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In this paper, we present a solution to a design problem of control strategies for multi-agent cooperative transport. Although existing learning-based methods assume that the number of agents is the same as that in the training environment, the number might differ in reality considering that the robots' batteries may completely discharge, or additional robots may be introduced to reduce the time required to complete a task. Therefore, it is crucial that the learned strategy be applicable to scenarios wherein the number of agents differs from that in the training environment. In this paper, we propose a novel multi-agent reinforcement learning framework of event-triggered communication and consensus-based control for distributed cooperative transport. The proposed policy model estimates the resultant force and torque in a consensus manner using the estimates of the resultant force and torque with the neighborhood agents. Moreover, it computes the control and communication inputs to determine when to communicate with the neighboring agents under local observations and estimates of the resultant force and torque. Therefore, the proposed framework can balance the control performance and communication savings in scenarios wherein the number of agents differs from that in the training environment. We confirm the effectiveness of our approach by using a maximum of eight and six robots in the simulations and experiments, respectively., 14 pages, 14 figures

Different authors have addressed a number of problems in the area of distributed control proposing convincing solutions to specific problems such as static coverage, dynamic coverage/exploration, rendezvous, flocking, formation control. However, a major limitation of problem-specific approaches is a fundamental lack of flexibility when the group meets unexpected conditions and has to change its goal on the fly. In this paper, we show that a large class of distributed control problems can be cast into a general framework based on the adoption of the Lloyd methodology. The adoption of a unified framework enables efficient solutions for the specific problems guaranteeing at the same time important safety and functional properties and a large degree of flexibility in the execution of group tasks. The paper sets the theoretical basis for this development and proves the efficacy of the proposed solutions through extensive simulations and experimental results.

Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.

This paper presents semantic-based methods for the understanding of human movements in robotic applications. To understand human movements, robots need to first, recognize the observed or demonstrated human activities, and secondly, learn different parameters to execute an action or robot behavior. In order to achieve that, several challenges need to be addressed such as the automatic segmentation of human activities, identification of important features of actions, determine the correct sequencing between activities, and obtain the correct mapping between the continuous data and the symbolic and semantic interpretations of the human movements. This paper aims to present state-of-the-art semantic-based approaches, especially the new emerging approaches that tackle the challenges of finding generic and compact semantic models for the robotics domain. Finally, we will highlight potential breakthroughs and challenges for the next years such as achieving scalability, better generalization, compact and flexible models, and higher system accuracy.

In this paper, we discuss the omni-directional gait of a passive-spine hexapod robot, which consists of three body segments connected by passive body joints. To design the turning and rotating gaits for this robot, firstly, we analyze a geometric model to demonstrate how segment rotation can result from abduction of the legs. Next, we obtain the principle of the rotation and direction of the body segments, which are analyzed by determining the geometric features of different configurations. The turning and rotating gaits are then attained by varying only the duration of the transition sequence of the passive-spine hexapod robot. The capability of sideways motion is also given in this paper. Finally, this proposed method for designing the omni-directional gait of a passive-spine hexapod is demonstrated by simulations and experiments.

This paper assesses some of the key legal and regulatory questions arising from the integration of physical robotic systems with cloud-based services, also called “cloud robotics.” The literature on legal and ethical issues in robotics has a strong focus on the robot itself, but largely ignores the background information processing. Conversely, the literature on cloud computing rarely addresses human–machine interactions, which raise distinctive ethical and legal concerns. In this paper, we investigate, from a European legal and regulatory perspective, the growing interdependence and interactions of tangible and virtual elements in cloud robotics environments. We highlight specific problems and challenges in regulating such complex and dynamic ecosystems and explore potential solutions. To illustrate practical challenges, we consider several examples of cloud robotics ecosystems involving multiple parties, various physical devices, and various cloud services. These examples illuminate the complexity of interactions between relevant parties. By identifying pressing legal and regulatory issues in relation to cloud robotics, we hope to inform the policy debate and set the scene for further research.

A crucial requirement for imparting autonomy to the fish-inspired robots is the trajectory planning capability that can generate obstacle-free and optimal trajectories to a commanded goal location. Research in the area of dynamics-aware trajectory planning, especially in the presence of environmental disturbances, for fish-inspired robots is scarce. Trajectory-following for fish-inspired robots, which is different from trajectory planning, has been well explored wherein feedback controllers that reject disturbances are employed to follow the pre-specified waypoints. This paper reports an optimal trajectory planning approach for Anguilliform-inspired robots based on model predictive planning framework. The robot’s dynamics constraints, as well as its interaction with the surrounding flow conditions, are captured via dynamics simulations and are expressed using discretized motion primitives. The motion primitives are then used for generating a search-tree and an A*-based algorithm is used for searching the optimal trajectory. We developed a simulation-based admissible heuristic function that is used for improving the computational performance of the developed trajectory planner. The developed simulation-based heuristic function provided a computational speed-up by a factor of up to 10.3 with respect to that of the Euclidean heuristic for test cases comprising of a variety of obstacle densities, ambient flow conditions, and goal locations. The trajectories generated by the developed approach have been found to be dynamically feasible, collision-free, and optimal for the experiments reported in this paper. We believe that the developed approach can impart autonomy to the Anguilliform-inspired robots that, due to their slender and hyper-redundant structures, can perform autonomous inspection and maintenance of sub-sea structures having narrow regions, obstacles, and ambient flow and can be useful in various civil and defense applications.

