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In this paper, a motion planning algorithm for floating planar under-actuated hyper-redundant snake robots is proposed. The presented algorithm generates locally optimal shape trajectories, i.e., continuous trajectories in the base space of the robot. Such shape trajectories produce a desired rotation of the snake robot, i.e., change in the uncontrolled orientation fiber variable. The proposed method formulates the motion planning problem as an optimization problem where the objective function could be defined to minimize various metrics, such as energy-based cost functions. Additionally, the proposed motion planning algorithm uses a heuristic to generate shape trajectories that avoid self-intersections and obstacle collision. Hence, the motion planning method generates shape trajectories that locally minimize user-defined cost functions and eliminate self-intersections or obstacle collision. The proposed gait generation method is validated using numerical simulations of five-link and seven-link snake robots. • Motion planning algorithm for reorienting snake robots in force-free environments. • The proposed algorithm is applicable to snake robots having any number of links. • The proposed algorithm generates optimal shape trajectories. • Generated shape trajectories are optimized based on user-defined cost functions. • The algorithm facilitates obstacle avoidance and inter-link collision avoidance. • Introduction of a novel heuristic for inter-link collision avoidance. [ABSTRACT FROM AUTHOR]

The article presents a novel idea to construct a smooth navigation function for a wheeled robot based on grid-based search, that enables replanning in dynamic environments. Since the dynamic constraints of the robot are also considered, the navigation function is combined with the model predictive control (MPC) to guide the robot safely to the defined goal location. The main novelty of this work is the definition of this navigation function and its MPC application with guaranteed closed-loop convergence in finite time for a non-holonomic robot with speed and acceleration constraints. The navigation function consists of an interpolated potential function derived from the grid-based search and a term that guides the orientation of the robot on continuous gradients. The navigation function guarantees convergent trajectories to the desired goal, results in smooth motion between obstacles, has no local minima, and is computationally efficient. The proposed navigation is also suitable in dynamic environments, as confirmed by experiments with a Husky mobile robot. • Navigation function created from the grid-based search enables real-time replanning. • Continuous navigation function is obtained by interpolating discrete grid-based costs. • MPC-based navigation takes velocity and acceleration constraints into account. • MPC with the control sequence generation scheme allows fast on-line computation. • The navigation approach guarantees closed-loop convergence for unicycle-type robots. [ABSTRACT FROM AUTHOR]

The ability to avoid collisions with moving robots is critical in many applications. Moreover, if the robots have limited battery life, the goal is not only to avoid collisions but also to design efficient trajectories in terms of energy consumption and total mission time. This paper proposes a novel strategy for assigning turn angles for collision-free path planning in scenarios where a small team of robots cooperate in a certain mission. The algorithm allows each robot to reach a predetermined destination safely. It establishes consecutive, short time intervals, and at each interval, possible conflicts are solved centrally in an optimal manner. This is done by keeping constant speeds but generating a discrete set of possible directions for each robot, and solving efficiently the turn-angle allocation for a collision-free path that minimizes the path deviation from the shortest one. Due to the discretization, the final paths are not optimal, but the system can react to possible failures during execution, as conflicts are resolved at each time interval. Computational results and Software-In-The-Loop simulations are presented in order to evaluate the proposed algorithm. A comparison with a state-of-the-art approach shows that our algorithm is more energy-efficient and achieves lower mission completion time. [ABSTRACT FROM AUTHOR]

Wheel-legged robots are a type of mobile robot that combines the advantages of wheeled robots, such as fast and stable movement and high efficiency, with the adaptability of legged robots to complex and unstructured environments. Therefore, wheel-legged robots have great potential for application in fields such as deep space exploration, disaster relief, and wilderness exploration. This paper categorizes and summarizes the structural forms and driving modes of wheel-legged robots, dividing them into three categories: wheel-legged hybrid robots, wheel-legged separation robots, and wheel-legged transformation robots based on their structural characteristics. Finally, this paper summarizes the structure and driving aspects of wheel-legged robots and provides an outlook on their development in these two areas. The research results presented in this paper help researchers understand the development process of wheel-legged robots and serve as a valuable reference for future research. [ABSTRACT FROM AUTHOR]

In the real world, multi-robot systems need to deal with on-the-fly (runtime) arrivals of new sets of tasks. This entails repeated adjustments of their current task allocations to include the newer ones while also ensuring that the overall performance does not degrade. This paper proposes a decentralized and distributed dynamic task allocation algorithm to handle this issue in a multi-robot scenario. The proposed work provides a conflict-free allocation of a set of tasks constituting a job to robots and minimizes the total execution time. These jobs can comprise multiple independent and/or dependent tasks or a combination thereof, which are injected on-the-fly into a network of robots. The dependent tasks of a job are related by precedence constraints that specify the ordering or dependencies between pairs of tasks. The work also describes a decentralized adaptive energy threshold mechanism for determining whether or not a robot needs to visit a battery stockpile after the execution of a task. Conflicting task selections among the robots in this decentralized set-up are resolved using mobile agents during runtime. Apart from allocating tasks to the robots, these mobile agents exploit the benefits of centralized and decentralized systems and provide an advantage over auction-based task allocation algorithms. The proposed algorithm takes into consideration the energy requirements, both during the task allocation process and actual execution. The proposed algorithm also caters to strategies to deal with delays caused by obstacles and congestion during the actual execution of the tasks. Experiments conducted using Webots , an open-source robot simulator, and Tartarus , a multi-agent platform, authenticate the efficacy of the proposed algorithm compared to other prominent task allocation algorithms in terms of minimization of average waiting time , total task allocation time , total job allocation time , and total execution time of an experiment. • Decentralized, distributed, and dynamic precedence-constrained MRTA algorithm • Mobile agents are used to resolve task conflicts and to allocate tasks among robots. • An adaptive energy threshold for recharging the robots is used. • Energy consumption is considered both during task allocation and actual execution. • Strategies to overcome delays due to obstacles during actual execution are proposed. [ABSTRACT FROM AUTHOR]

We address a motion planning and control problem for mobile robots to satisfy rich, time-varying tasks expressed as Signal Temporal Logic (STL) specifications. The specifications may include tasks with nested temporal operators or time-conflicting requirements (e.g., achieving periodic tasks or tasks defined within the same time interval). Moreover, the tasks can be defined in locations changing with time (i.e., dynamic targets), and their future motions are not known a priori. This unpredictability requires an online control approach which motivates us to investigate the use of control barrier functions (CBFs). The proposed CBFs take into account the actuation limits of the robots and a feasible sequence of STL tasks. They define time-varying feasible sets of states the system must always stay inside. We show the feasible sequence generation process that even includes the decomposition of periodic tasks and alternative scenarios due to disjunction operators. The sequence is used to define CBFs, ensuring STL satisfaction. We also show some theoretical results on the correctness of the proposed method. We illustrate the benefits of the proposed method and analyze its performance via simulations and experiments with aerial robots. • A robot must satisfy a signal temporal logic (STL) specification by visiting time-varying regions. • The future trajectories of time-varying regions are unknown. • The STL specification may include nested temporal operators or time-conflicting requirements. • We propose Sequential CBFs to drive a robot to its dynamic targets based on its STL specification. • The proposed algorithm is supported by theoretical results and tested via experiments. [ABSTRACT FROM AUTHOR]

An underactuated nonholonomic Dubins-vehicle-like robot with a lower-limited turning radius travels with a constant speed in a plane, which hosts unknown complex objects. In its local frame, the robot has sensorial access only to the part of the scene that is within a finite zone of visibility and in direct line of sight. It is required to approach and then circumnavigate the objects, with maintaining a given distance to the currently nearest of them, so that the ideal targeted path is the equidistant curve of the set of the objects. The focus is on the case where this curve cannot be perfectly traced due to excessive contortions and singularities. So the objective is restated as that of automatically finding, approaching and repeatedly tracing an approximation of the equidistant curve that is the best among those trackable by the robot with respecting safety concerns. Though pre-computation of this approximation is a hard problem of computational geometry, we show that autonomously seeking and tracking this approximation can be performed on-the-fly at a miserable computational cost via implementation of the proposed navigation law. This law is hybrid and operates in only a few discrete modes; within any mode, it is reactive, i.e., it directly converts the current observation into the current control. Nonlocal convergence of this law is justified by mathematically rigorous results and is confirmed by computer simulations and real-world experiments. • Underactuated nonholonomic Dubins-vehicle-like robot with a lower-limited turning radius. • Circumnavigation of unknown objects, with desirably maintaining a given distance to them. • Equidistant curve that is untrackable due to excessive contortion and singularities. • On-the-fly generation of the optimal trackable approximation at a miserable computational cost. • Computationally cheap (reactive) controller that simultaneously generates the path and ensures its tracking. [ABSTRACT FROM AUTHOR]

We develop an algorithm for the motion and task planning of a system composed of multiple robots and unactuated objects under tasks expressed as Linear Temporal Logic (LTL) constraints. The robots and objects evolve subject to uncertain dynamics in an obstacle-cluttered environment. The key part of the proposed solution is the intelligent construction of a coupled transition system that encodes the motion and tasks of the robots and the objects. We achieve such a construction by designing appropriate adaptive control protocols in the lower level, which guarantee the safe robot navigation/object transportation in the environment while compensating for the dynamic uncertainties. The transition system is efficiently interfaced with the temporal logic specification via a sampling-based algorithm to output a discrete path as a sequence of synchronized actions of the robots; such actions satisfy the robots' as well as the objects' specifications. The robots execute this discrete path by using the derived low level control protocol. Numerical experiments verify the proposed framework. • Robot tasks expressed by temporal logics offer great versatility and efficiency. • Simple items/objects often have to undergo processes and tasks. • New algorithm provides robot actions that satisfy theirs and objects' tasks. • Robot actions consist of robot navigation and cooperative object transportation. • Adaptive control is used to tackle uncertainty in robots' and objects' dynamics. [ABSTRACT FROM AUTHOR]

Robot sequential decision-making in the real world is a challenge because it requires the robots to simultaneously reason about the current world state and dynamics, while planning actions to accomplish complex tasks. On the one hand, declarative languages and reasoning algorithms support representing and reasoning with commonsense knowledge. But these algorithms are not good at planning actions toward maximizing cumulative reward over a long, unspecified horizon. On the other hand, probabilistic planning frameworks, such as Markov decision processes (MDPs) and partially observable MDPs (POMDPs), support planning to achieve long-term goals under uncertainty. But they are ill-equipped to represent or reason about knowledge that is not directly related to actions. In this article, we present an algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers. In this process, robot decision-making problems are decomposed into two interdependent (smaller) subproblems that focus on reasoning to "understand the world" and planning to "achieve the goal" respectively. The developed algorithm has been implemented and evaluated both in simulation and on real robots using everyday service tasks, such as indoor navigation, and dialog management. Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines including handcrafted action policies. • This article formally introduces the Integrated Reasoning and Planning (IRP) problem for robots. An IRP problem has a factored state space that is specified by a set of endogenous and exogenous variables. • This article introduces a sequential decision-making algorithm, called Interleaved Commonsense Reasoning and Probabilistic Planning (iCORPP), for addressing IRP problems. • This article presents systematic evaluations of iCORPP using two tasks of mobile robot navigation, and spoken dialog systems, as demonstrated on a real robot. • This article builds on our previous research that appeared in two conference papers (Zhang and Stone, 2015; Zhang et al., 2017). This article unifies their terminology, problem statements, and algorithms. • Compared to our prior work, this article introduces a new problem statement in Section 4.1, which covers the problems addressed in both conference papers. • To address the IRP problem, we have reformulated the iCORPP algorithm (Section 4.2) into a novel form that includes the three key steps of "logical reasoning," "probabilistic reasoning over world states," and "probabilistic reasoning about world dynamics." [ABSTRACT FROM AUTHOR]

Non-prehensile cooperative object transportation is a challenging model problem for distributed control and organization methods but also has practical applications. Therefore, it is widely studied in distributed robotics research. This paper describes and evaluates a novel transportation scheme for differential-drive mobile robots that is, to the authors' best knowledge, the most versatile scheme of its kind successfully evaluated with real-world hardware. The proposed scheme can conceptually deal with any number of robots and arbitrary polygonal objects, including non-convex ones, without having to retune, retrain, or reconfigure any of the control parameters between different scenarios. This is achieved by splitting the task into a formation control and a formation finding task, both of which are tackled with model-based approaches using distributed optimization. Formation control and formation finding are complicated by the robots' non-holonomic kinematic constraints. Therefore, a tailored distributed model predictive controller is used for formation control. Finding formations relies on a multibody-dynamics representation of the robots-object system to properly account for contact and non-holonomic constraints. Due to these measures, the transportation scheme achieves a very satisfactory performance and dexterity in real-world hardware experiments utilizing network communication and distributed computation. • First time such a versatile scheme is shown to work with non-holonomic robot hardware. • Convex and non-convex object shapes can be considered. • Fully distributed decision-making, no scenario-specific parameter tuning. • Non-holonomic constraints are naturally considered using multibody dynamics. • Distributed predictive control and distributed optimization serve as key methods. [ABSTRACT FROM AUTHOR]

Hedge algebra is gaining increasing attention due to its high efficiency and fast calculation speed compared to fuzzy logic. This paper proposes the first algebra-based formation controller with a leader–follower approach to maintain the desired formation of multiple robots. In each time step, the controller computes the desired positions of the followers and strives to minimize the distance between them and their desired positions. We evaluate the performance of this controller on our robots using ROS in a virtual environment with Gazebo. Our work proves hedge algebra's significant potential in the mobile robots' domain, specifically in formation control. HAFC allows not only the precise formation of a robot swarm into any desired formation, with minimal deviation but also adaptive transition to the different formations. Our results show that HAFC can effectively maintain the desired formation of multiple robots, even in certain extraordinary situations. Furthermore, HAFC exhibits higher efficiency than the fuzzy logic-based controller, as evidenced by its faster adaptation rate and shorter computation time. The proposed approach has the potential to be applied in various real-world scenarios, such as surveillance, transportation, and exploration, where the robot has to move in formation and change formation accordingly with the terrain. • This work is the first study in formation control of mobile robots and adaptive formation translation based on the Hedge-algebras theory. • HAFC exhibits higher efficiency compared to the fuzzy logic-based controller, as evidenced by its faster adaptation rate and shorter computation time. • The simulation is implemented on ROS and obtain a high accuracy and miniature errors. A structure is proposed so as to conduct real formation experiments. • The proposed approach has the potential to be applied in various real-world scenarios, such as surveillance, transportation, and exploration, where the robots have to move in formation and change formation accordingly with the terrain. [ABSTRACT FROM AUTHOR]

One of the main issues in robotics systems is planning and tracking a safe path in diverse environments. This paper addresses an optimal methodology for generating the desired path and thereafter forces the mobile robot to follow the designed reference path. The proposed technique has the potential to tackle the inherent challenges and intricacies of the environment, enabling the robot to navigate both static and dynamic workspaces. Several simulations are exploited to verify the captured theoretical results. Three cases were examined in a static environment, achieving average performance metrics for reference signals in the X- and Y-directions, and heading tracking of 99.62%, 99.64%, and 95.08%, respectively. For the dynamic environment, two cases were studied, with average performance in following the Y-direction and heading angle recorded as 97.58% and 86.79%, respectively. Simulation results show that the controller calculated the optimal signal with an average computational time of 7 ms per iteration for static environments and 21.3 ms per iteration for dynamic environments. • Low computational time : the computational time of path planner and path following is studied, and for the later, CasADi toolkit is used to minimize the computational time. • Path planner algorithm is complete : guarantees to find an existing path. • Systematic approach : the whole algorithm is explained in the paper facilitates further studies and enables a better understanding of the approach. • Applicable to a wide range of robot types : the proposed algorithm is suitable for any spatial search, and easily extentable to higher dimensions. [ABSTRACT FROM AUTHOR]

We present a novel method for navigation of mobile robots in challenging dynamic environments. The method, which is based on Nonlinear Model Predictive Control (NMPC), hinges upon a specially devised constraint for dynamics-aware collision avoidance. In particular, the constraint builds on the notion of avoidable collision state , taking into account the robot actuation capabilities in addition to the robot–obstacle relative distance and velocity. The proposed approach is applied to both ground and flying robots and tested in a variety of static and dynamic environments. Comparative simulations with an NMPC using a purely distance-based collision avoidance constraint confirm the superiority of the dynamics-aware version, especially for high-speed navigation among moving obstacles. Moreover, the results indicate that the method can work with relatively short prediction horizons and is therefore amenable to real-time implementation. • Robot navigation among obstacles at high-speed using Model Predictive Control. • Dynamics-aware collision avoidance enhances robot safety. • Dynamics-aware collision avoidance outperforms its distance-based counterpart. • Integration of velocity fields aids the robot in circumventing obstacles. [ABSTRACT FROM AUTHOR]

Swarm robotics involves the study of the behavior of a set of robots in carrying out collective tasks, such as alignment, navigation and formation. During cooperative tasks execution, the communication among robots can contribute to successfully performing tasks through an efficient messages exchange. This paper proposes a communication strategy for swarm robots, the so-called Double-Wave Swarm, aiming at alignment and navigation tasks. The Double-Wave Swarm is an improvement of a prior Wave Swarm communication approach that uses the concept of wave propagation for message exchange between neighbors. Double-Wave Swarm provides better communication network connectivity when compared to the former approach. Experiments with the robot simulator CoppeliaSim (V-REP) validate the proposed communication and highlight its efficiency and robustness while running alignment and navigation tasks. Also, the Double-Wave Swarm proved to be superior during swarm formation navigation into environments with obstacles in different scenarios if compared with Wave Swarm. • DWS is an improvement on an earlier WS communication approach. • DWS increases the connectivity between robots when compared to WS. • Sharing information through DWS helps in locating and orienting swarm robots. • DWS is a robust and effective approach to in formation navigation of multi-robots. [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]

In recent years, the navigation capabilities of mobile robots in off-road environments have increased significantly, opening up new potential applications in a variety of settings. By accurately identifying different types of terrain in unstructured environments, safe automated navigation can be supported. However, to enable safe path planning and execution, the traversability costs of the terrain types need to be accurately estimated. Such estimations are often performed manually by experts who possess information about the environment and are familiar with the capabilities of the robotic system or using simplified experiments. In this paper, we present an automated pipeline for generating traversability costs that use recorded locomotion data from a realistic experiment and descriptive information on the terrain obtained from earth observation data. The main contribution is that the cost estimation for different terrain types is based on locomotion data obtained in realistic standardized experiments. Moreover, by repeating the experiments with different robot systems we are easily able to reflect the actual capabilities of the systems. Experiments were conducted in an alpine off-road environment to record locomotion data of four different robot systems and to investigate the performance and validity of the proposed pipeline. The recorded locomotion data for the different robots are publicly available at https://robonav.ist.tugraz.at/data/ • Pipeline to estimate traversability costs for robots using recorded locomotion data. • Interpolation of costs for uncovered terrain classes using order relations. • Realistic off-road locomotion datasets for four heterogeneous robots. • Provision of guidelines for robot-specific recording of locomotion data. [ABSTRACT FROM AUTHOR]

"Smart cities" is a popular concept in modern urban development that demands innovative solutions to enhance various aspects of our lives. This paper introduces a novel application that aims to improve public road maintenance by utilizing a flying robot to repaint partially erased sections of the sidewalks' edges that are typically marked with black and white colors. The first contribution of this paper is the development of a fuzzy-logic-based stabilization system for an octocopter, which can serve as a liquids transporter and be equipped with a robot arm. The second contribution is the design of an embedded energy harvester for the flying robot, which optimizes the management of available power sources. Additionally, this project includes a complementary heuristic study that clarifies fundamental concepts related to a computer vision-based decision-making system. [ABSTRACT FROM AUTHOR]

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

This study investigates operability and acceptability issues in the teleoperation of robots. Prior studies have proposed efficient approaches to increase human perceptual ability and robot autonomy but with reduced operability and acceptance. We propose a novel teleoperation method that overcomes the weaknesses of existing approaches while inheriting their strengths. The key feature of our method is switching the teleoperated robot world from real to virtual. The user study results showed that the proposed method offered an improved user experience compared to the conventional methods, while task efficiency was equivalent in all methods. The contributions of this paper include the proposal of the teleoperation method by seamless switching between real and virtual space, the proposal of an image transformation method and visual effect to achieve seamless switching, and verification of the practicality of the proposed system through experiments on actual mobile robots. • Illusory control improved the operability and acceptability of the robot system. • An image transformation using neural networks for seamless transitions. • A visual effect that amplifies the sense of speed for seamless transitions. [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]

The robot behavior strategy is considered as a crucial part in the human-following task to help the robot maintain an appropriate distance and orientation to the selected target person (STP) with a smooth and safe manner. As usual, the robot is uniquely considered to follow the STP in a specific class of environments, such as unknown environments (non-mapped environments) or known environments (mapped environments). However, in real-life applications, the robot is sometimes requested to follow the STP in various types of environments, both in known and unknown ones. This observation raises the need to propose an alternative method to challenge the mentioned issue, as well as to break the current limit of the human-following function. In this paper, a new approach for the human-following strategy is proposed in which the mobile robot is enabled to follow the STP in mixed environments (non-mapped and mapped). In non-mapped environments, only the STP and the obstacle information with respect to the robot local coordinates are considered, whose purpose is to make the robot work without any prior understandings about its working environment. However, after the robot entered mapped environments, its prior knowledge of the working environment is leveraged to fulfill some additional requirements during the cooperation, such as the mobile robot in factories is not allowed to enter some specific areas even when the STP is executing technical tasks inside. Additionally, in this paper, a human-like inference mechanism is also introduced for the human-following strategy by using an extended hedge algebras. The proposed method is experimentally verified both in factories and laboratories. Demo Video Link : https://www.youtube.com/watch?v=YGrWU6ldKuw Since real videos in the factory are not allowed to publish, only visualization (in Rviz) is presented for demos in such kinds of environments. The visualization is synchronous with the real executions of the human–robot interactions. The robot used in the factory is an autonomous mobile robot (dimension 0.5 (m) ×1.0 (m), weight 120 (kg), carrying a tool cabinet around 300(kg))). The mobile robot is following the worker to support them during the technical processes in the car production line. In the video, the robot is represented by a green rectangular, and the STP is represented by a cylinder (with a sphere on its head) The events in the demo video are described more clearly in Appendix A. • First contribution: a new concept for human-following robots in mixed environments (mapped and non-mapped). • Second contribution: a hedge-algebras-based approach for the robot behavior strategy to work both in indoor and outdoor, mapped and non-mapped environments, and in different robot platforms. • Third contribution: an end-to-end human-following system for mobile robots by integrating all required modules, from trigger methods, the human detection and tracking to robot behavior policies and the inference mechanism. • Experiments are conducted both in factories and laboratories. And obtained results have shown that the proposed method works well in various environments, both indoor and outdoor and ondifferent robot platforms. [ABSTRACT FROM AUTHOR]

Autonomous mobile robots are a special class of robotic systems that can move a payload from one location to the other or perform a specific task. They allow efficient, precise, and streamlined workflow that makes human work less arduous. The market and research work related to these robots is increasing in anticipation of industry 5.0, where humans and machines are expected to co-exist and co-work. The future mobile robots are desired to have clean and cost-effective energy sources to have longer operation times and compliance with environmental requirements to allow application in diverse fields. The research on mechanical design, perception, navigation and control has carved out many commercially viable solutions for mobile robots. However, their widespread application is still limited due to the lack of efficient power systems for use in diverse and largely unknown/uncontrolled environments. The current power solutions incur high initial costs and require recharging or refuelling, which makes them unsuitable for unattended long-haul worktimes and cost-effective applications. These drawbacks are major hurdles in the wider applicability of terrain-based mobile robots to new domains and daily life scenarios, which are possible with the existing mechanical, perception, and control technologies. Keeping in view the need for advancement in this field and to gain a better understanding of the current state of the art and future directions, this work summarizes and reviews the energy solutions presented in the literature and used in notable commercially available terrain-based mobile robots. The provided solutions are categorized and discussed while the prospects and research gaps are also highlighted. A comparison of discussed power techniques is also provided, which can serve as a guideline for selecting a robot's energy source according to the desired requirements. [ABSTRACT FROM AUTHOR]

The haptic terrain classification is an essential component of a mobile walking robot control system, ensuring proper gait adaptation to the changing environmental conditions. In practice, such components are a part of an autonomous system and thus have to be lightweight, provide fast inference time, and guarantee robustness to minor changes in recorded sensory data. We propose transformer-based HAPTR and HAPTR2 terrain classification methods that use force and torque measurements from feet to meet these requirements. For reliable comparison of the proposed solutions, we adapt two classical machine learning algorithms (DTW-KNN and ROCKET), one temporal convolution network (TCN), and use the state-of-the-art CNN-RNN. The experiments are performed on publicly available PUTany and QCAT datasets. We show that the proposed HAPTR and HAPTR2 methods achieve accuracy on par or better than state-of-the-art approaches with a lower number of parameters, faster inference time, and improved robustness to input signal distortions. These features make HAPTR and HAPTR2 excel in terrain recognition tasks when considering real-world requirements. • Fast and lightweight transformer-based approach for the terrain classification. • Modality Attention Layer improves the system's robustness in real-world scenarios. • Autonomy requirements inspired our evaluation of terrain classification algorithms. • The benchmark on two open source datasets creates a baseline for further research. [ABSTRACT FROM AUTHOR]

In this paper, we present an active visual SLAM approach for omnidirectional robots. The goal is to generate control commands that allow such a robot to simultaneously localize itself and map an unknown environment while maximizing the amount of information gained and consuming as low energy as possible. Leveraging the robot's independent translation and rotation control, we introduce a multi-layered approach for active V-SLAM. The top layer decides on informative goal locations and generates highly informative paths to them. The second and third layers actively re-plan and execute the path, exploiting the continuously updated map and local features information. Moreover, we introduce two utility formulations to account for the presence of obstacles in the field of view and the robot's location. Through rigorous simulations, real robot experiments, and comparisons with state-of-the-art methods, we demonstrate that our approach achieves similar coverage results with lesser overall map entropy. This is obtained while keeping the traversed distance up to 39% shorter than the other methods and without increasing the wheels' total rotation amount. Code and implementation details are provided as open-source, and all the generated data is available online for consultation. • Hierarchical Active SLAM framework. • Continuous active control of a robot for optimized heading directions. • Higher exploration amount per traveled meter. • Formulation of new utilities using the presence of obstacles in the field of view. [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]

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]

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]

Abstract: This article presents a cooperative approach for tracking a moving spherical object in 3D space by a team of mobile robots equipped with sensors, in a highly dynamic environment. The tracker’s core is a particle filter, modified to handle, within a single unified framework, the problem of complete or partial occlusion for some of the involved mobile sensors, as well as inconsistent estimates in the global frame among sensors, due to observation errors and/or self-localization uncertainty. We present results supporting our approach by applying it to a team of real soccer robots tracking a soccer ball, including comparison with ground truth. [Copyright &y& Elsevier]

This article proposes a dynamic model based on distance and orientation between an omnidirectional mobile robot and a quadrotor Unmanned Aerial Vehicle (UAV), both under the leader–follower scheme. It is assumed that the omnidirectional robot is already controlled in order to follow a desired trajectory. On the other hand, a backstepping with a Continuous Sliding-Mode Control (C-SMC) are designed for the quadrotor with the objective of keeping a distance and formation angle with respect to the omnidirectional robot. Numerical simulations and real-time experiments show the performance of the proposed control strategy. • A multi-agent model, based on distance and orientation between robots, is developed. • The control system scheme is based on a Continuous Sliding-Modes and Backstepping. • The UAV keeps a distance and attitude with respect to the omnidirectional robot. • The velocities of the omnidirectional robot act as a perturbation to the UAV. [ABSTRACT FROM AUTHOR]

Abstract: With increasing demand on reliable robotic platforms that can alleviate the burden of daily painstaking tasks, researchers have focused their effort towards developing robotic platforms that possess a high level of autonomy and versatility in function. These robots, capable of operating either individually or in a group, also possess the structural modular morphology that enables them to adapt to the unstructured nature of a real environment. Over the past two decades, significant work has been published in this field, particularly in the aspects of autonomy, mobility and docking. This paper reviews the primary methods in the literature related to the fields of modular and reconfigurable mobile robotics. By bringing together aspects of modularity, including docking and autonomy, and synthesizing the most relevant findings, there is optimism that a more complete understanding of this field will serve as a starting ground for innovation and integration of such technology in the urban environment. [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: Mobile robots are becoming more heavily used in environments where human involvement is limited, impossible, or dangerous. These robots perform some of the more laborious human tasks on Earth and throughout the solar system, simultaneously saving resources and offering automation. Higher levels of autonomy are also being sought in these applications, such as distributed exploration and mapping of unknown areas. Smaller, less expensive mobile robots are becoming more prevalent, which introduces unique challenges in terms of limited sensing accuracy and onboard computing resources. This paper presents a novel low-cost, limited-resource approach to autonomous multi-robot mapping and exploration in unstructured environments. Design and implementation details are presented, along with results from two planetary style environments. Results demonstrate that low-cost ($ 1250) mobile robots capable of simultaneous localization and mapping can be successfully constructed. The multi-robot system presented in this paper participated in the 2008 International Conference on Robotics and Automation (ICRA) Space Robotics Challenge, receiving two awards for successfully completing the ’Onto the Surface’ and ’Map the Environment’ events in a simulated planetary environment. This work demonstrates not only that such systems are possible, but also that this direction of research is important and needs attention. [Copyright &y& Elsevier]

Abstract: One important design decision for the development of autonomously navigating mobile robots is the choice of the representation of the environment. This includes the question of which type of features should be used, or whether a dense representation such as occupancy grid maps is more appropriate. In this paper, we present an approach which performs SLAM using multiple representations of the environment simultaneously. It uses reinforcement to learn when to switch to an alternative representation method depending on the current observation. This allows the robot to update its pose and map estimate based on the representation that models the surrounding of the robot in the best way. The approach has been implemented on a real robot and evaluated in scenarios, in which a robot has to navigate in- and outdoors and therefore switches between a landmark-based representation and a dense grid map. In practical experiments, we demonstrate that our approach allows a robot to robustly map environments which cannot be adequately modeled by either of the individual representations. [Copyright &y& Elsevier]

Abstract: This paper presents a novel design of a robust visual tracking control system, which consists of a visual tracking controller and a visual state estimator. This system facilitates human–robot interaction of a unicycle-modeled mobile robot equipped with a tilt camera. Based on a novel dual-Jacobian visual interaction model, a robust visual tracking controller is proposed to track a dynamic moving target. The proposed controller not only possesses some degree of robustness against the system model uncertainties, but also tracks the target without its 3D velocity information. The visual state estimator aims to estimate the optimal system state and target image velocity, which is used by the visual tracking controller. To achieve this, a self-tuning Kalman filter is proposed to estimate interesting parameters and to overcome the temporary occlusion problem. Furthermore, because the proposed method is fully working in the image space, the computational complexity and the sensor/camera modeling errors can be reduced. Experimental results validate the effectiveness of the proposed method, in terms of tracking performance, system convergence, and robustness. [Copyright &y& Elsevier]

Abstract: Recent developments in sensor technology have made it feasible to use mobile robots in several fields, but robots still lack the ability to accurately sense the environment. A major challenge to the widespread deployment of mobile robots is the ability to function autonomously, learning useful models of environmental features, recognizing environmental changes, and adapting the learned models in response to such changes. This article focuses on such learning and adaptation in the context of color segmentation on mobile robots in the presence of illumination changes. The main contribution of this article is a survey of vision algorithms that are potentially applicable to color-based mobile robot vision. We therefore look at algorithms for color segmentation, color learning and illumination invariance on mobile robot platforms, including approaches that tackle just the underlying vision problems. Furthermore, we investigate how the inter-dependencies between these modules and high-level action planning can be exploited to achieve autonomous learning and adaptation. The goal is to determine the suitability of the state-of-the-art vision algorithms for mobile robot domains, and to identify the challenges that still need to be addressed to enable mobile robots to learn and adapt models for color, so as to operate autonomously in natural conditions. [Copyright &y& Elsevier]

Abstract: This work establishes the reference signal conditions for zero tracking error when controlling wheeled mobile robots under the kinematic framework, that is, when the low-level dynamics is neglected. The reference characterization is based on the classical decoupled robot control and the inverse kinematics of fixed, centered orientable, castor and Swedish wheels. Procedures to avoid tracking error when a particular condition is not satisfied are also indicated. Simulations are shown to illustrate the reference conditions for each type of mobile robot and their implications. Finally, an industrial forklift is considered in a real situation to validate the previous results and to highlight the limits of the kinematic framework assumption. [Copyright &y& Elsevier]

Abstract: In this research, we focus our discussion on the discontinuous dynamic behavior transition of a walking robot. For the transition, bifurcation of potential function is utilized. We demonstrate the behavior transition of the walking robot between biped and quadruped locomotion as an adaptation to the environment depending on the gradient of a slope. The behavior transition is carried out by using bifurcation of parameters used for control. We also show the effectiveness of hysteresis in the transition. [Copyright &y& Elsevier]

Abstract: Tight formation-based operations are critical in several emerging applications for robot collectives — ranging from cooperative payload transport to synchronized distributed data-collection. In this paper, we investigate the optimal relative layout for members of a team of Differentially-Driven Wheeled Mobile Robots (DD-WMRs) moving in formation for ultimate deployment in cooperative payload transport tasks. Our particular focus is on modeling such formations, developing the motion plans and determining the “best formation” in a differential-geometric setting. Specifically, a preferred team-fixed frame serves as a virtual leader inducing motion plans for the individual DD-WMRs which form the vertices of a virtual structure. The resulting motion plans for the DD-WMRs as well as overall team-performance depend both on the specified team-frame motions as well as their relative-layout within the formation. Emphasis is placed on developing suitable invariant (yet quantitative) measures of formation quality and a systematic optimization-based selection of the formation-layout. The use of relative formation-parameterization with respect to a team-frame serves to decouple the team-level optimal layout selection process. The optimal location of each DD-WMR can now be found with respect to the team-frame individually and the feasibility of distributed implementation facilitates scaling to larger-sized formations. Analytical and numerical results, from case studies of formation optimization of three DD-WMRs maneuvering along certain desired planar paths, are presented to highlight the salient features and benefits. [Copyright &y& Elsevier]

Abstract: Hybrid maps where local metric submaps are kept in the nodes of a graph-based topological structure are gaining relevance as the focus of robot Simultaneous Localization and Mapping (SLAM) shifts towards spatial scalability and long-term operation. In this paper we examine the applicability of spectral graph partitioning techniques to the automatic generation of metric submaps by establishing groups in the sequence of observations gathered by the robot. One of the main aims of this work is to provide a probabilistically grounded interpretation of such a partitioning technique in the context of generating local maps. We also discuss how to apply it to different kinds of sensory data (landmarks extracted from stereo images and laser range scans) and how to consider them simultaneously. An important feature of our approach is that the partitioning takes into account the intrinsic characteristics of the sensors, such as the sensor field of view, instead of applying heuristics supplied by a human as in other works. Thus the robot builds “subjective” local maps whose size will be determined by the nature of the sensors. The ideas presented here are supported by experimental results from a real mobile robot as well as simulations for statistical analysis. We discuss the effects of considering different combinations of sensors in the resulting clustering of the environment. [Copyright &y& Elsevier]

Abstract: This paper presents a constructive method to design output-feedback cooperative controllers that force a group of unicycle-type mobile robots with limited sensing ranges to perform desired formation tracking, and guarantee no collisions between the robots. The robot velocities are not required for control implementation. For each robot an interlaced observer, which is a reduced order observer plus an interlaced term, is designed to estimate the robot unmeasured velocities. The observer design is based on a coordinate transformation that transforms the robot dynamics to a new dynamics, which does not contain velocity quadratic terms. The interlaced term is determined after the formation control design is completed to void difficulties due to observer errors and consideration of collision avoidance. Smooth and times differentiable jump functions are introduced and incorporated into novel potential functions to design a formation tracking control system. Despite the robot limited sensing ranges, no switchings are needed to solve the collision avoidance problem. Simulations illustrate the results. [Copyright &y& Elsevier]

Abstract: Potential field method has been widely used for mobile robot path planning, but mostly in a static environment where the target and the obstacles are stationary. The path planning result is normally the direction of the robot motion. In this paper, the potential field method is applied for both path and speed planning, or the velocity planning, for a mobile robot in a dynamic environment where the target and the obstacles are moving. The robot’s planned velocity is determined by relative velocities as well as relative positions among robot, obstacles and targets. The implementation factors such as maximum linear and angular speed of the robot are also considered. The proposed approach guarantees that the robot tracks the moving target while avoiding moving obstacles. Simulation studies are provided to verify the effectiveness of the proposed approach. [Copyright &y& Elsevier]

In this paper the problem of free gait generation and adaptability with reinforcement learning are addressed for a six-legged robot. Using the developed free gait generation algorithm the robot maintains to generate stable gaits according to the commanded velocity. The reinforcement learning scheme incorporated into the free gait generation makes the robot choose more stable states and develop a continuous walking pattern with a larger average stability margin. While walking in normal conditions with no external effects causing unstability, the robot is guaranteed to have stable walk, and the reinforcement learning only improves the stability. The adaptability of the learning scheme is tested also for the abnormal case of deficiency in one of the rear-legs. The robot gets a negative reinforcement when it falls, and a positive reinforcement when a stable transition is achieved. In this way the robot learns to achieve a continuous pattern of stable walk with five legs. The developed free gait generation with reinforcement learning is applied in real-time on the actual robot both for normal walking with different speeds and learning of five-legged walking in the abnormal case. [Copyright &y& Elsevier]

This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach is demonstrated experimentally. [Copyright &y& Elsevier]

In this paper a rotational actuator with a novel adaptable compliance (inverse of stiffness) is presented. First, a number of comparable designs are given with their possible drawbacks. The MACCEPA concept and design is then described in detail. The equation to calculate the generated torque is derived. Depending on the design parameters, it is shown that the torque is a quasi linear function with respect to the angle between the equilibrium position and the actual position. Also, the change of the pre-tension has a quasi linear effect on the torque. Another advantage is that the actuator can be built with standard components, e.g. electrical servo motors. Experiments show independent control of the equilibrium position and compliance. The use of the MACCEPA in the Controlled Passive Walking biped Veronica is described. Controlled Passive Walking is an approach that combines the advantages of actively controlled robots and passive walkers. By adapting the compliance of the joints, natural motions can be chosen in order to obtain a controllable and energy efficient walking motion. To test the concept, the biped Veronica is built, actuated by six MACCEPAs. [Copyright &y& Elsevier]

Wheel odometry is a common method for high resolution relative localisation. However, wheel odometry relies on the integrity and accuracy of a kinematic model. In this paper, a new method for relative localisation, ‘visiodometry’, which does not rely on a kinematic model, is proposed. The system consists of two ground-facing cameras mounted on either side of the robot. From the sequence of images acquired, the relative change in pose of the robot is estimated using a phase correlation based method. Results on a plain coloured carpeted surface, show that the method provides a truly odometric type sensor data input similar in modality and resolution to wheel odometry. A method to calibrate the visiodometry system using a 1D object is also presented. [Copyright &y& Elsevier]

Small robot helicopters are becoming a popular research platform due to the availability of off-the-shelf components and their suitability for useful applications. We describe the Oxford Aerial Tracking System (OATS) that we are commissioning, which takes a commercial airframe and low-level flight controller, and adapts these for use in applications requiring the visual tracking of ground targets. This uses a camera on a two-axis gimbal feeding images into an on-board processing system, which communicates summary information to a ground station. So far we have tested the system off the aircraft using laboratory targets and canned image sequences; we have also developed in simulation a variety of high-level algorithms for the platform, including those for target area scanning, geo-localisation, 3-D path planning, and target trajectory prediction; this paper focuses on the hardware, system software, and object tracking sub-systems. Testing on the airframe is now underway. [Copyright &y& Elsevier]

In human–robot communication it is often important to relate robot sensor readings to concepts used by humans. We suggest the use of a virtual sensor (one or several physical sensors with a dedicated signal processing unit for the recognition of real world concepts) and a method with which the virtual sensor can learn from a set of generic features. The virtual sensor robustly establishes the link between sensor data and a particular human concept. In this work, we present a virtual sensor for building detection that uses vision and machine learning to classify the image content in a particular direction as representing buildings or non-buildings. The virtual sensor is trained on a diverse set of image data, using features extracted from grey level images. The features are based on edge orientation, the configurations of these edges, and on grey level clustering. To combine these features, the AdaBoost algorithm is applied. Our experiments with an outdoor mobile robot show that the method is able to separate buildings from nature with a high classification rate, and to extrapolate well to images collected under different conditions. Finally, the virtual sensor is applied on the mobile robot, combining its classifications of sub-images from a panoramic view with spatial information (in the form of location and orientation of the robot) in order to communicate the likely locations of buildings to a remote human operator. [Copyright &y& Elsevier]

SAURON (Surveillance AUtonomous Robots Over Network) is a research project focused on the use of autonomous patrolling robots for surveillance purposes. This paper presents the structure of SAURON localization module AMIRoLoS (Active Marker Internet-based Robot Localization System). It provides on demand the robot position in indoor and outdoor environments using active markers and commercial off-the-shelf web-cams. The system has a client-server architecture, and is now part of a message-oriented middleware that is under development at the University of Brescia. The functionality of the localization module has been demonstrated with real-world experiments. [Copyright &y& Elsevier]

In this paper we propose a novel method of sensor planning for a mobile robot localization problem. We represent the conditional dependence relation between local sensing results, actions, and belief of the global localization using a Bayesian network. Initially, the structure of the Bayesian network is learned from the complete data of the environment using the algorithm combined with a genetic algorithm (GA). In the execution phase, when the robot is kidnapped to some place, it plans an optimal sensing action by taking into account the trade-off between the sensing cost and the global localization belief, which is obtained by inference in the Bayesian network. We have validated the learning and planning algorithm by simulation experiments in an office environment. [Copyright &y& Elsevier]

Abstract: The Scale Invariant Feature Transform, SIFT, has been successfully applied to robot localization. Still, the number of features extracted with this approach is immense, especially when dealing with omnidirectional vision. In this work, we propose a new approach that reduces the number of features generated by SIFT as well as their extraction and matching time. With the help of a Particle Filter, we demonstrate that we can still localize the mobile robot accurately with a lower number of features. [Copyright &y& Elsevier]