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The paper presents a method for decentralized flocking and global formation building for a network of unicycle-like robots described by the standard kinematics equations with hard constraints on the robots linear and angular velocities. We propose decentralized motion coordination control algorithms for the robots so that they collectively move in a desired geometric pattern from any initial position. There are no predefined leaders in the group and only local information is required for the control. The effectiveness of the proposed control algorithms is illustrated via computer simulations and experiments with real robots. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of guiding a mobile robot towards a target using only range sensors. The bearing information is not available. The target can be stationary or moving. It can be the source of some gas leakage or nuclear radiation or it can be some landmark or beacon or any manoeuvring vehicle. The mobile robot can be a ground vehicle or an aerial vehicle flying at a fixed altitude. In literature, many different strategies are proposed which use the range only measurement but they involve estimation of different parameters or have switching control strategy which make them difficult to implement. We propose two sets of conditions, one for stationary target and another for both stationary and moving target. Any control strategy, that will satisfy these conditions, can bring the robot arbitrarily close to the target. There are no restrictions on the initial conditions. Estimation of any parameter is not required. Some candidate controllers are presented that included continuous controllers and switching controllers. Simulations are carried out with these controllers to validate our result with and without measurement noise. Experimental results with ground mobile robot are presented. [ABSTRACT FROM AUTHOR]

Target search and tracking is a classical but difficult problem in many research domains, including computer vision, wireless sensor networks and robotics. We review the seminal works that addressed this problem in the area of swarm robotics , which is the application of swarm intelligence principles to the control of multi-robot systems. Robustness , scalability and flexibility , as well as distributed sensing, make swarm robotic systems well suited for the problem of target search and tracking in real-world applications. We classify the works we review according to the variations and aspects of the search and tracking problems they addressed. As this is a particularly application-driven research area, the adopted taxonomy makes this review serve as a quick reference guide to our readers in identifying related works and approaches according to their problem at hand. By no means is this an exhaustive review, but an overview for researchers who are new to the swarm robotics field, to help them easily start off their research. [ABSTRACT FROM AUTHOR]

A novel path-planning algorithm is proposed for a tracked mobile robot to traverse uneven terrains, which can efficiently search for stability sub-optimal paths. This algorithm consists of combining two RRT-like algorithms (the Transition-based RRT (T-RRT) and the Dynamic-Domain RRT (DD-RRT) algorithms) bidirectionally and of representing the robot–terrain interaction with the robot’s quasi-static tip-over stability measure (assuming that the robot traverses uneven terrains at low speed for safety). The robot’s stability is computed by first estimating the robot’s pose, which in turn is interpreted as a contact problem, formulated as a linear complementarity problem (LCP), and solved using the Lemke’s method (which guarantees a fast convergence). The present work compares the performance of the proposed algorithm to other RRT-like algorithms (in terms of planning time, rate of success in finding solutions and the associated cost values) over various uneven terrains and shows that the proposed algorithm can be advantageous over its counterparts in various aspects of the planning performance. [ABSTRACT FROM AUTHOR]

When a user and a robot share the same physical workspace the robot may need to keep an updated 3D representation of the environment. Indeed, robot systems often need to reconstruct relevant parts of the environment where the user executes manipulation tasks. This paper proposes a spatial attention approach for a robot manipulator with an eye-in-hand Kinect range sensor. Salient regions of the environment, where user manipulation actions are more likely to have occurred, are detected by applying a clustering algorithm based on Gaussian Mixture Models applied to the user hand trajectory. A motion capture sensor is used for hand tracking. The robot attentional behavior is driven by a next-best view algorithm that computes the most promising range sensor viewpoints to observe the detected salient regions, where potential changes in the environment have occurred. The environment representation is built upon the PCL KinFu Large Scale project [1], an open source implementation of KinectFusion. KinFu has been modified to support the execution of the next-best view algorithm directly on the GPU and to properly manage voxel data. Experiments are reported to illustrate the proposed attention based approach and to show the effectiveness of GPU-based next-best view planning compared to the same algorithm executed on the CPU. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new wireless hybrid control algorithm for visual servoing of mobile robots. In particular, the hybrid system is developed using the two autonomous control algorithms, i.e., position-based visual servoing (PBVS) and image-based visual servoing (IBVS). The PBVS algorithm is used for global routing, whereas the IBVS algorithm is used for the fine navigation. It helps in the specific steering towards the desired point for approaching the searched object. The proposed algorithm requires only the desired and actual poses of the mobile robot, and does not need any additional requirements such as the map of the environment or artificial landmarks. For the linearization of output signals, neural network extended Kalman filter is used. Several experimental as well as simulation results are presented in order to show the applicability of the proposed algorithm in the hybrid vision based control of mobile robots. [ABSTRACT FROM AUTHOR]

