waashk/dblp huggingface dataset:
robot navigation in very cluttered environments by preference-based fuzzy behaviors. one of the key challenges in application of autonomous ground vehicles (agvs) is navigation in environments that are densely cluttered with obstacles. the control task becomes more complex when the configuration of obstacles is not known a priori. the most popular control methods for such systems are based on reactive local navigation schemes that tightly couple the robot actions to the sensor information. because of the environmental uncertainties, fuzzy behavior systems have been proposed. the most difficult problem in applying fuzzy-reactive-behavior-based navigation control systems is that of arbitrating or fusing the reactions of the individual behaviors, which is addressed here by the use of preference logic. this paper presents the design of a preference-based fuzzy behavior system for navigation control of robotic vehicles using the multivalued logic framework. as shown in simulation and experimental results, the proposed method allows the robot to smoothly and effectively navigate through cluttered environments such as dense forests. experimental comparisons with the vector field histogram method (vfh) show that the proposed method usually produces smoother albeit longer paths to the goal.
on multi-rate fusion for non-linear sampled-data systems: application to a 6d tracking system. egomotion estimation, e.g. for robot navigation or augmented reality applications, requires the fusion of non-linear sampled-data system with different sensors. an example is to fuse the complimentary characteristics of visual and inertial sensors. existing approaches either use kalman filters in conventionally sampled systems or use particle filters to accommodate the uncertainty of motion models. this paper introduces an approach that models multi-rate non-linear systems to exploit the characteristics of both sensors, assuming synchronicity and periodicity of measurements. the final contribution of this paper is an in-depth analysis and performance comparison of the extended kalman filter, the unscented kalman filter and three particle filters (bootstrap, extended and unscented). while there is large debate over the pros and cons of these two approaches, this work shows the following results for fusing visual and inertial data in 6 dof (position and orientation) in a tracking application: the bootstrap particle filter gives higher estimation error than extended and unscented particle filters, which give very similar results than extended and unscented kalman filters, but with considerable higher computational burden.
objective locomotion parameters based inverted pendulum trajectory generator. this paper describes a new trajectory generator for lucy, a bipedal walking robot actuated by pleated pneumatic artificial muscles. the strategy is based on the inverted pendulum approximation, which models the robot as a single point mass. the trajectory generator allows the step length, intermediate foot lift and velocity to be chosen for each step while keeping the zero moment point (zmp) in the ankle point during the single support phase and it provides a smooth transition of the zmp from the rear ankle point to the front ankle point during the double support phase. the path of the zmp is discussed for a complete multibody model instead of a single point mass. the proposed trajectory generator has been tested in the robot and the results are presented and discussed.
closed loop motion planning of cooperating mobile robots using graph connectivity. in this paper we address the problem of planning the motion of a team of cooperating mobile robots subject to constraints on relative configuration imposed by the nature of the task they are executing. we model constraints between robots using a graph where each edge is associated with the interaction between two robots and describes a constraint on relative configurations. we develop a decentralized motion control system that leads each robot to their individual goals while maintaining the constraints specified on the graph. we present experimental results with groups of holonomic and non-holonomic mobile robots.
theory and implementation of path planning by negotiation for decentralized agents. this paper presents a cooperative decentralized path-planning algorithm for a group of autonomous agents that provides guaranteed collision-free trajectories in real-time. the algorithm is robust with respect to arbitrary delays in the wireless traffic, possible sources being transmission time and error correction. agents move on reserved areas which are guaranteed not to intersect, therefore ensuring safety. a handshaking procedure guarantees recent information states for the agents. conflicts between agents are resolved by a cost-based negotiation process. the basic algorithm is augmented by the introduction of waypoints, which increase performance at the cost of additional wireless traffic. an implementation of the algorithm is tested in simulation and successfully applied to a real system of autonomous robots. the results are presented and discussed.