Due to high degrees-of-freedom of humanoids and induced redundancy, there are multiple ways of performing a given manipulation task. Finding optimal ways of performing tasks is one desirable property of any motion planning framework. This includes optimizing both the path with respect to a certain objective function and also the final pre-grasp or goal position. Additionally, a variety of constraints need to be satisfied such as stability, self-collision and collision with objects in the environment and also kinematic loop-closure formed during the task. In this paper, an asymptotically optimal sampling based approach for generating motion plans is presented. A novel constraint solver extension to the bidirectional Fast Marching Trees (BFMT*) algorithm in the form of a way-point generator is proposed such that it can be applied for whole-body motion planning of humanoids. Moreover, a comparison of the performance of the proposed extension of BFMT* and the state-of-art RRT* based motion planner is shown. A gradient based inverse kinematics solver has also been implemented in combination with an optimization technique to generate goal configurations in order to ensure optimality in the pre-grasp position. The efficacy of the proposed approach is evaluated in a simulation environment on Hubo+ robot model. The results show a significant improvement in path costs, as well as overall optimality of given tasks for the proposed approach. Additionally, rigorous analysis over the choice of planning algorithms considered in this paper is present for the considered scenarios.

In this paper stiffness of cable-driven parallel robots (CDPRs) is analyzed in detail and based on this analysis, the stiffness-feasible workspace is introduced. This workspace includes all stable poses which increasing the internal forces can modify the total stiffness of the robot. It has been shown that in the CDPRs, the concept of the internal forces can be applied for keeping cables in tension and increasing stiffness. However, it should be noted that increasing the internal forces may decrease the overall stiffness of the mechanism and it is only applicable in stabilizable poses of the workspace. Therefore, stiffness-feasible workspace determines the allowable internal forces range which can increase the stiffness and it will guarantee that in this range of the internal forces, the structure of the robot is stable. In this paper, by employing this criterion and evolutionary algorithms, a CDPR is optimally designed, and a set of answers is presented. In this design, in addition to the stiffness-feasible workspace, another criterion as stiffness number is presented which is useful for specifying the distribution of the stiffness and stiffness-feasibility of the robot.

This paper presents a robust method for generic obstacle detection and collision warning in Advanced Driver Assistance Systems (ADAS). The highlight of our method is the ability to detect all obstacles without prior knowledge and detect partially occluded obstacles including the obstacles that have not completely appeared in the frame (truncated obstacles). Our results show an improvement of 90% more true positives per frame compared to one of the state-of-the-art methods. Our proposed method is robust to variations in illumination and to a wide variety of vehicles and obstacles that are encountered while driving. Distortions are introduced when Inverse Perspective Mapping (IPM) projects the camera image onto the road surface plane. In this paper, we first show that the angular distortion in the IPM domain belonging to obstacle edges varies as a function of their corresponding 2D location in the camera plane. We use this information to perform proposal generation. We propose a novel proposal assessment method based on fusing statistical properties from both the IPM image and the camera image to perform robust outlier elimination and false positive reduction. We also present an annotated obstacle detection dataset derived from various source videos that can serve as a benchmark for the evaluation of future obstacle detection algorithms. The source videos containing diverse illumination and traffic conditions are derived from multiple publicly available datasets.

This paper attempts to address the problem of online modulation of virtual impedance for an assistive robot based on real-time gait and activity measurements to personalize the assistance for different users at different states. In this work, smart shoes and inertial sensors are introduced to measure ground contact forces and knee joint kinematics, respectively. An automatic impedance tuning (AIT) approach is presented for a knee assistive device (KAD) based on real-time activity recognition and gait phase detection. The activities considered in this paper are level, uphill, and downhill walking. A Gaussian mixture model (GMM) is employed to map the fuzzy likelihood of various activities and gait phases to the desired virtual impedance of the KAD. The prior estimate of virtual impedance is defined using human knee impedance identified with the walking data collected on different users. The AIT approach is integrated into the high-level impedance-based controller of the KAD for assistance during the stance phase. Finally, to evaluate the benefit of the proposed algorithm in stance phase, an EMG sensor is placed on the vastus medialis muscle group of three participants. The proposed approach is compared with two baseline approaches: constant impedance and finite state machine, and the results demonstrate that the profiles of impedance parameters and robot assistive torque are smoother and the muscle activity of vastus medialis is reduced. It is also noticed that the participants reduce their step lengths and increase walking cadence with assistance from the KAD.

Target searching in an unknown environment is a traditional research issue in the multiagent area. In some real cases, the agents do not only discover the targets; instead, they have subsequent tasks that must be completed before a deadline. In this paper, these cases are abstracted as the agents searching for target locations in an unknown environment and then occupying these target locations within a limited time. The agents can obtain rewards by occupying the target locations, and the goal of this problem is to maximize net income, defined as total reward minus the moving cost of the agents. This problem can be transformed into the traditional problems, and then be solved by previous related algorithms. However, this approach is not optimal. In this paper, we present a method that combines previous algorithms and a decision-making algorithm. The experiments demonstrate that the method containing our decision-making algorithm can lead to higher net income than simply using previous algorithms. This work was supported by the National Natural Science Foundation of China (61472079, 61807008 and 61806053), the Natural Science Foundation of Jiangsu Province of China (BK20171363, BK20180356, BK20180369).

Human–robot interaction in natural settings requires filtering out the different sources of sounds from the environment. Such ability usually involves the use of microphone arrays to localize, track and separate sound sources online. Multi-microphone signal processing techniques can improve robustness to noise but the processing cost increases with the number of microphones used, limiting response time and widespread use on different types of mobile robots. Since sound source localization methods are the most expensive in terms of computing resources as they involve scanning a large 3D space, minimizing the amount of computations required would facilitate their implementation and use on robots. The robot’s shape also brings constraints on the microphone array geometry and configurations. In addition, sound source localization methods usually return noisy features that need to be smoothed and filtered by tracking the sound sources. This paper presents a novel sound source localization method, called SRP-PHAT-HSDA, that scans space with coarse and fine resolution grids to reduce the number of memory lookups. A microphone directivity model is used to reduce the number of directions to scan and ignore non significant pairs of microphones. A configuration method is also introduced to automatically set parameters that are normally empirically tuned according to the shape of the microphone array. For sound source tracking, this paper presents a modified 3D Kalman (M3K) method capable of simultaneously tracking in 3D the directions of sound sources. Using a 16-microphone array and low cost hardware, results show that SRP-PHAT-HSDA and M3K perform at least as well as other sound source localization and tracking methods while using up to 4 and 30 times less computing resources respectively.