RGBD sensors are commonly used in robotics applications for many purposes, including 3D reconstruction of the environment and mapping. In these tasks, uncalibrated sensors can generate poor quality results. In this paper we propose a quick and easy to use approach to estimate the undistortion function of RGBD sensors. Our approach does not rely on the knowledge of the sensor model, on the use of a specific calibration pattern or on external SLAM systems to track the device position. We compute an extensive representation of the undistortion function as well as its statistics and use machine learning methods for approximation of the undistortion function. We validated our approach on datasets acquired from different kinds of RGBD sensors and using a precise 3D ground truth. We also provide a procedure for evaluating the quality of the calibration using a mobile robot and a 2D laser range finder. The results clearly show the advantages in using sensor data calibrated with the method described in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we develop an autonomous construction system in which self-contained ground robots build a protective barrier by means of compliant pockets. We present a stochastic control algorithm based on two biological mechanisms–stigmergy and templates–that takes advantage of compliant pockets for autonomous construction with single and multiple robots. The control algorithm guides the robot(s) to build the protective barrier without relying on a central planner, an external computer, or a motion capture system. We propose a statistical model to represent the structures built with the compliant pockets, and we provide a set of criteria for assessing the performance of the proposed system. To demonstrate the feasibility of the proposed system, real-robot and simulation experiments were carried out. The results show the viability of the proposed autonomous construction system. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of detecting possible misbehavior in a group of autonomous mobile robots, which coexist in a shared environment and interact with each other and coordinate according to a set of common interaction rules . Such rules specify what actions each robot is allowed to perform in order to interact with the other members of the group. The rules are distributed, i.e., they can be evaluated only starting from the knowledge of the individual robot and the information the robot gathers from neighboring robots. We consider misbehaving those robots which, because of either spontaneous failures or malicious tampering, do not follow the rules and whose behavior thus deviates from the nominal assigned one. The main contribution of the paper is to provide a methodology to detect such misbehavior by observing the congruence of actual behavior with the assigned rules as applied to the actual state of the system. The presented methodology is based on a consensus protocol on the events observed by robots. The methodology is fully distributed in the sense that it can be performed by individual robots based only on the local available information, it has been theoretically proven and validated with experiments involving real aerial heterogeneous robots. [ABSTRACT FROM AUTHOR]

This paper proposes a design of global output-feedback path-following controllers for unicycle-type mobile robots based on the recently developed level curve approach. The controllers are designed based on Lyapunov’s direct and backstepping methods to force the position of the robot to satisfy the equation of the path and to move along the path tangentially at a desired linear velocity. An interlaced observer, which is a reduced-order observer plus an interlaced term, is designed to estimate the robot unmeasured velocities and disturbances. The observer design is based on a coordinate transformation that transforms the robot dynamics to a new form, which does not contain velocity quadratic terms. The interlaced term is determined during the control design to void difficulties due to observer errors and unbounded reference path derivatives. Simulations are included to illustrate the effectiveness of the proposed concept. [ABSTRACT FROM AUTHOR]

Monte Carlo localization is a powerful and popular approach in mobile robot localization. Line segment-based maps provide a compact and scalable representation of indoor environments for mobile robot navigation. But Monte Carlo localization has seldom been studied in the context of line segment-based maps. A key step of the approach–and one that can endow it with or rob it of the attributes of accuracy, robustness and efficiency–is the computation of the so called importance weight associated with each particle. In this paper, we propose a new method for the computation of importance weights on maps represented with line segments, and extensively study its performance in pose tracking. We also compare our method with three other methods reported in the literature and present the results and insights thus gathered. The comparative study, conducted using both simulated and real data, on maps built from real data available in the public domain clearly establish that the proposed method is more accurate, robust and efficient than the other methods. [ABSTRACT FROM AUTHOR]

Quadruped robots offer better maneuverability over wheeled mobile robots. However, a quadruped robot contains many joint actuators which have to operate in a coordinated fashion to achieve the desired locomotion. Joint actuations cause various degrees of disturbance on the robot body and may even destabilize the system. Thus, prior dynamic analysis plays an important role for development of control laws for quadruped locomotion. Here, a three dimensional dynamic model of a quadruped has been developed using the bond graph technique which can be interfaced with various controller models. This model contains a detailed sub-model for telescopic compliant legs. Results from simulations, animations and experiments are discussed. Turning motion at various leg speeds is studied for dynamic stability of the robot. The effect of leg compliance on locomotion parameters is studied which helps in selecting a suitable compliance. Performance measure is carried out using energy efficiency as deciding criteria. Study on energy efficient quadruped structure, energy efficient locomotion gait and foot trajectory have been carried out for designing an efficient quadruped. [ABSTRACT FROM AUTHOR]

Personal Mobility Robots, such as the Seqway may be the remedy for the transportation related problems in the congested environment, especially for the last and first mile problems of the elderly people. However, the vehicle segmentation issues for the mobility robots, impede the use of these devices on the shared paths. The mobility reports can only be used in the designated areas and private facilities. The traffic regulatory institutions lack robot–society interaction database. In this study, we proposed methods and algorithms which can be employed on a widespread computing device, such as an Android tablet, to gather travel information and rider behavior making use of the motion and position sensors of the tablet PC. The methods we developed, first filter the noisy sensor readings using a complementary filter, then align the body coordinate system of the device to the Segway’s motion coordinate. A couple of state of the art classification methods are integrated to classify the braking states of the Segway. The classification algorithms are not limited to classification of the braking states, but they can be used for other motion related maneuvers on the road surfaces. The detected braking states and the other classified features related to the motion are reflected to the screen of the Android tablet to inform the rider about the riding and motion conditions. The developed Android application also gathers these travel information to build a National database for further statistical analysis of the robot–society interaction. [ABSTRACT FROM AUTHOR]

Several anonymous Dubins-car like mobile robots travel in a planar environment that hosts a scalar field, like the level of radiation or concentration of a contaminant. The objective is to co-operatively detect and localize the boundary of the set where the field value exceeds a certain threshold. The robots suffer from deficits of competence, communication, perception, and maneuverability: they do not know the field profile a priory, are not aware of the team size, cannot communicate with and recognize one another, can measure only the value of the field at the current location, are subjected to nonholonomic constraints, and are able to move along paths of only limited curvatures. We propose a new decentralized navigation strategy that drives all robots to the desired environmental boundary, with subsequent stable circulation along it. This strategy is based on an autonomous control of every robot, prevents collisions between them and ultimately ensures their pseudo-uniform distribution over the boundary to better utilize the resources of the team for representatively portraying the boundary. Furthermore, the proposed control scheme does not employ gradient estimation, which typically needs ineffective concentration of robots into tight clusters, and is non-demanding with respect to both computation and motion. Its mathematically rigorous justification is provided. The effectiveness of the proposed guidance law is confirmed by computer simulations and real-world experiments. [ABSTRACT FROM AUTHOR]