context-based design of robotic systems. the need for improving the robustness, as well as the ability to adapt to different operational conditions, is a key requirement for a wider deployment of robots in many application domains. in this paper, we present an approach to the design of robotic systems, that is based on the explicit representation of knowledge about context. the goal of the approach is to improve the system's performance, by dynamically tailoring the functionalities of the robot to the specific features of the situation at hand. while the idea of using contextual knowledge is not new, the proposed approach generalizes previous work, and its advantages are discussed through a case study including several experiments. in particular, we identify many attempts to use contextual knowledge in several basic functionalities of a mobile robot such as: behavior, navigation, exploration, localization, mapping and perception. we then show how re-designing our mobile platform with a common representation of contextual knowledge, leads to interesting improvements in many of the above mentioned components, thus achieving greater flexibility and robustness in the face of different situations. moreover, a clear separation of contextual knowledge leads to a design methodology, which supports the design of small specialized system components instead of complex self-contained subsystems.
the virtual wall approach to limit cycle avoidance for unmanned ground vehicles. robot navigation in unknown and very cluttered environments constitutes one of the key challenges in unmanned ground vehicle (ugv) applications. navigational limit cycles can occur when navigating (ugvs) using behavior-based or other reactive algorithms. limit cycles occur when the robot is navigating towards the goal but enters an enclosure that has its opening in a direction opposite to the goal. the robot then becomes effectively trapped in the enclosure. this paper presents a solution named the virtual wall approach (vwa) to the limit cycle problem for robot navigation in very cluttered environments. this algorithm is composed of three stages: detection, retraction, and avoidance. the detection stage uses spatial memory to identify the limit cycle. once the limit cycle has been identified, a labeling operator is applied to a local map of the obstacle field to identify the obstacle or group of obstacles that are causing the deadlock enclosure. the retraction stage defines a waypoint for the robot outside the deadlock area. when the robot crosses the boundary of the deadlock enclosure, a virtual wall is placed near the endpoints of the enclosure to designate this area as off-limits. finally, the robot activates a virtual sensor so that it can proceed to its original goal, avoiding the virtual wall and obstacles found on its way. simulations, experiments, and analysis of the vwa implemented on top of a preference-based fuzzy behavior system demonstrate the effectiveness of the proposed method.
towards 3d point cloud based object maps for household environments. this article investigates the problem of acquiring 3d object maps of indoor household environments, in particular kitchens. the objects modeled in these maps include cupboards, tables, drawers and shelves, which are of particular importance for a household robotic assistant. our mapping approach is based on pcd (point cloud data) representations. sophisticated interpretation methods operating on these representations eliminate noise and resample the data without deleting the important details, and interpret the improved point clouds in terms of rectangular planes and 3d geometric shapes. we detail the steps of our mapping approach and explain the key techniques that make it work. the novel techniques include statistical analysis, persistent histogram features estimation that allows for a consistent registration, resampling with additional robust fitting techniques, and segmentation of the environment into meaningful regions.
learning and generalising semantic knowledge from object scenes. the robot described in this paper learns words that relate to objects and their attributes, and also learns concepts, which may be recursive, that involve relationships between several objects. once the system is explicitly taught some words by a human teacher it finds new objects that might help to refine its concepts. once it has found a new object, it tries to generalise its concepts to include the new object and asks the teacher for feedback. the robot learns further properties of objects by interacting with them, by touching them or walking around them to gain a new perspective. the system learns semantic knowledge from spoken interactions, using speech recognition and generation, motion segmentation, feature extraction from images using ripple down rules and generalisation using inductive logic programming.
end-to-end congestion control protocols for remote programming of robots, using heterogeneous networks: a comparative analysis. there are many interesting aspects of internet telerobotics within the network robotics context, such as variable bandwidth and time-delays. some of these aspects have been treated in the literature from the control point of view. moreover, only a little work is related to the way internet protocols can help to minimize the effect of delay and bandwidth fluctuation on network robotics. in this paper, we present the capabilities of tcp, udp, tcp las vegas, tear, and trinomial protocols, when performing a remote experiment within a network robotics application, the uji industrial telelaboratory. comparative analysis is presented through simulations within the ns2 platform. results show how these protocols perform in two significant situations within the network robotics context, using heterogeneous wired networks: (1) an asymmetric network when controlling the system through a adsl connection, and (2) a symmetric network using the system on campus. conclusions show a set of characteristics the authors of this paper consider very important when designing an end-to-end congestion control transport protocol for internet telerobotics.