This paper addresses the problem of Human-Aware Navigation (HAN), using multi camera sensors to implement a vision-based person tracking system. The main contributions of this paper are as follows: ...

Self-assembling robots have the potential to undergo autonomous morphological adaptation. However, due to the simplicity in their hardware makeup and their limited perspective of the environment, self-assembling robots are often not able to reach their potential and adapt their morphologies to tasks or environments without external cues or prior information. In this paper, we present supervised morphogenesis — a control methodology that makes self-assembling robots truly flexible by enabling aerial robots to exploit their elevated position and better view of the environment to initiate and control (hence supervise) morphology formation on the ground. We present results of two case studies in which we assess the feasibility of the presented methodology using real robotic hardware. In the case studies, we implemented supervised morphogenesis using two different aerial platforms and up to six self-assembling autonomous robots. We furthermore quantify the benefits attainable for self-assembling robots through cooperation with aerial robots using simulation-based studies. The research presented in this paper is a significant step towards realizing the true potential of self-assembling robots by enabling autonomous morphological adaptation to a priori unknown tasks and environments., SCOPUS: ar.j, info:eu-repo/semantics/published

When applied in some harsh environments (e.g. in poisonous atmosphere or underwater), odor source localization robots are able to perform better than animals without being hurt. During the past three decades, robotic odor source localization has become a popular research field with various algorithms being proposed. These algorithms can be roughly divided into four categories: gradient-based algorithms, bio-inspired algorithms, multi-robot algorithms and probabilistic and map-based algorithms. In this paper, we present a literature review of these four categories and discuss their pros and cons. We also discuss the current trends and some future challenges according to some research papers published in recent years.

The aim of this paper is to demonstrate, based on robot results, the effectiveness and practicability of vestibular feedback to central pattern generators (CPG) employed for the locomotion of quadruped robots. We build a new quadruped robot with mechanisms enabling walking to running and apply CPGs modulated by simple vestibular sensory feedback (a body tilt multiplied by a fixed gain). As a result, the robot safely locomotes at a variety of speeds by autonomously changing the gait from walking to trotting to galloping according to speed, despite the fact that the walk and gallop are not preprogrammed. In addition, as this paper’s major contribution, we discover and demonstrate that the robot robustly runs with an emergent gallop while stepping on and over several types of unperceived obstacles, while being suddenly pulled forward, and while the physical balance is changed (i.e., a weight is put forward on the robot), by autonomously modifying the phase differences between the four legs from the basic gallop. To our knowledge, no other galloping robots have been reported that can adapt to an unperceived obstacle. We conclude that CPGs modulated by vestibular feedback is effective and practical as a gait generator for bio-inspired robots that are expected to have both the abilities of “speed-based autonomous gait transition” and “autonomous robust running”.

Recently, MANET (Mobile Ad Hoc Network) researchers have shown increased interest in using mobile robot technology for their testbed platforms. Despite the existence of review papers that discuss the usage of mobile robot technology pertaining to a MANET testbed from the perspective of a MANET researcher, said findings are rather lacklustre as it is not the sole purpose of said reviews. Hence, this review aims to comprehensively discuss and analyse MANET testbeds that were facilitated with mobile robot technology for previous undertaken research. To enable readers to keep abreast with mobile robot technology used in previous research, whilst presenting the advantages and disadvantages of said methods, this review will first superficially discuss prior robot based MANET testbed facilities, before presenting technical analysis overview and critical analysis. Additionally, suggestions to heighten mobility mechanisms by using mobile robots to be more practical, easy and inexpensive are also included in this paper. The technical and critical content of this review is expected to be a source of reference for other MANET researchers interested in the most suitable mobile robots to ensure real mobility in their MANET testbeds.

This paper reports a feedback-based manoeuvre planning approach for automated nonprehensile selective micromanipulation of large, microscopic biological objects ( ∼ 100 μ m ). We employ ferromagnetic micro-particles as microrobots actuated via a global magnetic field produced by electromagnetic coils placed in quadrupole configuration. The microrobot motion is programmed to push the target object to the goal location. We employ a three-step approach comprising: (a) generate a collision-free optimal path between the initial and the commanded goal location, (b) generate a manoeuvre planning algorithm that invokes one of the three motion manoeuvres, namely, ‘approach’, ‘push’, and ‘align’ depending upon the instantaneous locations of the microrobot, target object, and the desired waypoint, and (c) deploy a simple proportional controller that determines the currents required in the electromagnetic coils that can produce a suitable magnetic field for executing the manoeuvre invoked by the manoeuvre planner. This paper reports a number of validation experiments conducted on both zebrafish, i.e., Danio rerio embryos and silica beads as target objects. We envisage that the developed inexpensive approach can be useful in robotic manipulation of biological objects with sizes in hundreds of microns including large biological cells, polyploid giant cancer cells (PGCC), multicellular spheroids, Dictyostelium slug, human oocytes, and autophagy candidates. We also believe that functionalising the microrobots with living cells or suitable chemicals will make it possible to perform on-chip biological experiments in future.

Natural rubber latex is an important energy material. However, the harvest of latex is still manual, and there are few researches on automatic work. This paper proposed a method for robot to harvest from each rubber tree without stopping. The robot toggled the collection cup through the flexible actuator, the cup poured out the latex and rotated to the next collection position. In this way, it not only completed the harvest work, but also prepared for the next collection. Aiming at the problem of latex splashing during rapid toggle, the structural parameters of the collection cup and the flexible actuator were modeled and studied. This method made the rotation speed of the collection cup smoother and avoids splashing. To enable the robot to accurately complete the harvesting work, this paper used two-dimensional Light Detection and Ranging (LiDAR) and ranging sensor to locate the space position of the collection cup. The results show that when the toggle speed is 0.5 m/s and the latex volume is 300 ml, the average shaking height is 3.58 mm. In the field test, the lateral error of positioning is less than 8.86 mm, and the height error is less than 0.72 mm; the average harvest rate is 98.18%. The robot has high efficiency and good stability, and can be applied to the rubber plantation to harvest automatically.

Sampling-based, search-based, and optimization-based motion planners are just some of the different approaches developed for motion planning problems. Given the wide variety of application tackled by autonomous mobile manipulators, the question “which planner to choose” may be tough. In this paper, we review the state of the art of the most common approaches, and present a set of benchmarks with the aim to provide not only a theoretical review but also a qualitative/quantitative comparison of the algorithms. Our purpose is to provide an insight and analyze their performance with respect to different metrics. The results are based on an Underwater Vehicle Manipulator System UVMS, although they can be extended to terrestrial and aerial robots as well. The paper uses these results to formalize a set of guidelines for the selection process of the most appropriate approach, for a given problem/requirements.

This paper presents NAR-RM, a method for learning robot reaching motions from a set of demonstrations using Nonlinear AutoRegressive (NAR) polynomial models. Reaching motions are modeled as solutions to autonomous discrete-time nonlinear dynamical systems, so that the movements started near the data of the demonstrations follow the trained trajectories and always reach and stop at the target. Since NAR models obtained using standard system identification techniques do not always adequately model the reaching motions, in this paper we present a method that uses a least-squares estimator with constraints to impose the location of fixed points in the model. With the imposition of new fixed points it is possible to change the location of the original fixed points of the model, thus allowing the learning of stable reaching motions. We evaluate our method using a library of human handwriting motions, a mobile robot and an industrial manipulator.

Existing path and velocity planning methods for car-like vehicles, the paths of which are subject to constraints on the derivative of the curvature in the horizontal plane, do not accurately express the relationships among position, velocity and acceleration in 3D space. Moreover, velocity planning algorithms are efficient only when the curvature and derivative of the curvature have the same velocity demand. As efficiency and comfort are two key issues in promoting planning algorithms, in this paper, the vehicle is allowed to know the nearly shortest-length path and to set a continuous velocity and acceleration profile to track the trajectory reference while taking into account bounds on acceleration (including lateral acceleration) and jerk that are consistent with comfort. First, to construct a nearly shortest path, the 3D path surface is mapped onto the horizontal, profile and frontal planes, and a 2D path smoothing method is applied to solve the 3D path smoothing problem. This method has been used in highway design, but the theoretical understanding of its performance remains limited. This limitation is addressed from the viewpoint of 3D path smoothing in this paper. In addition, the jerk, acceleration, velocity, steering angle and steering angular acceleration profile are merged into a trajectory tracking task to provide a new velocity planning method to find the time-optimal path. Finally, the capabilities of the path and velocity planning methods within general planning schemes are also demonstrated.

Inverse kinematic (IK) problems based on the product of exponentials (POE) model are often transformed to a series of Paden–Kahan subproblems, which are based on geometric methods with geometric constraints. The focus of subproblem is to solve the 3rd order subproblems, among which the 3R-type subproblem is the most prevalent. In this paper, a general frame for the inverse solution of arbitrary 3R types, without geometric constraints, is presented. It can be applied in different 3R types of practical cases, including those with parallel, intersecting, and skewed relationships. This paper mainly focuses on: (1) developing a real algebraic geometric (RAG) method based on the properties of the screw theory and the Rodrigues’ rotation formula; (2) obtaining the closed-form solutions for arbitrary 3R subproblems, and ensuring the accuracies of these solutions; (3) expanding the Paden–Kahan subproblems and meeting the demands of online real-time applications; and finally, (4) verifying the effectiveness of the RAG method, through comparisons with the geometric method, using simulations and real experiments. The proposed frame can be widely applied in series, reconfigurable, and other types of robots.

This paper deals with range-only simultaneous localization and mapping (RO-SLAM), which is of particular interest in aerial robotics where low-weight range-only devices can provide a complementary continuous estimation between robot and landmarks when using radio-based sensors. Range-only sensors work at greater distances when compared to other commonly used sensors in aerial robotics and they are low-cost. However, the spherical shell uniform distribution inherent to range-only observations poses significant technological challenges, restricting the approaches that can be used to solve this problem. This paper presents an undelayed multi-hypothesis Extended Kalman Filter (EKF) approach based on Gaussian Mixture Models (GMM) and a reduced parameterization of the state vector to improve its efficiency. The paper also proposes a new robot-to-landmark and landmark-to-landmark range-only observation model for EKF-SLAM which takes advantage of the reduced parameterization. Finally, a new scheme is proposed for updating hypothesis weights based on an independence of beacon parameters. The method is firstly validated with simulations comparing the results with other state-of-the-art methods and later validated with real experiments for 3D RO-SLAM using several radio-based range-only sensors and an aerial robot.

Legged robots have advanced potential to move in complex environment accomplishing operating, rescuing and detecting tasks. In real applications, bypassing large obstacles is a more common choice for legged robots comparing with walking over and climbing the obstacles. However, few papers involve the obstacle avoidance approach for legged robots. An obstacle avoidance and motion planning scheme for a hexapod robot is presented in this paper. The scheme takes advantage of the superior mobility of the legged robot and fulfills requirements of walking stability and kinematic feasibility. Firstly, a novel obstacle avoidance trajectory planning method is proposed, which is inspired by the superior mobility of the legged robot. Then, a motion generation approach for the legged robot is developed to control the robot to walk along the planned trajectory. The approach coordinates the body motion and the feet motions to fulfill requirements of walking stability and kinematic feasibility simultaneously. Finally, the scheme is integrated on a hexapod robot and tested by real experiments.

This paper describes a new, innovative method by which multiple mobile robots can be detected by a laser scanner. Each robot incorporates bars in its construction which generate a significant pattern in the laser scan. The proposed technique allows a robust detection and control of successively moving robots, despite the partial shadowing through the bars. In this paper the optimal number of bars and their best arrangement for detection is shown. Furthermore the impact of different bar diameters is described. Increased visibility of the bars by the use of multiple laser scanners and their positioning to ensure detection of the robots is also described. Finally it describes the accuracy that can be achieved with this system. The position accuracy was determined by trials on an actual system. Simulations and experiments confirm that this is a reliable and precise method for position determination of multiple robots using a single sensor.

An underwater manipulator is a complex system, highly non-linear and subject to disturbances caused by underwater effects. To obtain a reliable system, robust control strategies have to be designed for the manipulator. The main contribution of this paper is the development of the low-level position/force control structure for an underwater manipulator. The proposed control strategy is planned in the operational space and combines together the parallel control structure for position/force applications with the sliding mode theory and the manipulator model information. The dynamic model of the system incorporates the hydrodynamic effects and an approximation of the end-effector force contact with the environment. This paper presents a method for computing the interaction force at the end-effector in the absence of a force–torque sensor. The control structure is validated through a Lyapunov-stability approach and experimental results. The control structure is tested on a 6 degrees-of-freedom underwater manipulator interacting with the underwater environment.

Thirty-five papers from the ethological literature were surveyed with the perception and reaction of flying animals to autonomous navigation tasks organized and analyzed using a schema theoretic framework. Flying animals are an existence proof of autonomous collision-free flight in unknown and disordered environments. Because they successfully avoid obstacles, self-orient, and evade predators and capture prey to survive, the collected information could inform the design of biologically-inspired behaviors for control of a small unmanned aerial system (SUAS) to improve the current state-of-the art in autonomous obstacle avoidance. Five observations were derived from the surveyed papers: sensing is done by vision in lighted scenarios and sonar in darkness, one sensor is always dominant, adaptive sensing is beneficial, no preference was identified for lateral versus vertical avoidance maneuvers, and reducing speed is consistently seen across species in response to objects in the flight path. Additionally, the questions of defining clutter and scale of speed reduction left unanswered by the literature were discussed. Finally, three rules for control of a SUAS in an unknown environment that restricts maneuverability were identified. These are the distance to begin maneuvers to avoid an obstacle in the flight path, the direction to adjust the flight path, and the role of centered flight in determining the adjustment. Surveyed 35 ethological papers for perception and reaction of flying animals.Bio-inspired motor and perceptual schemas for small unmanned aerial systems.Open questions of defining clutter and scale of speed reduction discussed.Identified three rules for control of a SUAS in an unknown environment.

Legged robots have potential advantages in mobility compared with wheeled robots. Hence, legged robots are widely utilized in outdoor unstructured environments. Human-following operation is one of important tasks for outdoor robots. However, most current human-following strategies requires large amount of computation resource hence difficult to be applied to legged robots. This paper proposes real-time low-computation cost human-following framework in outdoor environment for legged robots. Our method takes a full consideration of the differences between legged robots and wheeled robots. Firstly, an on-line extrinsic calibration method is proposed to calculate the camera coordinate and the world coordinate system. Then, a real-time low-computation cost human-following method utilizing RGBD cameras and 3D LIDAR is proposed. The robot motion considers tracking the leading person while avoiding obstacles. Furthermore, a dynamic alternating tripod trotting gait is developed to control the robot to follow the leading person. Finally, the method is implemented and tested on a hexapod robot Qingzhui with indoor and outdoor experiments. The framework proposed in this paper can be a valuable reference for other legged robots when operated in outdoor environments.

IF=1.95; International audience; This paper studies a robotic cell in which multiple single-armed robot manipulators are equipped for transporting parts between the machines. Compared with most previous studies, processing time window is considered in this paper, i.e. identical parts are processed successively on multiple machines with upper and lower bounds on processing times. The problem discussed in this paper aims at finding optimal cyclic solution for the robot, that is, a sequence of robot moves that can be repeated endlessly and that can achieve the goal of minimum cycle time. Based on the description and analysis, the problem is formulated as a mixed-integer programming model with the objective of minimizing the cycle time. The commercial software CPLEX is used to solve the proposed model. This paper also presents a relaxed mathematical model to compute the lower bound of cycle time. In order to be more efficient, a model-based heuristic algorithm is constructed. Computational experiments on benchmark and randomly generated instances validate the feasibility and efficiency of the proposed heuristic algorithm.

This paper proposes a new type of transformable wheel-legged mobile robot that could be applied on both flat and rugged terrains. It integrates stability and maneuverability of wheeled robot and obstacle climbing capability of legged robot by means of a wheel-legged transformable mechanism. These two modes can be switched easily with two spokes touching terrain. In this paper, the motion analysis of the proposed robot under wheeled mode, legged mode and transformable mode are carried out after briefly introducing the concept and control system design. Then, the obstacle climbing strategies under wheeled and legged modes are obtained. Finally, a prototype of the proposed robot is designed and manufactured based upon the simulation analysis. And the experiment results validate the effectiveness of the proposed transformable wheel-legged mobile robot. A new type of transformable wheel-legged mobile robot is proposed.The transformable mechanism integrates mobility and over-obstacle capability.The applicable condition of obstacle avoidance is given in detail.The experiment results validate the effectiveness of the proposed robot.

Modern laser SLAM (simultaneous localization and mapping) and structure from motion algorithms face the problem of processing redundant data. Even if a sensor does not move, it still continues to capture scans that should be processed. This paper presents the novel filter that allows dropping 2D scans that bring no new information to the system. Experiments on MIT and TUM datasets show that it is possible to drop more than half of the scans. Moreover the paper describes the formulas that enable filter adaptation to a particular robot with known speed and characteristics of lidar. In addition, the indoor corridor detector is introduced that also can be applied to any specific shape of a corridor and sensor.

Among numerous applications of medical robotics, this paper concentrates on the design, optimal use and maintenance of the related technologies in the context of healthcare, rehabilitation and assistive robotics, and provides a comprehensive review of the latest advancements in the foregoing field of science and technology, while extensively dealing with the possible applications of participatory and opportunistic mobile sensing in the aforementioned domains. The main motivation for the latter choice is the variety of such applications in the settings having partial contributions to functionalities such as artery, radiosurgery, neurosurgery and vascular intervention. From a broad perspective, the aforementioned applications can be realized via various strategies and devices benefiting from detachable drives, intelligent robots, human-centric sensing and computing, miniature and micro-robots. Throughout the paper tens of subjects, including sensor-fusion, kinematic, dynamic and 3D tissue models are discussed based on the existing literature on the state-of-the-art technologies. In addition, from a managerial perspective, topics such as safety monitoring, security, privacy and evolutionary optimization of the operational efficiency are reviewed.

Rather than merely dealing with robot localization and mapping in 3D environments, this paper tackles a special topic which could be denominated as-is 3D building modeling with robots. We present a method with which to carry out the automatic 3D scanning of furnished buildings with the aim of obtaining complete and in-depth information regarding the principal visible structural components of an indoor environment (walls, floors and ceilings). This is an essential stage in the automatic creation of as-built BIM models that greatly facilitates the detection of the structure of a building. Our system, which is essentially composed of a mobile robot and a 3D laser scanner, autonomously navigates indoor environments that are occluded and cluttered. The approach can be considered as a structural-component based 3D mapping in which the robot moves to certain positions that are calculated using a new Next Best Scan (NBS) algorithm. We justify the contributions of our work in this research field by means of a broad theoretical comparison with other related methods. Key issues such as the complexity of the scene, occlusion, hypotheses, limitations and autonomy are discussed in the paper. This system has been tested in real and simulated environments. Complex scenes composed of several adjacent non-regular rooms (i.e., concave/convex rooms) have been tested, with promising results.

This paper contributes an extensive review of the history from the former robotics to the present providing a particular emphasis on innovative technical contents of robotic exploration vehicles. To this end, a comprehensive study with a representative collection of 100 mobile robots along the history was performed for which a robot’s statistical profile was obtained considering aspects such as weight, size, number of wheels, and speed of movement. In addition, an exhaustive bibliometric analysis has been conducted over 8120 contributions between 1963 and 2015. The study on the scientific literature found that, thought mobile robotics is a research field being displaced by other disciplines of higher scientific return (e.g., humanoid robots, unmanned aircraft systems or intelligent autonomous vehicles), it is nevertheless confirmed the continuity of mobile robotics with the aim of developing advanced science at the expense of forthcoming space exploration missions. Therefore, this paper attempts to address what is the current state-of-the-art and what are the future challenges set in mobile robotics.

Efficient and reliable slip sensor plays a critical role in grasp of deformable object. This paper provides a new method for slip detection based on video processing, which can offer multidimensional sensing information and it is applicable for gripping deformable objects. The macro camera mounted inside the gripper can capture the dynamic slip and distortion of the object. The features of interest points, detected by the SURF algorithm in subsequent frames, are used to identify the properties of object and calculate the objects changes. To determine the objects slippage, nine detecting windows in the camera view can detect nine tracks of the slippage. Besides, the objects distortion and direction of tangential force on the surface can be determined from these tracks, which helps to the estimation of the objects damage. Experiments on homogeneous and textured materials show that this method can detect the object slippage accurately, and meanwhile provides the tracks of the slippage, distortion information and the force situation. Furthermore, tests for comparison were carried out and the results show that this sensor has a high accuracy in detecting slippage and distortion and the error is acceptable, which has an significant impact in deformable object grasp. This paper provides a new method for slip detection based on video processing.The proposed slip sensor can offer multidimensional sensing information.The detecting method is applicable for gripping deformable objects with both textured and homogeneous surfaces.The experimental results indicate that the proposed method achieves a high detecting accuracy.

Traditional treatments, medication and surgery, for tremor management in Parkinsons disease have shown varying effectiveness carry a risk of significant side effects. Recent research and development of wearable tremor suppression technology have shown a promising third solution for tremor management. This paper presents the design of a novel multi-channel mechatronic splitter (MMS) for use in wearable tremor suppression devices. This mechatronic system allows a single drive motor to support multiple independent outputs. The operation (speed and direction) of the MMS is controlled by a 2 W DC motor. This low power characteristic may provide a promising approach to achieving a prolonged operating life for wearable devices. Furthermore, the size of the MMS can be scaled proportionally according to different applications for optimal performance. This paper describes the design, modeling, implementation and characterization of the MMS. The weight of the MMS prototype is 129 g, the maximum output speed is 120 rpm, and the maximum continuous torque is 0.15 Nm. In addition, recorded tremor motion along with voluntary movement from 7 individuals with Parkinsons disease was used to validate the performance of the MMS. The MMS was controlled to suppress tremor motion while following the voluntary movement of the subject. An average of 12.4% RMS error in voluntary motion tracking was achieved on a dynamic tremor suppression test. A mono-input-multiple-output mechatronic mechanism for tremor suppression is presented.The power transferred from the power source to the output is controlled by a 2 W DC motor.A single drive motor can be used to support multiple independent outputs.The mechanism was validated using tremor data from patients with Parkinsons disease.An average of 12.4% RMS error was achieved on a dynamic tremor suppression test.

In this paper, active tactile exploration for object shape estimation is explored. A prior work suggested to touch the most uncertain part of the estimated shape for minimizing the required number of touches. In this paper, it is pointed out that it may not be the best approach for fast estimation. We propose a novel criterion in active touch point selection for fast estimation, which considers both uncertainty of shape estimation and travel cost to touch. Our method employs a Gaussian process implicit surface model to learn the object shape from tactile information, which allows us to evaluate the uncertainty of the shape estimation with an analytic form. To estimate the travel costs for all the touch candidates, our method utilizes a computationally-efficient graph-based path planning method based on stochastic optimal control theory. Simulations with 2D and 3D objects and real-robot experiments with a 7DOF robot arm and a single finger device equipped with a tactile sensor are conducted. Experimental results verify the effectiveness of our method for fast tactile object shape estimation as compared to passive exploration and active exploration that touches the most uncertain part of the estimated shape. Active tactile exploration for object shape estimation.Consider uncertainty of estimated shape and travel cost to touch for fast estimation.Combine Gaussian process implicit surface with a graph-based path planning.Results indicate faster estimation as compared to comparisons.

Humanoid robots are expected to be integrated into daily life. This requires the robots to perform human-like actions that are easily understandable by humans. Learning by imitation is an effective framework that enables the robots to generate the same motions that humans do. However, it is generally not useful for the robots to generate motions that are precisely the same as learned motions because the environment is likely to be different from the environment where the motions were learned. The humanoid robot should synthesize motions that are adaptive to the current environment by modifying learned motions. Previous research encoded captured human whole-body motions into hidden Markov models, which are hereafter referred to as motion primitives, and generated human-like motions based on the acquired motion primitives. The contact between the body and the environment also needs to be controlled, so that the humanoid robots whole-body motion can be realized in its current environment. This paper proposes a novel approach to synthesizing kinematic data using the motion primitive and controlling the torques of all the joints in the humanoid robot to achieve the desired whole-body motions and contact forces. The experiments demonstrate the validity of the proposed approach to synthesizing and controlling whole-body motions by humanoid robots. This paper proposes the synthesis of whole body motions from stochastic motion primitives.The joint torques are computed during preserving the profile of the synthesized motion and controlling the reaction forces.Simulation demonstrates the validity of the motion synthesis and force control.

A key challenge in designing humanoid robots is producing natural and human-like behaviour. This study addressed this challenge by developing an instructive interface to design natural and human-like behaviour in a humanoid robot that will be helpful not only for professional robot motion designers but also for nonprofessionals. A staging method called contemporary colloquial theatre theory has been developed in the field of theatre to reproduce the natural behaviour of humans. Based on the theory, to design robot behaviour, we extracted implicit knowledge from a director’s instructions given to a robot actor in a robot theatre project, where the robot functions as an actor and interacts with human actors. This paper shows the validity of applying this knowledge extraction method by means of a public stage play entitled Night On The Milky Way Train and two short plays created for the purpose of carrying out an analysis. Our analysis produced important rules for designing the behaviour of humanoid robots. The rules were extracted in forms suited to the instructive function of the interface. In this paper, we report the results of a subjective experiment conducted to verify the effectiveness of this function. Our experimental results suggest that the rules derived are effective in improving robot behaviour.

For automotive applications, an accurate estimation of the ego-motion is required to make advanced driver assistant systems work reliably. The proposed framework for ego-motion estimation involves two components: The first component is the spatial registration of consecutive scans. In this paper, the reference scan is represented by a sparse Gaussian Mixture model. This structural representation is improved by incorporating clustering algorithms. For the spatial matching of consecutive scans, a normal distributions transform-based optimization is used. The second component is a likelihood model for the Doppler velocity. Using a hypothesis for the ego-motion state, the expected radial velocity can be calculated and compared to the actual measured Doppler velocity. The ego-motion estimation framework of this paper is a joint spatial and Doppler-based optimization function which shows reliable performance on real world data and compared to state-of-the-art algorithms.

In this paper we address the trajectory tracking problem of the end-effector of a single link flexible arm in which the gravity forces and the joint friction forces are taken into account. As an overall approach, a double loop cascade control is used to deal with the joint friction: in its inner loop a two-degree-of-freedom controller is responsible for the joint position in the presence of Coulomb friction disturbances, while in its outer loop an input-state feedback linearization-based controller is implemented to suppress the vibrations and track an end-effector trajectory. To highlight the benefits of the proposed controller in presence of joint friction, the controller was compared with other approaches based on feedback linearization reported in the literature, which considered only a single loop. The results of this comparison showed that our approach provides superior performance in terms of settling time, maximum overshoot and residual vibrations. The design of the controller presented relies on a non-linear single-lumped mass dynamic model that describes the dynamics of a single link arm with only one significant vibrational mode. The hypothesis of a single lumped mass model could be considered inaccurate given the distributed nature of the link mass, especially in situations in which the link mass is comparable to the payload mass held by the robot. However, for the parameters of our physical platform two studies are presented to demonstrate that the single lumped mass model is a proper description of the system even in situations in which the link mass is up twice bigger than the payload mass. The results of the paper are demonstrated with co-simulations with Matlab/Simulink and MSC ADAMS, and experimental validation.

Due to the recent technological progress, Human–RobotInteraction (HRI) has become a major field of research in both engineering and artistic realms, particularly so in the last decade. The mainstream interests are, however, extremely diverse: challenges are continuously shifting, the evolution of robot’ skills, as well as the advances in methods for understanding their environment radically impact the design and implementation of research prototypes. When directly deployed in a public installation or artistic performances, robots help foster the next level of understanding in HRI. To this effect, this paper presents a successful interdisciplinary art-science-technology project, the Aerostabiles, leading to a new way of conducting HRI research. The project consists of developing a mechatronic, intelligent platform embodied in multiple geometric blimps – cubes – that hover and move in the air. The artistic context of this project required a number of advances in engineering on the aspects of localization and control systems, flight dynamics, as well as interaction strategies, and their evolution through periods of collective activities called “research–creation residencies”. These events involve artists, engineers, and performers working in close collaboration, sometimes, over several weeks at a time. They generate fruitful exchanges between all researchers, but most of all, they present a unique and creative way to direct and focus the robotics development. This paper represents an overview of the technical contributions from a range of expertise through the artistic drive of the Aerostabiles project.

Simultaneous Localisation and Mapping (SLAM) has become one of key technologies used in advanced robot platform. The current state-of-art indoor SLAM with laser scanning rangefinders can provide accurate realtime localisation and mapping service to mobile robotic platforms such as PR2 robot. In recent years, many modern building designs feature large glass panels as one of the key interior fitting elements, e.g. large glass walls. Due to the transparent nature of glass panels, laser rangefinders are unable to produce accurate readings which causes SLAM functioning incorrectly in these environments. In this paper, we propose a simple and effective solution to identify glass panels based on the specular reflection of laser beams from the glass. Specifically, we use a simple technique to detect the reflected light intensity profile around the normal incident angle to the glass panel. Integrating this glass detection method with an existing SLAM algorithm, our SLAM system is able to detect and localise glass obstacles in realtime. Furthermore, the tests we conducted in two office buildings with a PR2 robot show the proposed method can detect ∼ 95% of all glass panels with no false positive detection. The source code of the modified SLAM with glass detection is released as a open source ROS package along with this paper.

This paper addresses the challenging issue of determining the most suitable control strategy (planning–decision–action and their interactions), for autonomous navigation of vehicles which must deal with different environments contexts (e.g., cluttered or not, dynamic or not, etc.). The paper’s main proposals are decomposed into two main parts: Firstly, the proposition of reliable and flexible components to perform short and long-term planning: at beginning, a generic and safe path planning-based on Parallel Elliptic Limit-Cycle (PELC) and its multi-criteria optimization (PELC ∗ ) have been proposed to perform either reactive or cognitive navigation. Afterward, it is proposed to suitably sequence several PELC/PELC ∗ in order to obtain an optimal g lobal path-based on PELC (gPELC ∗ ). Secondly, this paper proposes an overall Hybrid (reactive/cognitive) multi-controller architecture for autonomous navigation using PELC ∗ and gPELC ∗ . This architecture has been designed in order to use a uniform set-points convention and a common control law to perform several sub-tasks (e.g., obstacle avoidance, target reaching/tracking, path following, etc.). A multitude of simulations and a real experiment have been performed in order to confirm the potentialities of the overall proposed methodology.

Recent advances in visual motion detection and interpretation have made possible the rising of new robotic systems for autonomous and active surveillance. In this line of research, the current work discusses motion perception by proposing a novel technique that analyzes dense flow fields and distinguishes several regions with distinct motion models.The method is called Wise Optical Flow Clustering (WOFC) and extracts the moving objects by performing two consecutive operations: evaluating and resetting. Motion properties of the flow field are retrieved and described in the evaluation phase, which provides high level information about the spatial segmentation of the flow field. During the resetting operation, these properties are combined and used to feed a guided segmentation approach. The WOFC requires information about the number of motion models and, therefore, this paper introduces a model selection method based on a Bayesian approach that balances the models fitness and complexity. It combines the correlation of a histogram-based analysis with the decay ratio of the normalized entropy criterion. This approach interprets the flow field and gives an estimative about the number of moving objects.The experiments conducted in a realistic environment have proved that the WOFC presents several advantages that meet the requirements of common robotic and surveillance applications: is computationally efficient and provides a pixel-wise segmentation, comparatively to other state-of-the-art methods. This paper focuses on clustering the motion from dense optical flow fields.An unsupervised clustering methods are presented and a model selection is proposed.A comparison between the proposed technique with the K-means and EM is made.The experimental evaluations reveal that the proposed technique achieved the best F1 score.The proposed technique is superior in terms of robustness and computational demands.

To construct an intelligent space with a distributed camera sensor network, pre-calibration of all cameras (i.e., determining the absolute poses of each camera) is an essential task that is extremely tedious. This paper considers the automatic calibration method for camera sensor networks based on 3D texture map information of a given environment. In other words, this paper solves a global localization problem for the poses of the camera sensor networks given the 3D texture map information. To manage the complete calibration problem, we propose a novel image descriptor based on quantized line parameters in the Hough space (QLH) to perform a particle filter-based matching process between line features extracted from both a distributed camera image and the 3D texture map information. We evaluate the proposed method in a simulation environment with a virtual camera network and in a real environment with a wireless camera sensor network. The results demonstrate that the proposed system can calibrate complete external camera parameters successfully.

Why should an experimental psychologist bother entering a relatively novel field like social robotics and how could the scientific community in social robotics potentially gain from this? This paper highlights the theoretical and practical gains and challenges associated with an interdisciplinary approach when addressing current research questions in human–machine interaction, with a particular focus on social robotics. Three core issues are discussed from an experimental social psychological perspective: First, this paper focuses on the importance of a scientific, theory-driven approach to test causal relationships between key constructs of interest in the domain of human–machine interaction; second, it addresses the need for advancement in the realm of measurement, e.g., in terms of validity. To illustrate, this paper provides an overview of the author’s own research in the domain of human–robot interaction in which we have tried to live up to the aforementioned desiderata. Finally, following common Research-Development practice, we argue for bridging the gap between foundational and applied research by testing the validity of the theoretical assumptions and the robustness of our technical platforms in real-world human–machine interaction settings.

Many variants of the Iterative Closest Point (ICP) algorithm have been proposed for registering point clouds. This paper explores the performance of 20,736 ICP variants applied to the registration of point clouds for the purpose of terrain mapping, using data obtained from a mobile platform. The methodology of the study has involved taking sequences of 100 consecutive scans at three distinct scenes along the route of a mining haul truck operating in a typical surface mining environment. The scan sequences were obtained at 20 Hz from a Velodyne HDL-64E mounted on the truck. The aim is to understand how well the ICP variants perform in consolidating these scans into sub-maps. Variants are compared against three metrics: accuracy, precision, and relative computational cost. The main finding of the paper is that none of the variants is simultaneously accurate, precise, and fast to compute, across all three scenes. The best performing variants employed strategies that filtered the data sets, used local surface geometry in the form normals, and used the distance between points in one point cloud to a corresponding surface from a reference point cloud as a measure of the fit between two point clouds. The significance of this work is that it: (i) provides guidance in the construction of ICP variants for terrain mapping; and (ii) identifies the significant limitations of existing ICP variants for this application.