We present a mobile robot with sufficient computing power to simulate up to a quarter of a million neurons in real-time. We use this computing power, combined with various on-board sensory and motor systems (including silicon retinae) to implement a novel method for learning sensorimotor competences by example. That is, by temporarily manually controlling the robot, it can gather information about what sensorimotor mapping it should be performing. We show that such a learning-by-example system is well-suited to power efficient neuron-based computation (60 W for all quarter of a million neurons), that it can learn quickly (a few tens of seconds), and that its learning generalizes well to novel situations. [ABSTRACT FROM AUTHOR]

All physical systems must reliably extract information from their noisy and partially observable environment and build an internal representation of space to orient their behaviour. Precise egomotion estimation is important to keep external (i.e. environmental information) and internal (i.e. proprioception) cues coherent. The constructed representation subsequently defines the space of possible actions. Due to the multimodal nature of incoming streams of sensory information, egomotion estimation is a challenging sensor fusion problem. In this paper we present a distributed cortically inspired processing scheme for sensor fusion, which given various sensory inputs, and simple relations defining inter-sensory dependencies, relaxes into a solution which provides a plausible interpretation of the perceived environment. The proposed model has been implemented for egomotion estimation on an autonomous mobile robot. We demonstrate that the model provides a precise estimate of both robot position and orientation. [ABSTRACT FROM AUTHOR]

This paper presents a complete framework for reactive and flexible autonomous vehicle navigation. A human driver reactively guides a vehicle to the final destination while performing a smooth trajectory and respecting the road boundaries. The objective of this paper is to achieve similar behavior in an unmanned ground vehicle to reach a static or dynamic target location. This is achieved by using a flexible control law based on a novel definition of control variables and Lyapunov synthesis. Furthermore, a target assignment strategy to enable vehicle navigation through successive waypoints in the environment is presented. An elementary waypoint selection method is also presented to perform safe and smooth trajectories. The asymptotic stability of the proposed control strategy is proved. In addition, an accurate estimation of the maximum error boundary, according to the controller parameters, is given. With this indicator, the vehicle navigation will be safe within a certain boundaries. Simulations and experiments are performed in different cases to demonstrate the flexibility, reliability and efficiency of the control strategy. Our proposal is compared with different navigation methods from the literature such as those based on trajectory following. [ABSTRACT FROM AUTHOR]

In this paper, we present a new, generic approach for Simultaneous Localization and Mapping (SLAM). First of all, we propose an abstraction of the underlying sensor data using Normal Distribution Transform (NDT) maps that are suitable for making our approach independent from the used sensor and the dimension of the generated maps. We present several modifications for the original NDT mapping to handle free-space measurements explicitly. We additionally describe a method to detect and handle dynamic objects such as moving persons. This enables the usage of the proposed approach in highly dynamic environments. In the second part of this paper we describe our graph-based SLAM approach that is designed for lifelong usage. Therefore, the memory and computational complexity is limited by pruning the pose graph in an appropriate way. [ABSTRACT FROM AUTHOR]

We present a decision theoretic approach to mobile robot exploration. The method evaluates the reduction of joint path and map entropy and computes a potential information field in robot configuration space using these joint entropy reduction estimates. The exploration trajectory is computed descending on the gradient of this field. The technique uses Pose SLAM as its estimation backbone. Very efficient kernel convolution mechanisms are used to evaluate entropy reduction for each sensor ray, and for each possible robot orientation, taking frontiers and obstacles into account. In the end, the computation of this field on the entire configuration space is shown to be very efficient. The approach is tested in simulations in a pair of publicly available datasets comparing favorably both in quality of estimates and in execution time against an RRT ∗ -based search for the nearest frontier and also against a locally optimal exploration strategy. [ABSTRACT FROM AUTHOR]

Kidnapping occurs when a robot is unaware that it has not correctly ascertained its position, potentially causing severe map deformation and reducing the robot’s functionality. This paper presents metric-based techniques for real-time kidnap detection, utilising either linear or SVM classifiers to identify all kidnapping events during the autonomous operation of a mobile robot. In contrast, existing techniques either solve specific cases of kidnapping, such as elevator motion, without addressing the general case or remove dependence on local pose estimation entirely, an inefficient and computationally expensive approach. Three metrics that measured the quality of a pose estimate were evaluated and a joint classifier was constructed by combining the most discriminative quality metric with a fourth metric that measured the discrepancy between two independent pose estimates. A multi-class Support Vector Machine classifier was also trained using all four metrics and produced better classification results than the simpler joint classifier, at the cost of requiring a larger training dataset. While metrics specific to 3D point clouds were used, the approach can be generalised to other forms of data, including visual, provided that two independent ways of estimating pose are available. [ABSTRACT FROM AUTHOR]

