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• A constrained path planning algorithm for Ackerman mobile robot is proposed. • An improved ant colony algorithm with angle constraint is introduced. • An improved B-spline curve smoothing method with curvature constraint is proposed to ensure that the smooth path follows the maximum curvature limit of the mobile robot. In this paper, a new approach is proposed for the smooth path planning of Ackermann mobile robots based on an improved ant colony algorithm and B-spline curves. Firstly, by incorporating path length constraints and path smoothing constraints into the objective function, the smooth path planning problem for Ackermann mobile robots is transformed into a multi-objective optimization problem. Secondly, to address the limitations of the traditional ant colony algorithm, an improved ant colony algorithm based on the turning angle constraint (IACO-TAC) is proposed. IACO-TAC incorporates the distance factor and steering angle penalty factor in the heuristic function to reduce the path search's blindness. Moreover, the pheromone update method is improved, consisting of local pheromone update and global pheromone update, which uses a reward penalty mechanism to improve the convergence speed of the algorithm and increases the pheromone concentration of the global optimal path, respectively. Thirdly, an improved B-spline curve smoothing algorithm that considers the minimum turning radius constraint is proposed to generate a path that satisfies the kinematic constraints of the Ackermann mobile robot. Finally, the proposed method is evaluated by conducting gradient comparison experiments and ant colony algorithm comparison experiments on maps of different sizes. The experimental results demonstrate that our method exhibits a fast convergence rate and plans a path that balances path length and turn frequency while satisfying the kinematic constraints of the mobile robot. Thus, the proposed method offers an efficient and smooth path planning solution for Ackermann mobile robots in complex environments. [ABSTRACT FROM AUTHOR]

Localization is a self-position estimation problem and is one of the most critical research areas in autonomous mobile robots. This study presents novel solutions to two significant practical challenges in localization. The initial challenge pertains to the lack of distinct features within the environment, while the second involves the imperfectness of the map. Many studies are based on Extended Kalman Filter (EKF) and they need specific features from the environment for localization. The edges and corners are the popular natural feature types to be used in grid maps. The proposed study promises accurate and fast position tracking in the grid maps which have very few corners and edges. To achieve high performance in such a challenging map, we propose the virtual dynamic point landmark (VDPL) approach in this paper. VDPL does not need specific features such as edges and corners, and any part of the map can be used as a landmark. Since the real-world applications are based on imperfect maps, mostly generated by SLAM (Simultaneous Localization and Mapping) algorithm, the probabilistic distribution of the measurement model and measurement predictions are incorrect. In this study, the position errors that occur as a result of this incorrectness are alleviated by modifying the EKF algorithm. The modified equations of EKF for taking into account the map errors are clearly shown in the paper. The efficiency of the proposed solution is demonstrated in multiple simulations which are performed in randomly generated maps. Moreover, the benefits and real-time performance of the proposed approach are provided by real-world tests using an autonomous wheelchair platform. We believe that the methods developed in this study will improve the localization performance of autonomous robots that operate in challenging environments in terms of feature structure. [ABSTRACT FROM AUTHOR]

Collaboration of multiple mobile robots becomes important in situations where collaborative tasks are required. For this purpose, a method including obstacle detection based on collaborative path planning of multiple Autonomous Mobile Robots (AMRs) has been developed. This study ensures the usability of collaborative omnidirectional AMRs technologies in various fields. In the study, novel path planning and obstacle avoidance algorithms are developed for collaborative mobile robots with omnidirectional mobility. Essentially, these algorithms include motion planning performed by obstacle avoidance with two identical 3-wheel omnidirectional mobile robots (TWOMR). Numerical calculations of the collaborative algorithm have been performed after the kinematic calculations, and tested algorithms developed for the proposed model. As a result, it has been observed that the path planning and obstacle avoidance algorithms developed for collaborative omnidirectional AMRs successfully follow the Master robot in the most efficient trajectory without collision and it has been observed that the developed method works with high accuracy. [Display omitted] • The most efficient collision-free trajectory planning for collaborative mobile robots. • Path planning and obstacle avoidance algorithm at the safe following distance. • Path planning of collaborative autonomous omnidirectional mobile robots. • A novel method is used to realize avoidance motion for robots successfully. [ABSTRACT FROM AUTHOR]

The Robot Operating System 2 (ROS 2) is rapidly impacting the intelligent machines sector — on space missions, large agriculture equipment, multi-robot fleets, and more. Its success derives from its focused design and improved capabilities targeting product-grade and modern robotic systems. Following ROS 2's example, the mobile robotics ecosystem has been fully redesigned based on the transformed needs of modern robots and is experiencing active development not seen since its inception. This paper comes from the desks of the key ROS Navigation maintainers to review and analyze the state of the art of robotics navigation in ROS 2. This includes new systems without parallel in ROS 1 or other similar mobile robotics frameworks. We discuss current research products and historically robust methods that provide differing behaviors and support for most every robot type. This survey consists of overviews, comparisons, and expert insights organized by the fundamental problems in the field. Some of these implementations have yet to be described in literature and many have not been benchmarked relative to others. We end by providing a glimpse into the future of the ROS 2 mobile robotics ecosystem. • ROS 2's Nav2 has a number of new algorithms and capabilities suitable for a large number of applications. • This survey analyzes ROS 2 algorithm's capabilities and benchmarks performance. • Explains systems provided to build robust autonomous navigation systems in dynamic environments. [ABSTRACT FROM AUTHOR]

This paper focuses on the mobile robot path planning problem of optimizing the performance metrics of bidirectional A* algorithm in randomized two-dimensional map environments. An algorithm called direction constraints adaptive extended bidirectional A* (DCAE-BA*), which is an improvement of the traditional target dynamic bidirectional A* algorithm (TTD-BA*), is proposed to improve the performance metrics of the algorithm. Regarding the improvement, we propose the adaptive extension method and the direction-constrained optimal node extension method (DCONE). Simulation experiments were conducted for DCAE-BA*, TTD-BA* and traditional A* algorithm (A*) in a large number of random two-dimensional map environments. The simulation experimental scenarios consider four types of start and end point relative directions and three obstacle proportions to objectively and comprehensively evaluate the performance of the proposed algorithms. The results show that different scenarios have a significant impact on the algorithm performance metrics. Finally, the overall performance of the proposed algorithm is evaluated with a large number of experiments in "random" scenarios, and the results show that DCAE-BA* obtains significantly better search time for all three obstacle proportions, and better path length and number of expanded nodes for 10% and 25% obstacle proportions. The effectiveness of the proposed DCAE-BA* algorithm is demonstrated, which provides an essential reference for the path planning of mobile robots in a random 2D map environment. • DCAE-BA* is improved by adaptive extension and direction-constrained optimal node extension (DCONE). • The Start-End Dir and obstacle proportions have a significant impact on the algorithm metrics. • Overall, the DCAE-BA* has the highest computational efficiency for all three types of obstacle proportion. • DCAE-BA* optimizes the path length in the "diagonal" scenario most significantly. • As the proportion of obstacles increases, the metric optimization effect decreases. [ABSTRACT FROM AUTHOR]

Abstract 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. [ABSTRACT FROM AUTHOR]

Abstract Despite the comprehensive development in the field of navigation algorithms for mobile robots, the research on performance metrics and evaluation procedures for making standardized quantitative comparison between different algorithms has gained attention only recently. This work attempts to contribute with such effort by introducing a performance metric for the assessment of collision avoidance algorithms for mobile robots. The proposed metric comprehensively evaluates the actions taken by the objects and their consequences, in a given scenario of any given collision avoidance algorithm, based on the concept of probability of collision. The contribution of the paper encompasses the definition of the metric, the methodology to estimate the metric, and the framework to apply the metric for any given scenario. Experiments and numerical simulations are conducted to validate and demonstrate the effectiveness of the proposed metric in performance evaluation and comparison among different collision avoidance algorithms. [ABSTRACT FROM AUTHOR]

In order to move safely and accurately, mobile platforms using steerable wheels require adequate coordination of their actuators. One possibility to achieve actuator coordination is to control the motion of the chassis’ instantaneous centre of rotation (ICR) and motion around it. Considering the chassis as a rigid body, the ICR is located at the intersection of each wheel’s zero motion axis. In practice however, these axes may not concur, in particular when compliant actuators are used for wheel steering. They then no more define precisely an ICR and only an estimation of its position can be computed. Moreover, most parametrizations of the ICR position bring in singularities with no physical meaning, which hinder estimation. This paper introduces the H representation, a new parametrization of the motion state space free of any non-structural singularities, and presents an algorithm which estimates the ICR within the joint space. The proposed approach is compared in terms of reliability, efficiency, accuracy and robustness with three methods working within the operational space. Results suggest that the proposed estimation approach provides the best compromise for these performance indicators. [ABSTRACT FROM AUTHOR]

The paper details the first successful attempt at using model checking techniques to verify the correctness of distributed algorithms for robots evolving in a continuous environment. The study focuses on the problem of rendezvous of two robots with lights. There exist many different rendezvous algorithms that aim at finding the minimal number of colors needed to solve rendezvous in various synchrony models (e.g. , FSYNC, SSYNC, ASYNC). While these rendezvous algorithms are typically very simple, their analysis and proof of correctness tend to be extremely complex, tedious, and error-prone as impossibility results are based on subtle interactions between the activation schedules of the robots. The paper presents a generic verification model that can be concretely expressed in available software model-checkers. In particular, we explain the subtle design decisions that allow to keep the search space finite and tractable, as well as prove several important theorems that support them. As a sanity check, we use the model to verify several known rendezvous algorithms in six different models of synchrony. In each case, we find that the results obtained from the model checker are consistent with the results known in the literature. The model checker outputs a counter-example execution in every case that is known to fail. In the course of developing and proving the validity of the model, we identified several fundamental theorems, including the ability for a well chosen algorithm and ASYNC scheduler to produce an emerging property of memory in a system of oblivious mobile robots, and why it is not a problem when robots executing the gathering algorithms are equipped with lights. [Display omitted] • We describe a model to verify mobile robot gathering algorithms that evolve in a Euclidean continuous space. • We show that positive results obtained for the model extend to the initial system model. • We model check all known mobile robot gathering algorithms and verify previous pen-and-paper results. • We obtain new insights about key system hypotheses about problem solvability. • We introduce weaker models of consistency (inspired by regular registers). • We derive a new algorithm that solves mobile robot gathering in such a model. [ABSTRACT FROM AUTHOR]

Multi-robot conflict resolution is a challenging problem, especially in dense environments where many robots must operate safely in a confined space. Centralized solutions do not scale well with the number of robots in dynamic scenarios: a centralized communication can cause bottlenecks and may not be robust enough when channels are unreliable; the complexity of algorithms grows with the number of robots, making online re-computation too expensive in many situations. In this work, we propose a decentralized approach for conflict resolution where robots show reactive and safe behaviors, avoiding collisions with both static and dynamic objects, even under unreliable communication conditions and with low resources. They detect conflicts with neighboring obstacles locally and then apply rules to surround them in a roundabout fashion, assuming that others will follow the same policy. The method is designed for unicycle robots with range-finder sensors, and it is able to cope with noisy sensors and second-order dynamic constraints, ensuring always collision-free navigation. Besides, a set of metrics and scenarios for benchmarking in multi-robot collision avoidance are proposed. We also compare our method with others from the state of the art through extensive simulations. Experiments with real robots are also presented in order to show the feasibility of the system. [ABSTRACT FROM AUTHOR]

Many robots are limited in their operating capabilities, both computational and energy-wise. A strong desire exists to keep computation cost and energy consumption to a minimum when executing tasks like object recognition with a mobile robot. Adaptive action selection is a paradigm, offering great flexibility in trading off the cost of acquiring information against making robust and reliable inference under uncertainty. In this paper, we study active multi-view object recognition and describe an information-theoretic framework that combines and unifies two common techniques: online feature selection for reducing computational costs and view planning for resolving ambiguities and occlusions. Our algorithm adaptively chooses between the two strategies of either selecting only the features that are most informative to the recognition, or moving to a new viewpoint that optimally reduces the expected uncertainty on the identity of the object. This two step process allows us to keep overall computation cost minimal but simultaneously increase recognition accuracy. Extensive empirical studies on a large RGB-D dataset, and with two different feature sets, have validated the effectiveness of the proposed framework. Our experiments show that dynamic feature selection alone reduces the computation time at runtime 2.5–6 times and, when combining it with viewpoint selection, we significantly increase the recognition accuracy on average by 8%–18% absolute compared to systems that do not use these two strategies. By establishing a link between active object recognition and change detection, we were further able to use our framework for the follow-up task of actively detecting object change. Furthermore, we have successfully demonstrated the framework’s applicability to a low-powered quadcopter platform with limited operating time. [ABSTRACT FROM AUTHOR]

Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures. [ABSTRACT FROM AUTHOR]

Designing intelligent and robust autonomous navigation systems remains a great challenge in mobile robotics. Inverse reinforcement learning (IRL) offers an efficient learning technique from expert demonstrations to teach robots how to perform specific tasks without manually specifying the reward function. Most of existing IRL algorithms assume the expert policy to be optimal and deterministic, and are applied to experiments with relatively small-size state spaces. However, in autonomous navigation tasks, the state spaces are frequently large and demonstrations can hardly visit all the states. Meanwhile the expert policy may be non-optimal and stochastic. In this paper, we focus on IRL with large-scale and high-dimensional state spaces by introducing the neural network to generalize the expert’s behaviors to unvisited regions of the state space and an explicit policy representation is easily expressed by neural network, even for the stochastic expert policy. An efficient and convenient algorithm, Neural Inverse Reinforcement Learning (NIRL), is proposed. Experimental results on simulated autonomous navigation tasks show that a mobile robot using our approach can successfully navigate to the target position without colliding with unpredicted obstacles, largely reduce the learning time, and has a good generalization performance on undemonstrated states. Hence prove the robot intelligence of autonomous navigation transplanted from limited demonstrations to completely unknown tasks. [ABSTRACT FROM AUTHOR]

Localization of mobile robots is still an important topic, especially in case of dynamically changing, complex environments such as in Urban Search & Rescue (USAR). In this paper we aim for improving the reliability and precision of localization of our multimodal data fusion algorithm. Multimodal data fusion requires resolving several issues such as significantly different sampling frequencies of the individual modalities. We compare our proposed solution with the well-proven and popular Rauch–Tung–Striebel smoother for the Extended Kalman filter. Furthermore, we improve the precision of our data fusion by incorporating scale estimation for the visual modality. [ABSTRACT FROM AUTHOR]

This paper presents a concept for path planning of mobile robots in household environments. The proposed algorithm takes communicative means of locomotion into account in order to facilitate human–robot interaction. The well-known traditional artificial potential field method (APF) was extended by motion characteristics of household animals. The proposed algorithm includes velocity and orientation information and can be used in unknown, dynamic environments. The main contribution of this paper is the definition of an online, local path planning method by adapting animal motion attributes in order to assist human–robot interaction. The algorithm was implemented in an embedded system and evaluated on MOGI-ETHON, a holonomic drive mobile robot. [ABSTRACT FROM AUTHOR]

Mobile robot platforms capable of operating safely and accurately in dynamic environments can have a multitude of applications, ranging from simple delivery tasks to advanced assembly operations. These abilities rely heavily on a robust navigation stack, which requires stable and accurate pose estimations within the environment. To solve this problem, a modular localization system suitable for a wide range of mobile robot platforms was developed. By using LIDAR/RGB-D data, the proposed system is capable of achieving 1–2 cm in translation error and 1°–3° degrees in rotation error while requiring only 5–35 ms of processing time (in 3 and 6 DoF respectively). The system was tested in three robot platforms and in several environments with different sensor configurations. It demonstrated high accuracy while performing pose tracking with point cloud registration algorithms and high reliability when estimating the initial pose using feature matching techniques. The system can also build a map of the environment with surface reconstruction and incrementally update it with either the full field of view of the sensor data or only the unknown sections, which allows to reduce the mapping processing time and also gives the possibility to update a CAD model of the environment without degrading the detail of known static areas due to sensor noise. [ABSTRACT FROM AUTHOR]

Autonomous mobile robots (AMRs) interacting with an a priori distributed wireless sensor network (WSN) in a region can address the three-tier challenge of navigating in unknown environments: (i) identifying target locations, (ii) planning paths to the targets, and (iii) efficiently executing the navigation paths to the targets. This paper presents low-complexity algorithms to address the second-tier and third-tier challenges, i.e., efficiently planning and executing paths to target locations. These novel approaches use only the information inherent in WSNs, i.e., received signal strength (RSS). The objective is to have the AMR navigate to a target location by: (i) producing an RSS-based artificial magnitude distribution in the navigation region, (ii) using particle filtering based bearing estimation for orientation information, and (iii) using interpolated pseudogradient for efficient path planning and navigation. Here, the AMR does not require: (i) the global location information for itself or the WSN, (ii) a priori information of the direction of a target location, or (iii) sophisticated ranging equipment for prior mapping. The AMR relies only on local, neighborhood information and low-cost wireless directional antennas for navigation. Real-world and simulation experiments, using a variety of node-densities, demonstrate the effectiveness of the proposed schemes. The low-cost, low-complexity advantages of the WSN–AMR interactive navigation provide for efficient map-less and ranging-less navigation methods. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

Robotic odor source localization (OSL) has been viewed as a challenging task due to the turbulent nature of airflows and the resulting odor plume characteristics. The key to solving an OSL problem is designing an effective olfactory-based navigation algorithm, which guides a plume-tracing robot to find the odor source via tracing emitted plumes. Inspired by the mate-seeking behaviors of male moths, this article presents a behavior-based navigation algorithm for using on a mobile robot to locate an odor source in an unknown environment. Unlike traditional bio-inspired algorithms, which use fixed parameters to formulate robot search trajectories, we design a fuzzy controller to perceive the environment and adjust trajectory parameters based on the current search situation. Therefore, the robot can automatically adapt the scale of search trajectories to fit environmental changes and balance the exploration and exploitation of the search. Simulation and on-vehicle results show that compared to two classical olfactory-based navigation algorithms, the proposed algorithm is more efficient and outperforms them in terms of the averaged search time and success rate. [ABSTRACT FROM AUTHOR]

Abstract: The global localization problem for mobile robots is addressed in this paper. In this field, the most common approaches solve this problem based on the minimization of a quadratic loss function or the maximization of a probability distribution. The distances obtained from the perceptive sensors are used together with the predicted ones (from the estimates in the known map) to define a cost function or a probability to optimize. In our previous work, we developed an optimization-based global localization module that used evolutionary computation concepts. In particular, the algorithm engine was the Differential Evolution method. In this work, this algorithm has been modified including the minimization of the Kullback–Leibler divergence between true observations and estimates. This divergence is used to calculate the cost function of the localization module. The algorithm has been tested in different situations and the most important improvement is the ability to cope with different types of occlusions. [Copyright &y& Elsevier]

Semantic segmentation is a crucial task in emerging robotic applications like autonomous driving and social robotics. State-of-the-art methods in this field rely on deep learning, with several works in the literature following the trend of using larger networks to achieve higher performance. However, this leads to greater model complexity and higher computational costs, which make it difficult to integrate such models on mobile robots. In this work we investigate how it is possible to obtain lighter performing deep models introducing additional data at a very low computational cost, instead of increasing the network complexity. We consider the features used in the 3D Entangled Forests algorithm, proposing different strategies to integrate such additional information into different deep networks. The new features allow to obtain lighter and performing segmentation models, either by shrinking the network size or improving existing networks proposed for real-time segmentation. Such result represents an interesting alternative in mobile robotics application, where computational power and energy are limited. [ABSTRACT FROM AUTHOR]

Abstract: The paradigm case for robotic mapping assumes large quantities of sensory information which allow the use of relatively weak priors. In contrast, the present study considers the mapping problem for a mobile robot, CrunchBot, where only sparse, local tactile information from whisker sensors is available. To compensate for such weak likelihood information, we make use of low-level signal processing and strong hierarchical object priors. Hierarchical models were popular in classical blackboard systems but are here applied in a Bayesian setting as a mapping algorithm. The hierarchical models require reports of whisker distance to contact and of surface orientation at contact, and we demonstrate that this information can be retrieved by classifiers from strain data collected by CrunchBot’s physical whiskers. We then provide a demonstration in simulation of how this information can be used to build maps (but not yet full SLAM) in an zero-odometry-noise environment containing walls and table-like hierarchical objects. [Copyright &y& Elsevier]

Abstract: Due to their wide field of view, omnidirectional cameras are becoming ubiquitous in many mobile robotic applications.  A challenging problem consists of using these sensors, mounted on mobile robotic platforms, as visual compasses (VCs) to provide an estimate of the rotational motion of the camera/robot from the omnidirectional video stream. Existing VC algorithms suffer from some practical limitations, since they require a precise knowledge either of the camera-calibration parameters, or the 3-D geometry of the observed scene. In this paper we present a novel multiple-view geometry constraint for paracatadioptric views of lines in 3-D, that we use to design a VC algorithm that does not require either the knowledge of the camera calibration parameters, or the 3-D scene geometry. In addition, our algorithm runs in real time since it relies on a closed-form estimate of the camera/robot rotation, and can address the image-feature correspondence problem. Extensive simulations and experiments with real robots have been performed to show the accuracy and robustness of the proposed method. [Copyright &y& Elsevier]

Abstract: Terrain assessment and path planning for mobile robots are intrinsically linked. There exists a variety of terrain assessment algorithms and these methods follow the trend of low-fidelity at low-cost and high-fidelity at high-cost. We present a modular path-planning algorithm that uses a hierarchy of terrain-assessment methods, from low-fidelity to high-fidelity. Using the available sensor data, the visible terrain is first assessed with the low-fidelity, low-cost method. The decision to assess a piece of terrain with the high-fidelity, high-cost method is made considering potential path benefits and the cost of assessment. This can be thought of as providing a means to triage large amounts of terrain data. The result is a lower combined cost of the path and terrain assessment that exploits the capabilities of the robot chassis where prudent. We demonstrate a system using one implementation of the technique on a large number of simulated path planning problems in fractal terrain. Additionally, we provide results and system details from an experimental field test carried out on Devon Island, Canada. [Copyright &y& Elsevier]

Abstract: We present a sliding mode based strategy for a unicycle-like robot navigation and guidance. The proposed navigation law is applied to the problems of patrolling the border of a moving and deforming domain and reaching a target through a dynamic environment cluttered with moving obstacles. Mathematically rigorous analysis of the proposed approach is provided. The convergence and performance of the algorithm is demonstrated via experiments with real robots and extensive computer simulation. [Copyright &y& Elsevier]

Abstract: This article focuses on human navigation, by proposing a system for mapping and self-localization based on wearable sensors, i.e., a laser scanner and a 6 Degree-of-Freedom Inertial Measurement Unit (6DOF IMU) fixed on a helmet worn by the user. The sensor data are fed to a Simultaneous Localization And Mapping (SLAM) algorithm based on particle filtering, an approach commonly used for mapping and self-localization in mobile robotics. Given the specific scenario considered, some operational hypotheses are introduced in order to reduce the effect of a well-known problem in IMU-based localization, i.e., position drift. Experimental results show that the proposed solution leads to improvements in the quality of the generated map with respect to existing approaches. [Copyright &y& Elsevier]

Abstract: We present an algorithm to detect doors in images. The key to the algorithm’s success is its fusion of multiple visual cues, including standard cues (color, texture, and intensity edges) as well as several novel ones (concavity, the kick plate, the vanishing point, and the intensity profile of the gap below the door). We use the Adaboost algorithm to determine the linear weighting of the various cues. Formulated as a maximum a posteriori probability (MAP) problem, a multi-cue functional is minimized by a data-driven Markov chain Monte Carlo (DDMCMC) process that arrives at a solution that is shown empirically to be near the global minimum. Intensity edge information is used in the importance probability distribution to drive the Markov chain dynamics in order to achieve a speedup of several orders of magnitude over traditional jump diffusion methods. Unlike previous approaches, the algorithm does not rely upon range information and yet is able to handle complex environments irrespective of lighting conditions, reflections, wall or door color, or the relative orientation between the camera and the door. Moreover, the algorithm is designed to detect doors for which the lintel is occluded, which often occurs when the camera on a mobile robot is low to the ground. The versatility of the algorithm is tested on a large database of images collected in a wide variety of conditions, on which it achieves approximately 90% detection rate with a low false positive rate. Versions of the algorithm are shown for calibrated and uncalibrated camera systems. Additional experiments demonstrate the suitability of the algorithm for near-real-time applications using a mobile robot equipped with off-the-shelf cameras. [Copyright &y& Elsevier]

Abstract: This paper presents a cooperative distributed approach for searching odor sources in unknown structured environments with multiple mobile robots. While searching and exploring the environment, the robots independently generate on-line local topological maps and by sharing them with each other they construct a global map. The proposed method is a decentralized frontier based algorithm enhanced by a cost/utility evaluation function that considers the odor concentration and airflow at each frontier. Therefore, frontiers with higher probability of containing an odor source will be searched and explored first. The method also improves path planning of the robots for the exploration process by presenting a priority policy. Since there is no global positioning system and each robot has its own coordinate reference system for its localization, this paper uses topological graph matching techniques for map merging. The proposed method was tested in both simulation and real world environments with different number of robots and different scenarios. The search time, exploration time, complexity of the environment and number of double-visited map nodes were investigated in the tests. The experimental results validate the functionality of the method in different configurations. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach to a time and energy efficient online complete coverage solution for a mobile robot. While most conventional approaches strive to reduce path overlaps, this work focuses on smoothing the coverage path to reduce accelerations and yet to increase the average velocity for faster coverage. The proposed algorithm adopts a high-resolution grid map representation to reduce directional constraints on path generation. Here, the free space is covered by three independent behaviors: spiral path tracking, wall following control, and virtual wall path tracking. Regarding the covered region as a virtual wall, all the three behaviors adopt a common strategy of following the (physical or virtual) wall or obstacle boundaries for close coverage. Wall following is executed by a sensor-based reactive path planning control process, whereas the spiral (filling) path and virtual wall path are first modeled by their relevant parametric curves and then tracked via dynamic feedback linearization. For complete coverage, these independent behaviors are linked through a new path linking strategy, called a coarse-to-fine constrained inverse distance transform (CFCIDT). CFCIDT reduces the computational cost compared to the conventional constrained inverse distance transform (CIDT), which applies a region growing starting from the current robot position to find the nearest unexplored cell as well as the shortest path to it while constraining the search space. As for experimental validation, performance of the proposed algorithm is compared to those of conventional coverage techniques to demonstrate its completeness of coverage, energy and time efficiency, and robustness to the environment shape or the initial robot pose. [Copyright &y& Elsevier]

Abstract: A novel framework for the control of the collective movement of mobile robots is presented and analyzed in this article. It allows a group of robots to move as a unique entity performing the following functions: obstacle avoidance at group level, speed control and modification of the inter-robot distance. Its flocking controller is distributed among the robots, allowing them to move in the desired common direction and maintain a desired inter-robot distance. The framework is made up of different modules that modify the behavior of the group thus allowing different functions. They are based on consensus algorithms that allow the robots to agree on different parameters, taking into account which robot has more relevant information. New modules can be easily designed and incorporated into the framework in order to augment its capabilities. It can be easily implemented on any mobile robot capable of measuring the relative positions of neighboring robots and communicating with them. It has been successfully tested using 8 real robots and in simulation with up to 40 robots, demonstrating experimentally its scalability with an increasing number of robots. [Copyright &y& Elsevier]

Abstract: The global localization methods deal with the estimation of the pose of a mobile robot assuming no prior state information about the pose and a complete a priori knowledge of the environment where the mobile robot is going to be localized. Most existing algorithms are based on the minimization of an L2-norm loss function. In spite of the extended use of the L2-norm, the use of the L1-norm offers some alternative advantages. The present work compares the L1-norm and the L2-norm with the same basic optimization mechanism to determine the advantages of each norm when applied to the global localization problem. The algorithm has been tested subject to different noise levels to demonstrate the accuracy, effectiveness, robustness, and computational efficiency of both L1-norm and L2-norm approaches. [Copyright &y& Elsevier]

Abstract: In the first part of the present paper, a novel communication scheme of an autonomous robot team via Bluetooth radio is investigated. In the presented solution, an autonomous unit is equipped with two independent Bluetooth radios and so a relatively fast communication is possible in the team in a static (i.e. no ad hoc) networking topology. The performance of such a network was tested by implementing a linear graph topology by NXT robots. It was found that the reliability and the speed of such a communication scheme are satisfactory and give rise to applications in a robot team control task. In the second part of the paper, an area exploration method is presented based on the static linear communication network above. The method was tested by computer simulations for various obstacle configurations and densities. It was found that the proposed method performs better than the chosen reference methods in the case of zero or low obstacle density and when high (75% or 100%) exploration ratio is required. With a simple proof, we have shown that the proposed (fixed chain-like team) exploration method is optimal in the obstacle-free case under the constraint of the connectivity with the base station. [Copyright &y& Elsevier]

Abstract: This paper addresses a problem of sweep coverage by deploying a network of autonomous mobile robots. We propose a decentralized control algorithm for the robots to accomplish the sweep coverage. The sweep coverage is achieved by coordinating the robots to move along a given path that is unknown to the vehicles a priori. The motion coordination algorithm is developed based on simple consensus algorithms. The effectiveness of the algorithm is demonstrated via numerical simulations. The proposed algorithm would have applications to military and civilian operations. [Copyright &y& Elsevier]

Abstract: We consider the problem of efficiently finding an object with a mobile robot in an initially unknown, structured environment. The overall goal is to allow the robot to improve upon a standard exploration technique by utilizing background knowledge from previously seen, similar environments. We present two conceptually different approaches. Whereas the first method, which is the focus of this article, is a reactive search technique that decides where to search next only based on local information about the objects in the robot’s vicinity, the second algorithm is a more global and inference-based approach that explicitly reasons about the location of the target object given all observations made so far. While the model underlying the first approach can be learned from data of optimal search paths, we learn the model of the second method from object arrangements of example environments. Our application scenario is the search for a product in a supermarket. We present simulation and real-world experiments in which we compare our strategies to alternative methods and also to the performance of humans. [Copyright &y& Elsevier]

Abstract: Inspired by the Witkowski’s algorithm, we introduce a novel path planning and replanning algorithm — the two-way D∗ (TWD∗) algorithm — based on a two-dimensional occupancy grid map of the environment. Unlike the Witkowski’s algorithm, which finds optimal paths only in binary occupancy grid maps, the TWD∗ algorithm can find optimal paths in weighted occupancy grid maps. The optimal path found by the TWD∗ algorithm is the shortest possible path for a given occupancy grid map of the environment. This path is more natural than the path found by the standard D∗ algorithm as it consists of straight line segments with continuous headings. The TWD∗ algorithm is tested and compared to the D∗ and Witkowski’s algorithms by extensive simulations and experimentally on a Pioneer 3DX mobile robot equipped with a laser range finder. [Copyright &y& Elsevier]

Abstract: Task demonstration is an effective technique for developing robot motion control policies. As tasks become more complex, however, demonstration can become more difficult. In this work, we introduce an algorithm that uses corrective human feedback to build a policy able to perform a novel task, by combining simpler policies learned from demonstration. While some demonstration-based learning approaches do adapt policies with execution experience, few provide corrections within low-level motion control domains or to enable the linking of multiple of demonstrated policies. Here we introduce Feedback for Policy Scaffolding (FPS) as an algorithm that first evaluates and corrects the execution of motion primitive policies learned from demonstration. The algorithm next corrects and enables the execution of a more complex task constructed from these primitives. Key advantages of building a policy from demonstrated primitives is the potential for primitive policy reuse within multiple complex policies and the faster development of these policies, in addition to the development of complex policies for which full demonstration is difficult. Policy reuse under our algorithm is assisted by human teacher feedback, which also contributes to the improvement of policy performance. Within a simulated robot motion control domain we validate that, using FPS, a policy for a novel task is successfully built from motion primitives learned from demonstration. We show feedback to both aid and enable policy development, improving policy performance in success, speed and efficiency. [Copyright &y& Elsevier]

Abstract: The strategy for the control of vehicle platooning is proposed and tested on different mobile robot platforms. The decentralized platooning is considered, i.e. a virtual train of vehicles where each vehicle is autonomous and decides on its motion based on its own perceptions. The following vehicle only has information about its distance and azimuth to the leading vehicle. Its position is determined using odometry. The reference position and the orientation of the following vehicle are determined by the estimated path of the leading vehicle in a parametric polynomial form. The parameters of the polynomials are determined using the least-squares method. This parametric reference path is also used to determine the feed-forward part and to suppress tracking errors by a feed-back part of the applied globally stable nonlinear control law. The results of the experiment and simulations demonstrate the applicability of the proposed algorithm for vehicle platoons. [ABSTRACT FROM AUTHOR]

Abstract: In this paper design improvements and control algorithms are presented for a 2-DOF (Degree-Of-Freedom) hybrid leg-wheel walking machine. A prototype of a low-cost robot, which is capable of straight walking and steering with only two actuators, has been designed and built at LARM: Laboratory of Robotics and Mechatronics in Cassino. A control system has been developed in order to control the robot’s operation and to improve the prototype’s behavior. The designed control system and simulation results have been reported to show the operation of the prototype. [ABSTRACT FROM AUTHOR]

Abstract: Creating collision-free trajectories for mobile robots, known as the path planning problem, is considered to be one of the basic problems in robotics. In case of multiple robotic systems, the complexity of such systems increases proportionally with the number of robots, due to the fact that all robots must act as one unit to complete one composite task, such as retaining a specific formation. The proposed path planner employs a combination of Cellular Automata (CA) and Ant Colony Optimization (ACO) techniques in order to create collision-free trajectories for every robot of a team while their formation is kept immutable. The method reacts with obstacle distribution changes and therefore can be used in dynamical or unknown environments, without the need of a priori knowledge of the space. The team is divided into subgroups and all the desired pathways are created with the combined use of a CA path planner and an ACO algorithm. In case of lack of pheromones, paths are created using the CA path planner. Compared to other methods, the proposed method can create accurate collision-free paths in real time with low complexity while the implemented system is completely autonomous. A simulation environment was created to test the effectiveness of the applied CA rules and ACO principles. Moreover, the proposed method was implemented in a system using a real world simulation environment, called Webots. The CA and ACO combined algorithm was applied to a team of multiple simulated robots without the interference of a central control. Simulation and experimental results indicate that accurate collision free paths could be created with low complexity, confirming the robustness of the method. [ABSTRACT FROM AUTHOR]

Abstract: One of the main problems of robots is the lack of adaptability and the need for adjustment every time the robot changes its working place. To solve this, we propose a learning approach for mobile robots using a reinforcement-based strategy and a dynamic sensor-state mapping. This strategy, practically parameterless, minimises the adjustments needed when the robot operates in a different environment or performs a different task. Our system will simultaneously learn the state space and the action to execute on each state. The learning algorithm will attempt to maximise the time before a robot failure in order to obtain a control policy suited to the desired behaviour, thus providing a more interpretable learning process. The state representation will be created dynamically, starting with an empty state space and adding new states as the robot finds new situations that has not seen before. A dynamic creation of the state representation will avoid the classic, error-prone and cyclic process of designing and testing an ad hoc representation. We performed an exhaustive study of our approach, comparing it with other classic strategies. Unexpectedly, learning both perception and action does not increase the learning time. [ABSTRACT FROM AUTHOR]

Abstract: This study proposes a novel method for target tracking based on the combination of object matching and background anti-matching which take account of both the global property of covariance matching and local property of mean shift tracking synthetically. In the background anti-matching phrase, a certain number of background regions are extracted based on the feature of color orientation codes via an entropy filter, and the covariance matrix is adapted to match these regions to get the global motion of the background; further, the object matching is carried out by a mean-shift tracking algorithm. The proposed method is evaluated in various datasets in comparison with their counterpart algorithms; experimental results sufficiently demonstrate the effectiveness of the method proposed in this study. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a System on Chip (SoC) for the path following task of autonomous non-holonomic mobile robots. The SoC consists of a parameterized Digital Fuzzy Logic Controller (DFLC) core and a flow control algorithm that runs under the Xilinx Microblaze soft processor core. The fuzzy controller supports a fuzzy path tracking algorithm introduced by the authors. The FPGA board hosting the SoC was attached to an actual differential-drive Pioneer 3-DX8 robot, which was used in field experiments in order to assess the overall performance of the tracking scheme. Moreover, quantization problems and limitations imposed by the system configuration are also discussed. [Copyright &y& Elsevier]

Abstract: Recent research on mobile robots has focused on locomotion in various environments. In this paper, a gait-generation algorithm for a mobile robot that can travel from the ground to a wall and climb vertical surfaces is proposed. The algorithm was inspired by a gecko lizard. Our gait planning was based on inverse kinematics using the Jacobian of the whole body, where the redundancy was solved by defining an object function for the gecko posture to avoid collisions with the surface. The optimal scalar factor for these two objects was obtained by defining a superior object function to minimize the angular acceleration of joints. The algorithm was verified through simulation of the gecko model travelling on given task paths and avoiding abnormal joint movements and collisions. [Copyright &y& Elsevier]

Abstract: Autonomous robots are leaving the laboratories to master new outdoor applications, and walking robots in particular have already shown their potential advantages in these environments, especially on a natural terrain. Gait generation is the key to success in the negotiation of natural terrain with legged robots; however, most of the algorithms devised for hexapods have been tested under laboratory conditions. This paper presents the development of crab and turning gaits for hexapod robots on a natural terrain characterized by containing uneven ground and forbidden zones. The gaits we have developed rely on two empirical rules that derive three control modules that have been tested both under simulation and by experiment. The geometrical model of the SILO-6 walking robot has been used for simulation purposes, while the real SILO-6 walking robot has been used in the experiments. This robot was built as a mobile platform for a sensory system to detect and locate antipersonnel landmines in humanitarian demining missions. [Copyright &y& Elsevier]

Abstract: Multi-robot sensor-based coverage path planning requires every point given in the workspace has to be covered at least by a sensor of a robot in the robot team. In this study, a novel algorithm was proposed for the sensor-based coverage of narrow environments by considering energy capacities of the robots. For this purpose, the environment was modeled by a Generalized Voronoi diagram-based graph to guarantee complete sensor-based coverage. Then, depending on the required arc set, a complete coverage route was created by using the Chinese Postman Problem or the Rural Postman Problem, and this route was partitioned among robots by considering energy capacities. Route partitioning was realized by modifying the Ulusoy partitioning algorithm which has polynomial complexity. This modification handles two different energy consumptions of mobile robots during sensor-based coverage, which was not considered before. The developed algorithm was coded in C++ and implemented on P3-DX mobile robots both in laboratory and in MobileSim simulation environments. It was shown that the convenient routes for energy constrained multi-robots could be generated by using the proposed algorithm in less than 1 s. [Copyright &y& Elsevier]