visual door detection integrating appearance and shape cues. an important component of human-robot interaction is the capability to associate semantic concepts with encountered locations and objects. this functionality is essential for visually guided navigation as well as location and object recognition. in this paper we focus on the problem of door detection using visual information only. doors are frequently encountered in structured man-made environments and function as transitions between different places. we adopt a probabilistic approach for door detection, by defining the likelihood of various features for generated door hypotheses. differing from previous approaches, the proposed model captures both the shape and appearance of the door. this is learned from a few training examples, exploiting additional assumptions about the structure of indoor environments. after the learning stage, we describe a hypothesis generation process and several approaches to evaluate the likelihood of the generated hypotheses. the approach is tested on numerous examples of indoor environment. it shows a good performance provided that the door extent in the images is sufficiently large and well supported by low level feature measurements.
human behavior recognition using unconscious cameras and a visible robot in a network robot system. we developed a network robot system integrating various types of robots via ubiquitous networks that introduce an interactive robot into areas in which people are located. in this paper, we present a human behavior recognition method necessary for providing guidance in a public space, which uses a tangible network robot system composed of a mobile robot and vision sensors embedded in an environment. we define some basic features that a person typically exhibits when in need of guidance. various human behaviors were successfully interpreted by first recognizing the basic features and then forming their different combinations.
a dynamic object manipulation approach to dynamic biped locomotion. in this paper, we aim at an integrated approach to dynamic biped walking (dbw) and dynamic object manipulation (dom) at an abstract level. to this end, we offer a unified and abstract concept with a dual interpretation as a dom and as a dbw system. we validate the proposed approach by using a set of simulations on an illustrative case study and show how it can be used in modeling as well as design of planning and control algorithms for dom and dbw systems. in the case study, we describe the proposed approach and show its dual interpretation by identifying the relations between 2d dynamic object manipulation of a disc using two planar manipulators and 2d dynamic object locomotion of lower part of a biped robot. more specifically, having obtained the equations of dom, we change the boundary conditions of the problem in such a way that both radius and mass of the disc tend to infinity. simultaneously, both size and mass of the manipulators' base, i.e. the planet earth, tend to some values in the order of human body mass and dimension. regarding these changes, we can transform dom into dbw and vice versa. to test the proposed approach, a simple control strategy is introduced to handle impact between the manipulators (legs) and the object (the earth). in addition, a motion planning system is designed in such a way that the manipulators (legs) catch and throw the manipulated object (the earth) in appropriate configurations.
switching visual control based on epipoles for mobile robots. in this paper, we present a visual control approach consisting in a switching control scheme based on the epipolar geometry. the method facilitates a classical teach-by-showing approach where a reference image is used to control the robot to the desired pose (position and orientation). as a result of our proposal a mobile robot carries out a smooth trajectory towards the target and the epipolar geometry model is used through the whole motion. the control scheme developed considers the motion constraints of the mobile platform in a framework based on the epipolar geometry that does not rely on artificial markers or specific models of the environment. the proposed method is designed in order to cope with the degenerate estimation case of the epipolar geometry with short baseline. experimental evaluation has been performed in realistic indoor and outdoor settings.
exploration of a cluttered environment using voronoi transform and fast marching. the extended voronoi transform and the fast marching method combination provide potential maps for robot navigation in previously unexplored dynamic environments. the extended voronoi transform of a binary image of the environment gives a grey scale that is darker near the obstacles and walls and lighter far from them. the logarithm of the extended voronoi transform imitates the repulsive electric potential from walls and obstacles. the method proposed, called voronoi fast marching method, uses a fast marching technique on the extended voronoi transform of the environment's image, provided by sensors, to determine a motion plan. the computational efficiency of the method lets the planner operate at high rate sensor frequencies. this avoids the need for collision avoidance algorithms. the robot is directed towards the most unexplored and free zones of the environment so as to be able to explore all the workspace. this method is very fast and reliable and the trajectories are similar to the human trajectories: smooth and not very close to obstacles and walls. in this article we propose its application to the task of exploring unknown environments.
