collisions. A performance metric which minimises total yard-vehicle usage, while producing robust traffic plans by encouraging both early starting and finishing of jobs is presented for different vehicle fleet sizes and job allocation scenarios.

Robotic systems such as exoskeletons can be effectively used in the reduction of fatigue and musculoskeletal disorders (MSD) associated with physical tasks, but robots which work in physical contact with humans pose problems with user safety. A novel approach to developing intrinsically safe robots is to use passive actuators which have the advantage of being safer, ensuring stability, high force/weight ratios and lower power consumption. It is however not clear how effective an exoskeleton utilizing passive actuators would be in reducing fatigue and the risk of MSD. This paper analyzes the benefit of using such a system with results from dynamic simulations and an experiment using a specially designed mechanism used for evaluation. Results indicate that fatigue and effort could be reduced if robot impedance is minimized. Experiments also highlighted issues of implementing such a system into practice.

The contrast enhancement of gray-level digital images is considered in this paper. In particular, the mean image intensity is preserved while the contrast is enhanced. This provides better viewing consistence and effectiveness. The contrast enhancement is achieved by maximizing the information content carried in the image via a continuous intensity transform function. The preservation of image intensity is obtained by applying gamma-correction on the images. Since there is always a trade-off between the requirements for the enhancement of contrast and preservation of intensity, an improved multiobjective particle swarm optimization procedure is proposed to resolve this contradiction, making use of its flexible algorithmic structure. The effectiveness of the proposed approach is illustrated by a number of images including the benchmarks and an image sequence captured from a mobile robot in an indoor environment.

This paper considers a berth allocation problem (BAP) which requires the detemination of exact berthing times and positions of incoming ships in a container port. The problem is solved by optimizing the berth schedule so as to minimize concurrently the three objectives of mak span, number of crossings, and waitimg time. These objectives represent the interests of both port and ship operators. A multi-objective evolutionary algorithm (MOEA) that incorporates the concept of Pareto optimality is proposed for solving the multi-objective BAP. The MOEA is equipped with a novel solution decoding scheme which is specifically designed to optimize the use of berth space. The MOEA is also able to function in a dynamic context which is of more relevance to a real-world situation.

Evolutionary computation algorithms, such as the particle swarm optimization (PSO), have been widely applied in numerical optimizations and real-world product design, not only for their satisfactory performances but also in their relaxing the need for detailed mathematical modelling of complex systems. However, as iterative heuristic searching methods, they often suffer from difficulties in obtaining high quality solutions in an efficient manner. Since unnecessary resources used in computation iterations should be avoided, the determination of a proper termination condition for the algorithms is desirable. In this work, termination is cast as a decision-making process to end the algorithm. Specifically, the non-parametric sign- test is incorporated as a hypothetical test method such that a quantifiable termination in regard to specifiable decision-errors can be assured. Benchmark optimization problems are tackled using the PSO as an illustrative optimizer to demonstrate the effectiveness of the proposed termination condition.

This paper presents an investigation into combining migration strategies inspired by multi-deme parallel genetic algorithms with the XCS learning classifier system to provide parallel and distributed classifier migration. Migrations occur between distributed XCS classifier sub-populations using classifiers ranked according to numerosity, fitness or randomly selected. The influence of the degree-of-connectivity introduced by fully-connected, bi-directional ring and uni-directional ring topologies is examined. Results indicate that classifier migration is an effective method for improving classification accuracy, improving learning speed and reducing final classifier population size, in the single-step classification of noisy, artefact- inclusive human electroencephalographic signals. The experimental results will be used as part of our larger research effort investigating the feasibility of using EEG signals as an interface to allow paralysed persons to control a powered wheelchair or other devices.

The F2(Force Field) method is a novel approach for multi-robot motion planning and collision avoidance. The setting of parameters is however vital to its performance. This paper presents an approach using Particle Swarm Optimization (PSO) to properly determine the control parameters for the F2method. The goal of the optimization is to minimize the resultant path lengths. The approach presented in this paper can be used as a tool to obtain optimal parameters for various tasks before their execution. Simulations are carried out in various environments to show the feasibility of this approach.

This paper considers the routing of vehicles with limited capacity from a central depot to a set of geographically dispersed customers where actual demand is revealed only when the vehicle arrives at the customer. The solution to this vehicle routing problem with stochastic demand (VRPSD) involves the optimization of complete routing schedules with minimum travel distance, driver remuneration, and number of vehicles, subject to a number of constraints such as vehicle time window and capacity. To solve such a multiobjective combinatorial optimization problem, this paper presents a multiobjective evolutionary algorithm that incorporates two VRPSD-specific heuristics for local exploitation and a route simulation method to evaluate the fitness of solutions. A novel way of assessing the quality of solutions to the VRPSD on top of comparing their expected costs is also proposed. It is shown that the algorithm is capable of finding useful tradeoff solutions which are robust to the stochastic nature of the problem.

The effects of randomness of control coefficients in particle swarm optimization (PSO) are investigated through empirical studies. The PSO is viewed as a method to solve a coverage problem in the solution space when the global-best particle is reported as the solution. Randomness of the control coefficients, therefore, plays a crucial role in providing an efficient and effective algorithm. Comparisons of performances are made between the uniform and Gaussian distributed random coefficients in adjusting particle velocities. Alternative strategies are also tested, they include: i) pre-assigned randomness through the iterations, ii) selective hybrid random adjustment based on the fitness of the particles. Furthermore, the effect of velocity momentum factor is compared between a constant and random momentum. Numerical results show that performances of the proposed variations are comparable to the conventional implementation for simple test functions. However, enhanced performances using the selective and hybrid strategy are observed for complicate functions.

A novel force field (F2) method with variable speed for multi-robot motion planning and collaboration is presented in this paper. The basic concept of the F2method is to generate a force field for every robot based on and continuously changing according to its status including traveling speed, dimension, priority, location and environmental factor, etc. The interactions among robots' force fields and obstacles provide a natural way for collision avoidance and collaboration while robots are on their way to goals. Previous F2method assumes that robots travel with constant speeds and can react instantly to the resultant force to change their orientations. Starting from a problematic situation brought out by this hypothesis, this paper remedies the F2method by taking robots' dynamics and kinematics characteristics into consideration. In the variable speed force field method (VSF2), a robot can change its own speed according to environment information and its own status. Simulations in a real indoor environment were carried out and demonstrated the feasibility and effectiveness of this method