This review paper focuses on different aspects of perceived safety for a number of autonomous physical systems. This is a major aspect of robotics research, as more and more applications allow humans and autonomous systems to share their space, with crucial implications both on safety and on its perception. The alternative terms used to express related concepts (e.g., psychological safety, trust, comfort, stress, fear, and anxiety) are listed and explained. Then, the available methods to assess perceived safety (i.e., questionnaires, physiological measurements, behavioral assessment, and direct input devices) are described. Six categories of autonomous systems are considered (industrial poly-articulated manipulators, indoor mobile robots, mobile manipulators, humanoid robots, drones, and autonomous vehicles), providing an overview of the main themes related to perceived safety in the specific domain, a description of selected works, and an analysis of how motion and characteristics of the system influence the perception of safety. The survey also discusses experimental duration and location of the reviewed papers, and the connection between perceived safety and safety standards. • This review paper focuses on perceived safety for different autonomous systems. • Alternative terms used to express perceived safety are listed and explained. • Methods to assess perceived safety are described and discussed. • Considered robots: industrial and mobile manipulators, mobile robots and humanoids. • We also consider other autonomous systems, i.e., drones and autonomous vehicles. [ABSTRACT FROM AUTHOR]

Recent image-based robotic systems use predicted future state images to control robots. Therefore, the prediction accuracy of the future state image affects the performance of the robot. To predict images, most previous studies assume that the camera captures the entire scene and that the environment is static. However, in real robot applications, these assumptions do not always hold. For example, if a camera is attached to a mobile robot, its view changes from time to time. In this study, we analyzed the relationship between the performance of the image prediction model and the robot's behavior, controlled by an image-based navigation algorithm. Through mobile robot navigation experiments using front-faced and omni-directional cameras, we discussed the capabilities of the image prediction models and demonstrated their performance when applied to the image-based navigation algorithm. Moreover, to adapt to the dynamic changes in the environment, we studied the effectiveness of directing the camera to the ceiling. We showed that robust navigation can be achieved without using images from cameras directed toward the front or the floor, because these views can be disturbed by moving objects in a dynamic environment. • Empirical evaluation and comparison of image prediction models on a mobile robot. • Action-conditioned LSTM-based prediction model performs well across camera types. • Omni-directional camera is effective for image prediction models. • Omni-directional camera is effective for image-based mobile robot navigation tasks. [ABSTRACT FROM AUTHOR]

Multi-Agent Path Finding has been widely studied in the past few years due to its broad application in the field of robotics and AI. However, previous solvers rely on several simplifying assumptions. This limits their applicability in numerous real-world domains that adopt nonholonomic car-like agents rather than holonomic ones. In this paper, we give a mathematical formalization of the Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem. We propose a novel hierarchical search-based solver called Car-Like Conflict-Based Search to address this problem. It applies a body conflict tree to address collisions considering the shapes of the agents. We introduce a new algorithm called Spatiotemporal Hybrid-State A* as the single-agent planner to generate agents' paths satisfying both kinematic and spatiotemporal constraints. We also present a sequential planning version of our method, sacrificing a small amount of solution quality to achieve a significant reduction in runtime. We compare our method with two baseline algorithms on a dedicated benchmark and validate it in real-world scenarios. The experiment results show that the planning success rate of both baseline algorithms is below 50% for all six scenarios, while our algorithm maintains that of over 98%. It also gives clear evidence that our algorithm scales well to 100 agents in 300 m × 300 m scenario and is able to produce solutions that can be directly applied to Ackermann-steering robots in the real world. The benchmark and source code are released in https://github.com/APRIL-ZJU/CL-CBS. The video of the experiments can be found on YouTube. • An efficient method to solve path planning problem for multiple car-like robots. • Adopts search-based algorithm to generate paths satisfying agents' kinematic constraints. • Scales well to dozens of agents in simulation and useful for real-world applications. [ABSTRACT FROM AUTHOR]

In a world with rapidly growing levels of automation, robotics is playing an increasingly significant role in every aspect of human endeavour. In particular, many types of mobile robots are increasingly being utilised in places and for tasks that are difficult and dangerous for humans. Although the vision of fully autonomous mobile robotic platforms that can perform complex tasks without direct guidance from a human operator is compelling, the reality is that the current state of robotics technology is still a long way from being able to achieve this capability outside of very narrowly constrained contexts. Technology advancement for improved mobile robotic teleoperation and remote control is vital to enable robotic vehicles to operate with increasing autonomy levels while allowing for effective remote operation when task complexity is too great for the autonomous systems. Being motivated to bridge this gap, we present a review of existing teleoperation methods and enhancement techniques for control of mobile robots. After defining teleoperation, we provide a detailed review that analyses, categorises, and summarises existing mobile robot teleoperation methods. Next, we highlight existing enhancement techniques that have been applied to these teleoperation methods, along with their relative advantages and disadvantages. Finally, several promising future research directions are identified. The paper concludes with a discussion of research challenges and future research possibilities. [ABSTRACT FROM AUTHOR]

Due to the increasing complexity of today's tasks and the demand for higher performance and robustness, using a single robot is not always expedient in robotics applications. Therefore, having multiple autonomous robotic agents collaborate utilizing explicit communication is gaining more attention. The goal of this article is to develop a distributed algorithm that allows the formation control of multiple differentially-driven mobile robots. The formation control goal is formulated in a novel manner by leveraging results on the control of a single differentially-driven mobile robot, which is sophisticated due to the present non-holonomic constraint. This results in a nonlinear distributed control problem. The fundamental functionality of the developed algorithm is analyzed in simulation scenarios. The applicability to real-life scenarios is demonstrated through experiments with custom hardware. To the best of the authors' knowledge, this is the first time that nonlinear distributed model predictive control is applied to a formation of differentially-driven mobile robots using a theoretically-founded cost function and, moreover, that the results are verified with hardware experiments. • Control of a non-holonomic mobile robot in real-life experiments to any setpoint. • Kinematic output description for a formation of non-holonomic mobile robots. • Cooperative distributed control scheme ensuring the convergence to any setpoint. • Multiple experiments show the algorithm's applicability to real-life scenarios. [ABSTRACT FROM AUTHOR]