on redundancy, efficiency, and robustness in coverage for multiple robots. motivated by potential efficiency and robustness gains, there is growing interest in the use of multiple robots for coverage. in coverage, robots visit every point in a target area, at least once. previous investigations of multi-robot coverage focus on completeness of the coverage, and on eliminating redundancy, but do not formally address robustness. moreover, a common assumption is that elimination of redundancy leads to improved efficiency (coverage time). we address robustness and efficiency in a novel family of multi-robot coverage algorithms, based on spanning-tree coverage of approximate cell decomposition of the work-area. we analytically show that the algorithms are robust, in that as long as a single robot is able to move, the coverage will be completed. we also show that non-redundant (non-backtracking) versions of the algorithms have a worst-case coverage time virtually identical to that of a single robot-thus no performance gain is guaranteed in non-redundant coverage. surprisingly, however, redundant coverage algorithms lead to guaranteed performance which halves the coverage time even in the worst case. we present a polynomial-time redundant coverage algorithm, whose coverage time is optimal, and which is able to address robots heterogeneous in speed and fuel. we compare the performance of all algorithms empirically and show that the use of the optimal algorithm leads to significant improvements in coverage time.
framework and service allocation for network robot platform and execution of interdependent services. this research introduces a framework for network robot platform (nwr-pf) and a service allocation method for heterogeneous distributed robots; they cooperate to perform services that include interdependent tasks. the proposed framework is composed of three layers: connection units, the area management gateway, and the robot-user interaction database. the area management gateway and robot-user interaction database treat the information of the users, robots, services, and service history in a uniform manner, because the connection units hide the differences, such as format and protocol, among the heterogeneous robots. the 4w1h matching method is also proposed for service allocation. the method selects the most suitable robots by comparing the elements of user, robot, and service information. moreover, it generates robot commands in a common format, by combining the service scenario, and service history. a feature of the method is that both robotic functions to execute service and functions that robots possess are described as independent parameters. after the service trigger is received, both the robot and scenario needed to execute the service are decided by matching these parameters. experiments show that robots controlled by nwr-pf can perform interdependent services by referring to the service history.
robust and accurate global vision system for real time tracking of multiple mobile robots. this paper presents a new global vision system for tracking of multiple mobile robots. to the best knowledge of the authors it outperforms all existing global vision systems with respect to measurement precision and accuracy, high speed and real time operation and reliable tracking of large (theoretically unlimited) number of robots under light intensity changes. the originality of the proposed system lies mainly in specially designed robot marks and robots' poses measuring directly in bayer format image delivered by the camera. these two measures enable robust pose estimation of the robots with subpixel precision, while the significant simplification of the image processing algorithms ensures tracking of many robots with very high framerates. with algorithms running on a 3 ghz athlon 64 processor 65 robots can be tracked at 80 fps. moreover, in order to perform a thorough analysis of the system performances related to defined requirements, we propose a new experimental procedure that can serve as a benchmark for evaluation of other systems for the same purpose.
facial behaviour mapping - from video footage to a robot head. as autonomous robotic systems advance, they will be required and designed for interaction with humans in order to exchange information, which is essential for fulfilling their tasks. it is well established that human-machine interactions are more believable and memorable when a physical entity is present, provided that the machine behaves in a realistic manner. it is desirable to adopt face-to-face communication, because it is the most natural and efficient way of exchanging information, and does not require users to alter their habits. in this context, this paper describes a process for animating a robot head, based on video input of a human head. we map from the 2d coordinates of feature points into the robot's servo space, using partial least squares (pls). learning is done using a small set of keyframes manually created by an animator. the method is efficient, robust to tracking errors and independent of the scale of the face being tracked.
accurate robot simulation through system identification. robot simulators are useful tools for developing robot behaviour. they provide a fast and efficient means for testing robot control code at the convenience of the office desk. in all but the simplest cases though, due to complexities of physical systems modelled in the simulator, there are considerable differences between the behaviour of the robot in the simulator and that in the real world environment. in this paper we present a novel method to create a robot simulator using real sensor data. logged sensor data are used to construct a mathematically explicit model (in the form of a narmax polynomial) of the robot's environment. the advantage of such a transparent model -in contrast to opaque modelling methods such as artificial neural networks -is that it can be analysed to characterise the modelled system, using established mathematical methods. in this paper we compare the behaviour of the robot running a particular task in both the simulator and the real-world using qualitative and quantitative measures including statistical methods to investigate the faithfulness of the simulator.
fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping. this work investigates the use of semantic information to link ground level occupancy maps and aerial images. a ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omni-directional camera, gps and a laser range finder. this semantic information is used for local and global segmentation of an aerial image. the result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground.