waashk/dblp huggingface dataset:
free gait generation with reinforcement learning for a six-legged robot. 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.
a sensor fusion framework for online sensor and algorithm selection. this paper presents a sensor fusion framework for selecting online the most reliable logical sensors and the most suitable algorithm for fusing sensor data in a robot platform. the framework is rule-based, employing the concept of using the simplest sensor fusion algorithm with the most reliable sensors. the framework is realized by implementing measures that were developed to quantify online sensor performance. statistical, histogram, time series and graphical analyses demonstrate the advantages of this new framework.
constructing dependable certainty grids from unreliable sensor data. measurements from sensors as they are used for robotic grid map applications typically show behavior like degradation or discalibration over time, which affects the quality of the generated maps. this paper presents two novel algorithms for the generation of certainty grids dealing with this behavior. the first algorithm named fault-tolerant certainty grid (ftcg) performs voting over multiple sensor readings. this approach removes up to (n-1)/2 faulty measurements for grid cells that are updated by n independent sensors, however it requires that each grid cell is covered by at least three different independent sensors. the second algorithm named robust certainty grid (rcg) uses a sensor validation method that detects abnormal sensor measurements and adjusts a confidence value for each sensor. this method also supports reintegration of recovered sensors from transient faults and sensor maintenance by providing a measurement for the operability of a sensor. the rcg algorithm works with at least three sensors with a partially overlapping sensing range and needs fewer sensor inputs and less memory than the ftcg approach. results from simulation and an experimental evaluation on an autonomous mobile robot show that under the presence of unreliable sensor data, both algorithms perform better than the bayesian approach typically used for certainty grids.
omni-directional mobile robot controller based on trajectory linearization. in this paper, a nonlinear controller design for an omni-directional mobile robot is presented. the robot controller consists of an outer-loop (kinematics) controller and an inner-loop (dynamics) controller, which are both designed using the trajectory linearization control (tlc) method based on a nonlinear robot dynamic model. the tlc controller design combines a nonlinear dynamic inversion and a linear time-varying regulator in a novel way, thereby achieving robust stability and performance along the trajectory without interpolating controller gains. a sensor fusion method, which combines the onboard sensor and the vision system data, is employed to provide accurate and reliable robot position and orientation measurements, thereby reducing the wheel slippage induced tracking error. a time-varying command filter is employed to reshape an abrupt command trajectory for control saturation avoidance. the real-time hardware-in-the-loop (hil) test results show that with a set of fixed controller design parameters, the tlc robot controller is able to follow a large class of 3-degrees-of-freedom (3dof) trajectory commands accurately.
network robot systems. this article introduces the definition of network robot systems (nrs) as is understood in europe, usa and japan. moreover, it describes some of the nrs projects in europe and japan and presents a summary of the papers of this special issue.
development of a 3dof mobile exoskeleton robot for human upper-limb motion assist. in order to assist physically disabled, injured, and/or elderly persons, we have been developing exoskeleton robots for assisting upper-limb motion, since upper-limb motion is involved in a lot of activities of everyday life. this paper proposes a mechanism and control method of a mobile exoskeleton robot for 3dof upper-limb motion assist (shoulder vertical and horizontal flexion/extension, and elbow flexion/extension motion assist). the exoskeleton robot is mainly controlled by the skin surface electromyogram (emg) signals, since emg signals of muscles directly reflect how the user intends to move. the force vector at the end-effector is taken into account to generate the natural and smooth hand trajectory of the user in the proposed control method. an obstacle avoidance algorithm is applied to prevent accidental collision between the user's upper-limb and the robot frame. the experiment was performed to evaluate the effectiveness of the proposed exoskeleton robot.