Despite the increasing popularity of the cloud robotics paradigm, the literature on the field still lacks comprehensive analysis on several aspects of the technology. Therefore, the adoption of common standards and frameworks is fundamental for developing the field and allowing practical works to be reproduced and compared. This work presents a ROS-based open framework for robot-cloud communication, easing the integration of robotics and remote cloud platforms, and discusses the implementation of the overall cloud robotics stack over open source software and commercial off-the-shelf devices. Additionally, we present two practical implementations in which most of the computation is carried out remotely and perform a series of experiments to demonstrate our technique. Our results indicate that task times can be reduced up to 15% when using remote cloud platforms even under 150 ms average communication latency over the public Internet while observing figures as low as 2% on throughput loss in sensor data transmission. In general, such results point to the feasibility of the presented approach in different classes of applications, even under non-ideal network and cloud settings. • Introduce an open framework based on ROS for robot-cloud communication. • Our framework requires no external network configuration for use over the Internet. • Propose a repeatable methodology for implementing cloud robotics. • Our work is validated considering different applications and cloud platforms setups. • Cloud computation can outperform embedded computation even in complex networks. [ABSTRACT FROM AUTHOR]

Most of the existing path planning algorithms require a prior global map. Although there have been some algorithms proposed for unknown environments, they can only deal with those which just have several hidden obstacles in a roughly known global map. In order to improve the efficiency of robot's path planning without prior global maps, this paper proposes a Convolutionally Evaluated Gradient First Search (CE-GFS) path planning algorithm. It allows the robot to collect environmental information and complete path planning simultaneously. Firstly, the Gradient First Search (GFS) algorithm is proposed based on the gradient score parameter, with which the conventional cost function is replaced. The GFS can adapt to any moving direction through the environmental information surrounding the mobile robot and computing the gradient score parameter. Secondly, CE-GFS path planning algorithm is proposed based on GFS and convolutional evaluation method. The CE-GFS helps the robots to evaluate the efficiency of the global path and get global perception ability, so that they are prevented from going astray, which can significantly improve the efficiency of path planning. Finally, several simulation tests and field tests have been carried out. The test results show that convolutional evaluation improves the efficiency of CE-GFS by 86.94% on average compared with GFS at the price of 0.18% decrease in path planning success rate. Moreover, the time cost of the proposed CE-GFS algorithm is 42.18% less than that of FAR-Planner in some special cases. • Our CE-GFS gets rid of this dependence on an exploration algorithm, and allows the robot to combine the exploration and path planning stages. • Cost functions in conventional grid map algorithms are replaced with gradient score in our algorithm, to make the robot more flexible. • Convolutional evaluation method is proposed to evaluate the efficiency of the global path through local information, which can prevent the robot from going astray. [ABSTRACT FROM AUTHOR]

Self-reconfigurable robots are systems built from a collection of modules that have the ability to form themselves into many different topologies. This includes forming robots of different shapes, but also includes dividing and merging to form different numbers of physically independent robots during the course of the mission. We call systems of this type reconfigurable teams. The challenge with such systems is to utilize these capabilities in an effective fashion to complete the given mission. In this work, we describe a behavior-based algorithm and an auction-based algorithm, each inspired by previous work in mobile robot task allocation, to control reconfigurable teams. For each technique, we extend the traditional methods by giving them the ability to utilize the dividing and merging operations to improve efficiency of the overall mission. We then apply these algorithms to missions of exploration and placement of surveillance sensors, showing that both techniques successfully avail themselves of the reconfigurations in an efficient and effective manner. [ABSTRACT FROM AUTHOR]

Experience demonstrates that autonomous mobile robots running in the field in a dynamic environment often breakdown. Generally, mobile robots are not designed to efficiently manage faulty or unforeseen situations. Even if some research studies exist, there is a lack of a global approach that really integrates dependability and particularly fault tolerance into the mobile robot design. This paper presents an approach that aims to integrate fault tolerance principles into the design of a robot real-time control architecture. A failure mode analysis is firstly conducted to identify and characterize the most relevant faults. Then the fault detection and diagnosis mechanisms are explained. Fault detection is based on dedicated software components scanning faulty behaviors. Diagnosis is based on the residual principle and signature analysis to identify faulty software or hardware components and faulty behaviors. Finally, the recovery mechanism, based on the modality principle, proposes to adapt the robot’s control loop according to the context and current operational functions of the robot. This approach has been applied and implemented in the control architecture of a Pioneer 3DX mobile robot. [ABSTRACT FROM AUTHOR]

The problem of homing a mobile robot to a given reference location under unknown relative and absolute positions is addressed in this paper. This problem is easy to solve when all the positions and kinematic variables are known or are observable, but remains a challenge when only range is measured. Its complexity further increases when variable and unknown drifts are added to the motion, which is typical for marine vehicles. Based on the range measurements, it is possible to drive the robot arbitrarily close to the reference. This paper presents a complete solution and demonstrates the validity of the approach based on the Lyapunov theory. The use of models, which are often affected by uncertainties and/or unmodeled terms, is intended to be minimal and only some constraints are imposed on the speed of the robot. We derive a control law that makes the robot converge asymptotically to the reference and prove its stability theoretically. Nevertheless, as it is well known, practical limitations on the actuation can weaken some properties of convergence, namely when the system dynamics require increasing actuation along the approach trajectory. We will demonstrate that the robot reaches a positively invariant set around the reference whose upper bound is determined. Finally, we conclude our work by presenting simulation and experimental data and by demonstrating the validity and the robustness of the method. [ABSTRACT FROM AUTHOR]

An autonomous mobile robot in a human’s living space should be able to realize not only collision-free motion, but also human-centered motion, i.e., motion giving priority to a moving human according to the situation. In this study, we propose a real-time obstacle avoidance method for an autonomous mobile robot that considers the robot’s dynamic constraints, personal space, and the human’s directional area using grid-based X – Y – T space path planning. The proposed method generates collision-free motion in which the robot can give way to humans. The relative position, velocity and avoidance motion with respect to the robot varies from person to person. To show the effectiveness of the proposed method to human-like motion, we verify the robot’s motion under several assumed scenarios by changing the initial state of both the robot and the human. Moreover, we verify the robot’s motion with respect to a simulated human based on the human-like behavior approach. Through these simulations, we confirm that the proposed method is able to generate safe human-centered motion under several assumed scenarios. Additionally, the effectiveness of the proposed method in practice is confirmed by experiments in which the human’s position and velocity are estimated using a laser range finder. [ABSTRACT FROM AUTHOR]

The evolution of contemporary mobile robotics has given thrust to a series of additional conjunct technologies. Of such is the semantic mapping, which provides an abstraction of space and a means for human–robot communication. The recent introduction and evolution of semantic mapping motivated this survey, in which an explicit analysis of the existing methods is sought. The several algorithms are categorized according to their primary characteristics, namely scalability, inference model, temporal coherence and topological map usage. The applications involving semantic maps are also outlined in the work at hand, emphasizing on human interaction, knowledge representation and planning. The existence of publicly available validation datasets and benchmarking, suitable for the evaluation of semantic mapping techniques is also discussed in detail. Last, an attempt to address open issues and questions is also made. [ABSTRACT FROM AUTHOR]

We propose an efficient time-optimal trajectory planning algorithm for differential-driven wheeled mobile robots with bounded motor control inputs in the environment with cranked road where two corners with arbitrary angles exist. Based on dynamics for differential-driven wheeled mobile robots including actuating motors as well as on the bang–bang principle for time-optimality, we plan the time-optimal free path trajectory which unifies path planning and trajectory generation. Thus it improves total time significantly while handling obstacle avoidance. We divide our trajectory into three sections and then divide each section into an appropriate number of subsections to make five subsections in total. We introduce the concept of transition angle to solve the problem with formulating search loops efficiently. Numerical results are provided to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Time to contact or time to collision (TTC) is utmost important information for animals as well as for mobile robots because it enables them to avoid obstacles; it is a convenient way to analyze the surrounding environment. The problem of TTC estimation is largely discussed in perspective images. Although a lot of works have shown the interest of omnidirectional camera for robotic applications such as localization, motion, monitoring, few works use omnidirectional images to compute the TTC. In this paper, we show that TTC can be also estimated on catadioptric images. We present two approaches for TTC estimation using directly or indirectly the optical flow based on de-rotation strategy. The first, called “gradient based TTC”, is simple, fast and it does not need an explicit estimation of the optical flow. Nevertheless, this method cannot provide a TTC on each pixel, valid only for para-catadioptric sensors and requires an initial segmentation of the obstacle. The second method, called “TTC map estimation based on optical flow”, estimates TTC on each point on the image and provides the depth map of the environment for any obstacle in any direction and is valid for all central catadioptric sensors. Some results and comparisons in synthetic and real images will be given. [ABSTRACT FROM AUTHOR]

Path planning is one of the basic and interesting functions for a mobile robot. This paper explores the application of Bacterial Foraging Optimization to the problem of mobile robot navigation to determine the shortest feasible path to move from any current position to the target position in an unknown environment with moving obstacles. It develops a new algorithm based on Bacterial Foraging Optimization (BFO) technique. This algorithm finds a path towards the target and avoiding the obstacles using particles which are randomly distributed on a circle around a robot. The criterion on which it selects the best particle is the distance to the target and the Gaussian cost function of the particle. Then, a high level decision strategy is used for the selection and thus proceeds for the result. It works on local environment by using a simple robot sensor. So, it is free from having generated additional map which adds cost. Furthermore, it can be implemented without requirement to tuning algorithm and complex calculation. To simulate the algorithm, the program is written in C language and the environment is created by OpenGL. To test the efficiency of the proposed technique, results are compared with Basic Bacterial Foraging Optimization (BFO) and another well-known algorithm called Particle Swarm Optimization (PSO) to give the guarantee that the proposed method gives better and optimal path. [ABSTRACT FROM AUTHOR]

This paper proposes a new pose estimation algorithm in the framework of robotic navigation problems. The algorithm gives the mobile robot (MR) pose on the basis of the difference between the MR desired pose and the MR current pose. In this regard the MR sensor readings and the readings of a virtual sensor are employed. The algorithm is advantageous in comparison with other pose estimation algorithms including those based on classical filter approaches because of the small computation time. Simulation and real-world experimental results are included to illustrate the effectiveness of the pose estimation algorithm and its potential for integration in MR control structures and algorithms. [ABSTRACT FROM AUTHOR]

In mobile robotics, feature based maps are very popular for the representation of the environment. Some of the main advantages of these maps are final compactness and expressivity, aspects that make storage easier and simplify higher level reasoning. Most existing approaches, however, stick to low level features such as points, segments and sometimes circles, corners or splines. This paper presents the incorporation of rectangles as higher level features in a feature based graph Simultaneous Localization and Mapping (SLAM) framework for the consideration of the structure of the environment in the mapping process. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of relating the motion of a fixed-wing unmanned aerial vehicle (UAV) with a less constrained agent (LCA) whose motion pattern is comparable to unmanned ground vehicles (UGVs). The main challenge of the scenario is different velocity constraints for UAV and the LCA. To rise to the challenge, for fixed-wing UAV a novel moving center trajectory is proposed whose center is determined by the LCA. Moreover, the sufficient conditions on the proposed trajectory for fulfillment of UAV constraints are derived. In the next step, ambient disturbances like wind gust are considered for UAV heading rate computation which are mitigated by employment of the well known sliding mode controller. Simulation results validate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Due to the advancements of robotic systems, they are able to be employed in more unstructured outdoor environments. In such environments the robot–terrain interaction becomes a highly non-linear function. Several methods were proposed to estimate the robot–terrain interaction: machine learning methods, iterative geometric methods, quasi-static and fully dynamic physics simulations. However, to the best of our knowledge there has been no systematic evaluation comparing those methods. In this paper, we present a newly developed iterative contact point estimation method for static stability estimation of actively reconfigurable robots. This new method is systematically compared to a physics simulation in a comprehensive evaluation. Both interaction models determine the contact points between robot and terrain and facilitate a subsequent static stability prediction. Hence, they can be used in our state space global planner for rough terrain to evaluate the robot’s pose and stability. The analysis also compares deterministic versions of both methods to stochastic versions which account for uncertainty in the robot configuration and the terrain model. The results of this analysis show that the new iterative method is a valid and fast approximate method. It is significantly faster compared to a physics simulation while providing good results in realistic robotic scenarios. [ABSTRACT FROM AUTHOR]

Operations of transportation in cluttered environments require robust motion planning algorithms. Specially with large and heavy vehicles under hazardous operations of maintenance, such as in the ITER, an international nuclear fusion research project. The load transportation inside the ITER facilities require smooth and optimized paths with safety margin of 30 cm. The transportation is accomplished by large rhombic-like vehicles to exploit its kinematic capabilities. This paper presents the performance analysis of a motion planning algorithm to optimize trajectories in terms of clearance, smoothness and execution time in cluttered scenarios. The algorithm is an upgraded version of a previous one used in ITER, replacing the initialization implemented using Constrained Delaunay Triangulation by the Fast Marching Square. Exhaustive simulated experiments have been carried out in different levels of ITER buildings, comparing the performance of the algorithm using different metrics. [ABSTRACT FROM AUTHOR]

Discriminating or classifying different terrains is an important ability for every autonomous mobile robot. A variety of sensors, preprocessing techniques, and algorithms in different robots were applied. However, little attention was paid to the way sensory data was generated and to the contribution of different sensory modalities. In this work, a quadruped robot traversing different grounds using a variety of gaits is used, equipped with a collection of proprioceptive (encoders on active, and passive compliant joints), inertial, and foot pressure sensors. The effect of different gaits on classification performance is assessed and it is demonstrated that separate terrain classifiers for each motor program should be employed. Furthermore, poor performance of randomly generated motor commands confirms the importance of coordinated behavior on sensory information structuring. The collection of sensors sensitive to active, “tactile”, terrain exploration proved effective. Among the individual modalities, encoders on passive compliant joints delivered best results. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for image-based visual servoing, extending the existing works that use the trifocal tensor (TT) as source for image measurements. In the proposed approach, singularities typically encountered in this kind of methods are avoided. A formulation of the TT-based control problem with a virtual target resulting from the vertical translation of the real target allows us to design a single controller, able to regulate the robot pose towards the desired configuration, without local minima. In this context, we introduce a super-twisting control scheme guaranteeing continuous control inputs, while exhibiting strong robustness properties. Our approach is valid for perspective cameras as well as catadioptric systems obeying the central camera model. All these contributions are supported by convincing numerical simulations and experiments under a popular dynamic robot simulator. [ABSTRACT FROM AUTHOR]

This paper presents novel contributions on image-based control of a mobile robot using a general catadioptric camera model. A catadioptric camera is usually made up by a combination of a conventional camera and a curved mirror resulting in an omnidirectional sensor capable of providing 360 ° panoramic views of a scene. Modeling such cameras has been the subject of significant research interest in the computer vision community leading to a deeper understanding of the image properties and also to different models for different types of configurations. Visual servoing applications using catadioptric cameras have essentially been using central cameras and the corresponding unified projection model. So far only in a few cases more general models have been used. In this paper we address the problem of visual servoing using the so-called radial model. The radial model can be applied to many camera configurations and in particular to non-central catadioptric systems with mirrors that are symmetric around an axis coinciding with the optical axis. In this case, we show that the radial model can be used with a non-central catadioptric camera to allow effective image-based visual servoing (IBVS) of a mobile robot. Using this model, which is valid for a large set of catadioptric cameras (central or non-central), new visual features are proposed to control the degrees of freedom of a mobile robot moving on a plane. In addition to several simulation results, a set of experiments was carried out on Robot Operating System (ROS)-based platform which validates the applicability, effectiveness and robustness of the proposed method for image-based control of a non-holonomic robot. [ABSTRACT FROM AUTHOR]

This work presents a set of techniques to improve the cross-compatibility between the Webots simulator and the educational e-puck mobile robot. The work is motivated by the need of a realistic simulation environment, especially designed to consider the unique challenges associated to multi-robot systems (MRS), such as the robot-robot interaction (RRI). This article starts by proposing a new Webots programming architecture that provides full control of the e-puck's firmware. By doing so, one can easily improve the already existing e-puck's functionalities on Webots, as well as adding new functionalities required by most MRS applications. Among the range of new possible functionalities, this article describes the successful implementation of the microphone, the speaker, and the Bluetooth for robot-robot communication. Due to the limitations of the e-puck's Bluetooth modules, this work goes even further by bringing some insights about the Gumstix Overo COM turret and how robot-robot communication may be achieved using WiFi technology. The outcomes provided by the experiments conducted using both virtual and real e-pucks under the Webots framework open new doors for low-cost multi-robot applications. [ABSTRACT FROM AUTHOR]

Vector field SLAM is a framework for localizing a mobile robot in an unknown environment by learning the spatial distribution of continuous signals such as those emitted by WiFi or active beacons. In our previous work we showed that this approach is capable of keeping a robot localized in small to medium sized areas, e.g. in a living room, where four continuous signals of an active beacon are measured (Gutmann et al., 2012). In this article we extend the method to larger environments up to the size of a complete home by deploying more signal sources for covering the expanded area. We first analyze the complexity of vector field SLAM with respect to area size and number of signals and then describe an approximation that divides the localization map into decoupled sub-maps to keep memory and run-time requirements low. We also describe a method for re-localizing the robot in a vector field previously mapped. This enables a robot to resume its navigation after it has been kidnapped or paused and resumed. The re-localization method is evaluated in a standard test environment and shows an average position accuracy of 10 to 35 cm with a localization success rate of 96 to 99%. Additional experimental results from running the system in houses of up to 125 m2 demonstrate the performance of our approach. The presented methods are suitable for commercial low-cost products including robots for autonomous and systematic floor cleaning. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we address the problem of robot navigation in environments with deformable objects. The aim is to include the costs of object deformations when planning the robot’s motions and trade them off against the travel costs. We present our recently developed robotic system that is able to acquire deformation models of real objects. The robot determines the elasticity parameters by physical interaction with the object and by establishing a relation between the applied forces and the resulting surface deformations. The learned deformation models can then be used to perform physically realistic finite element simulations. This allows the planner to evaluate robot trajectories and to predict the costs of object deformations. Since finite element simulations are time-consuming, we furthermore present an approach to approximate object-specific deformation cost functions by means of Gaussian process regression. We present two real-world applications of our motion planner for a wheeled robot and a manipulation robot. As we demonstrate in real-world experiments, our system is able to estimate appropriate deformation parameters of real objects that can be used to predict future deformations. We show that our deformation cost approximation improves the efficiency of the planner by several orders of magnitude. [Copyright &y& Elsevier]

Abstract: New modifications of VFH+ and V FH∗ (Vector Field Histogram) methods are presented in the paper. The features of the histogramic algorithms are generally improved and also using a potential of advanced sensors such as laser scanners is enabled. The main focus of the work is an introduction of modifications that provides collision-free navigation in environments with moving obstacles. For this purpose, a new approach in creating a look-ahead tree is proposed. All new modifications are analyzed and experimentally verified. The results obtained using conventional and modified methods have been compared. Experiments have demonstrated the validity and the technical feasibility of the proposed methods. [Copyright &y& Elsevier]

The autonomous multi-robot system is an emerging technology that has a wide range of potential applications, such as environmental monitoring, exploration of unknown area, battlefield surveillance, and search and rescue. One major challenge in such applications is how to deploy each robotic agent autonomously in a distributed manner. In this paper, we proposed a distributed coverage control strategy named multi-stage virtual force interaction scheme (VFIS), where the agents' deployment process is split into stages and each agent iteratively seeks its next position according to the interaction among agents and the interaction between agents and the perceived environment. The interactions are realized via virtual repulsive forces and virtual vortex forces, where the latter are newly proposed to enhance the exploration capability of agents. We also designed a group of benchmark testing problems for the mission of monitoring coverage of complex environments with unknown obstacles. Extensive simulation experiments were conducted based on the defined benchmark configurations and the results showed a favorable performance of the invented strategy. In addition, practical experiments were carried out using a group of mobile robots, which validated the effectiveness of the proposed method. • A virtual-force-based approach was designed and employed in the multi-robot monitoring coverage task. • A group of benchmark problems were designed for the simulation test. • A novel virtual vortex force was introduced. • The effectiveness was validated by the practical experiments using nine mobile robots. • The robustness was verified through the comparison with the state of the arts. [ABSTRACT FROM AUTHOR]

In order for a mobile robot to be able to effectively operate in complex, dynamic environments it must be capable of understanding both where and what the objects around them are. In this paper we introduce the semantic probability hypothesis density (SPHD) filter, which allows robots to simultaneously track multiple classes of targets despite measurement uncertainty, including false positive detections, false negative detections, measurement noise, and target misclassification. The SPHD filter is capable of incorporating a different motion model for each type of target and of functioning in situations where the number of targets is unknown and time-varying. To demonstrate the efficacy of the SPHD filter, we conduct both simulated and hardware tests with multiple target types containing both static and dynamic targets. We show that the SPHD filter allows effective tracking of multiple classes of targets even with detection error to some level, and performs better than a collection of PHD filters running in parallel, one for each target class. We also provide a detailed methodology that practitioners can use to fit the probabilistic sensor models necessary to run the SPHD filter. • Adding semantic information improves the performance of multi-target trackers. • Practitioners can procedurally generate semantic measurement models. • Semantic tracking provides robots with a more nuanced understanding of the world. [ABSTRACT FROM AUTHOR]