autonomous sailboat navigation for short course racing. the paper presents a compact method to calculate a suitable route for a sailboat in order to reach any specified target. the calculation is based on the optimisation of the time derivative of the distance between boat and target and features a hysteresis condition, which is of particular importance for beating to windward. the algorithm provides an answer to the perennial question when to tack on upwind courses. further, it immediately adapts to varying wind conditions. the resulting routes for different conditions are analysed on the basis of a simulation featuring a mathematical boat model. experiments have been carried out using an unmanned and autonomously controlled sailboat. the navigated route agrees well with the simulation results.
monitoring the execution of robot plans using semantic knowledge. even the best laid plans can fail, and robot plans executed in real world domains tend to do so often. the ability of a robot to reliably monitor the execution of plans and detect failures is essential to its performance and its autonomy. in this paper, we propose a technique to increase the reliability of monitoring symbolic robot plans. we use semantic domain knowledge to derive implicit expectations of the execution of actions in the plan, and then match these expectations against observations. we present two realizations of this approach: a crisp one, which assumes deterministic actions and reliable sensing, and uses a standard knowledge representation system (loom); and a probabilistic one, which takes into account uncertainty in action effects, in sensing, and in world states. we perform an extensive validation of these realizations through experiments performed both in simulation and on real robots.
robots in the kitchen: exploiting ubiquitous sensing and actuation. our goal is to develop intelligent service robots that operate in standard human environments, automating common tasks. in pursuit of this goal, we follow the ubiquitous robotics paradigm, in which intelligent perception and control, are combined with ubiquitous computing. by exploiting sensors and effectors in its environment, a robot can perform more complex tasks without becoming overly complex itself. following this insight, we have developed a service robot that operates autonomously in a sensor-equipped kitchen. the robot learns from demonstration, and performs sophisticated tasks, in concert with the network of devices in its environment. we report on the design, implementation, and usage of this system, which is freely available for use, and improvement by others, in the research community.
real-time tour construction for a mobile robot in a dynamic environment. mobile robots are increasingly used in many areas. an optimum trajectory increases the effectiveness of a mobile robot. however, the environment may change dynamically which may require a real-time tour construction for the mobile robot. in this study, a heuristic-based tsp approach is applied to real-time dynamic tour construction problem for a mobile robot. savings algorithm together with dijsktra's algorithm is used to determine a feasible tour for the mobile robot. the proposed method is applicable when the network is complete or sparse, directed or undirected. experiments are conducted to show the effectiveness of the proposed algorithm.
automated design of distributed control rules for the self-assembly of prespecified artificial structures. the self-assembly problem involves the design of agent-level control rules that will cause the agents to form some desired, target structure, subject to environmental constraints. this paper describes a fully automated rule generation procedure that allows structures to successfully self-assemble in a simulated environment with constrained, continuous motion. this environment implicitly imposes ordering constraints on the self-assembly process, where certain parts of the target structure must be assembled before others, and where it may be necessary to assemble (and subsequently disassemble) temporary structures such as staircases. a provably correct methodology is presented for computing a partial order on the self-assembly process, and for generating rules that enforce this order at runtime. the assembly and disassembly of structures is achieved by generating another set of rules, which are inspired by construction behavior among certain species of social insects. computational experiments verify the effectiveness of the approach on a diverse set of target structures.
bayesian space conceptualization and place classification for semantic maps in mobile robotics. the future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. towards this aim, this work attempts to create a hierarchical probabilistic concept-oriented representation of space, based on objects. specifically, it details efforts taken towards learning and generating concepts and attempts to classify places using the concepts gleaned. several algorithms, from naive ones using only object category presence to more sophisticated ones using both objects and relationships, are proposed. both learning and inference use the information encoded in the underlying representation-objects and relative spatial information between them. the approaches are based on learning from exemplars, clustering and the use of bayesian network classifiers. the approaches are generative. further, even though they are based on learning from exemplars, they are not ontology specific; i.e. they do not assume the use of any particular ontology. the presented algorithms rely on a robots inherent high-level feature extraction capability (object recognition and structural element extraction) capability to actually form concept models and infer them. thus, this report presents methods that could enable a robot to to link sensory information to increasingly abstract concepts (spatial constructs). such a conceptualization and the representation that results thereof would enable robots to be more cognizant of their surroundings and yet, compatible to us. experiments on conceptualization and place classification are reported. thus, the theme of this work is-conceptualization and classification for representation and spatial cognition.
rose - a framework for multicast communication via unreliable networks in multi-robot systems. multi-robot systems obtain their performance advantages from close collaboration of the participating robot systems. as the demand for such cooperation increases, the aspect of wireless communication is getting more and more important. to cope with the challenges of the wireless communication we propose an easy to use communication framework for a multi-robot system. this framework is designed for wireless networks, therefore it expects unreliable communication and can even cope with complete network separations. using multicast as the default communication scheme, the framework can directly benefit from the broadcast nature of the wireless medium if such an optimisation is supported by the lower network protocols. we therefore introduce our multicast capable wireless routing protocol, which supports this optimisation. the software is implemented and frequently used in our experimental multi-robot system.
fuzzy logic-based real-time robot navigation in unknown environment with dead ends. the proposed approach in this paper involves a new grid-based map model called ''memory grid'' and a new behavior-based navigation method called ''minimum risk method''. the memory grid map records not only the environmental information, but also the robot experience. the minimum risk method is just one of the applications of the memory grid technique, which addresses the local minimum problem faced by a goal-oriented robot navigating in unknown indoor environments. the minimum risk implies that the robot is able to choose the safest region that can avoid colliding with obstacles and prevent the robot from iterating previous trajectory. this method is demonstrated to work in long wall, large concave, recursive u-shaped, unstructured, cluttered, maze-like, and dynamic indoor environments. it adopts a strategy of multi-behavior coordination in which a novel path-searching behavior is developed to recommend the region offering the minimum risk. fuzzy logic is used to implement the behavior design and coordination. the proposed approach is verified with simulation and real-world tests.
depth control of remotely operated underwater vehicles using an adaptive fuzzy sliding mode controller. sliding mode control, due to its robustness against modelling imprecisions and external disturbances, has been successfully employed to the dynamic positioning of remotely operated underwater vehicles. in order to improve the performance of the complete system, the discontinuity in the control law must be smoothed out to avoid the undesirable chattering effects. the adoption of a properly designed thin boundary layer has proven effective in completely eliminating chattering, however, leading to an inferior tracking performance. this paper describes the development of a depth control system for remotely operated underwater vehicles. the adopted approach is based on the sliding mode control strategy and enhanced by an adaptive fuzzy algorithm for uncertainty/disturbance compensation. the stability and convergence properties of the closed-loop system are analytically proved using lyapunov stability theory and barbalat's lemma. numerical results are presented in order to demonstrate the control system performance.
conceptual spatial representations for indoor mobile robots. we present an approach for creating conceptual representations of human-made indoor environments using mobile robots. the concepts refer to spatial and functional properties of typical indoor environments. following different findings in spatial cognition, our model is composed of layers representing maps at different levels of abstraction. the complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition. the system also incorporates a linguistic framework that actively supports the map acquisition process, and which is used for situated dialogue. finally, we discuss the capabilities of the integrated system.
kinematic analysis of two novel 3upu i and 3upu ii pkms. two novel 3upu i and 3upu ii pkms (parallel kinematic machines) with two rotations and one translation are proposed, and their kinematics are studied systematically. first, the kinematic characteristics of the 3upu i and 3upu ii pkms are analyzed and the geometric constrained equations are derived. second, some analytic formulae are derived for solving inverse displacement, inverse/forward velocity and acceleration of the two pkms. third, the reachable workspaces of the two pkms are solved and analyzed. the analytic results are verified by their simulation mechanism.
curious george: an attentive semantic robot. state-of-the-art methods have recently achieved impressive performance for recognising the objects present in large databases of pre-collected images. there has been much less focus on building embodied systems that recognise objects present in the real world. this paper describes an intelligent system that attempts to perform robust object recognition in a realistic scenario, where a mobile robot moving through an environment must use the images collected from its camera directly to recognise objects. to perform successful recognition in this scenario, we have chosen a combination of techniques including a peripheral-foveal vision system, an attention system combining bottom-up visual saliency with structure from stereo, and a localisation and mapping technique. the result is a highly capable object recognition system that can be easily trained to locate the objects of interest in an environment, and subsequently build a spatial-semantic map of the region. this capability has been demonstrated during the semantic robot vision challenge, and is further illustrated with a demonstration of semantic mapping. we also empirically verify that the attention system outperforms an undirected approach even with a significantly lower number of foveations.
the initial development of object knowledge by a learning robot. we describe how a robot can develop knowledge of the objects in its environment directly from unsupervised sensorimotor experience. the object knowledge consists of multiple integrated representations: trackers that form spatio-temporal clusters of sensory experience, percepts that represent properties for the tracked objects, classes that support efficient generalization from past experience, and actions that reliably change object percepts. we evaluate how well this intrinsically acquired object knowledge can be used to solve externally specified tasks, including object recognition and achieving goals that require both planning and continuous control.
virtual sensors for human concepts - building detection by an outdoor mobile robot. 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.
sensor-driven neural control for omnidirectional locomotion and versatile reactive behaviors of walking machines. this article describes modular neural control structures for different walking machines utilizing discrete-time neurodynamics. a simple neural oscillator network serves as a central pattern generator producing the basic rhythmic leg movements. other modules, like the velocity regulating and the phase switching networks, enable the machines to perform omnidirectional walking as well as reactive behaviors, like obstacle avoidance and different types of tropisms. these behaviors are generated in a sensori-motor loop with respect to appropriate sensor inputs, to which a neural preprocessing is applied. the neuromodules presented are small so that their structure-function relationship can be analysed. the complete controller is general in the sense that it can be easily adapted to different types of even-legged walking machines without changing its internal structure and parameters.
reinforcement learning for problems with symmetrical restricted states. a reinforcement learning method is proposed that can utilize parts of states and their partial symmetries to solve a problem efficiently. in most cases the action selection does not need considering all the states but only needs looking at parts of states or restricted state of corresponding action. moreover, restricted states of different actions are symmetrical, and thus the action value function based on restricted states can be shared which further reduces the reinforcement learning problem size. the method is compared, in terms of simulation results and other aspects, with other standard reinforcement learning methods.
robot task planning using semantic maps. task planning for mobile robots usually relies solely on spatial information and on shallow domain knowledge, such as labels attached to objects and places. although spatial information is necessary for performing basic robot operations (navigation and localization), the use of deeper domain knowledge is pivotal to endow a robot with higher degrees of autonomy and intelligence. in this paper, we focus on semantic knowledge, and show how this type of knowledge can be profitably used for robot task planning. we start by defining a specific type of semantic maps, which integrates hierarchical spatial information and semantic knowledge. we then proceed to describe how these semantic maps can improve task planning in two ways: extending the capabilities of the planner by reasoning about semantic information, and improving the planning efficiency in large domains. we show several experiments that demonstrate the effectiveness of our solutions in a domain involving robot navigation in a domestic environment.
distributed coordination architecture for multi-robot formation control. in the exploration and implementation of formation control strategies, communication range and bandwidth limitations form a barrier to large scale formation control applications. the limitations of current formation control strategies involving a leader-follower approach and a consensus-based approach with fully available group trajectory information are explored. a unified, distributed formation control architecture that accommodates an arbitrary number of group leaders and arbitrary information flow among vehicles is proposed. the architecture requires only local neighbor-to-neighbor information exchange. in particular, an extended consensus algorithm is applied on the group level to estimate the time-varying group trajectory information in a distributed manner. based on the estimated group trajectory information, a consensus-based distributed formation control strategy is then applied for vehicle level control. the proposed architecture is experimentally implemented and validated on a multi-robot platform under local neighbor-to-neighbor information exchange with a single or multiple leaders involved.