Category Archives: Robotics

Socially acceptable collision avoidance

May 10, 2018 08:25 ,

Haoan Wang, Antonio Tota, Bilin Aksun-Guvenc, Levent Guvenc Real time implementation of socially acceptable collision avoidance of a low speed autonomous shuttle using the elastic band method, Mechatronics, Volume 50, 2018, Pages 341-355, DOI: 10.1016/j.mechatronics.2017.11.009.

This paper presents the real time implementation of socially acceptable collision avoidance using the elastic band method for low speed autonomous shuttles operating in high pedestrian density environments. The modeling and validation of the research autonomous vehicle used in the experimental implementation is presented first, followed by the details of the Hardware-In-the-Loop connected and autonomous vehicle simulator used. The socially acceptable collision avoidance algorithm is formulated using the elastic band method as an online, local path modification algorithm. Parameter space based robust feedback plus feedforward steering controller design is used. Model-in-the-loop, Hardware-In-the-Loop and road testing in a proving ground are used to demonstrate the effectiveness of the real time implementation of the elastic band based socially acceptable collision avoidance method of this paper.

Posted in: Robot motion planning , Tagged: , ,

Omnidirectional localization

May 9, 2018 08:10 ,

Milad Ramezani, Kourosh Khoshelham, Clive Fraser, Pose estimation by Omnidirectional Visual-Inertial Odometry,Robotics and Autonomous Systems,
Volume 105, 2018, Pages 26-37, DOI: 10.1016/j.robot.2018.03.007.

In this paper, a novel approach to ego-motion estimation is proposed based on visual and inertial sensors, named Omnidirectional Visual-Inertial Odometry (OVIO). The proposed approach combines omnidirectional visual features with inertial measurements within the Multi-State Constraint Kalman Filter (MSCKF). In contrast with other visual inertial odometry methods that use visual features captured by perspective cameras, the proposed approach utilizes spherical images obtained by an omnidirectional camera to obtain more accurate estimates of the position and orientation of the camera. Because the standard perspective model is unsuitable for omnidirectional cameras, a measurement model on a plane tangent to the unit sphere rather than on the image plane is defined. The key hypothesis of OVIO is that a wider field of view allows the incorporation of more visual features from the surrounding environment, thereby improving the accuracy and robustness of the ego-motion estimation. Moreover, by using an omnidirectional camera, a situation where there is not enough texture is less likely to arise. Experimental evaluation of OVIO using synthetic and real video sequences captured by a fish-eye camera in both indoor and outdoor environments shows the superior performance of the proposed OVIO as compared to the MSCKF using a perspective camera in both positioning and attitude estimation.

Posted in: Mobile Robot Localization , Tagged:

Dynamic and efficient occupancy mapping

May 7, 2018 08:08 ,

Vitor Guizilini and Fabio Ramos, Towards real-time 3D continuous occupancy mapping using Hilbert maps, The International Journal of Robotics Research
Vol 37, Issue 6, pp. 566 – 584, DOI: 10.1177/0278364918771476.

The ability to model the surrounding space and determine which areas are occupied is of key importance in many robotic applications, ranging from grasping and manipulation to path planning and obstacle avoidance. Occupancy modeling is often hindered by several factors, such as: real-time constraints, that require quick updates and access to estimates; quality of available data, that may contain gaps and partial occlusions; and memory requirements, especially for large-scale environments. In this work we propose a novel framework that elegantly addresses all these issues, by producing an efficient non-stationary continuous occupancy function that can be efficiently queried at arbitrary resolutions. Furthermore, we introduce techniques that allow the learning of individual features for different areas of the input space, that are better able to model its contained information and promote a higher-level understanding of the observed scene. Experimental tests were conducted on both simulated and real large-scale datasets, showing how the proposed framework rivals current state-of-the-art techniques in terms of computational speed while achieving a substantial decrease (of orders of magnitude) in memory requirements and demonstrating better interpolative powers, that are able to smooth out sparse and noisy information.

Posted in: Mobile robot mapping , Tagged:

A nice analysis of the particularities of deep learning when applied to robotics, where the need to act is principal (unlike in other disciplines such as computer vision)

April 28, 2018 07:51 ,

Niko Sünderhauf, Oliver Brock, Walter Scheirer, Raia Hadsell, Dieter Fox, Jürgen Leitner, Ben Upcroft, Pieter Abbeel, Wolfram Burgard, Michael Milford, and Peter Corke, The limits and potentials of deep learning for robotics, The International Journal of Robotics Research Vol 37, Issue 4-5, pp. 405 – 420, DOI: 10.1177/0278364918770733.

The application of deep learning in robotics leads to very specific problems and research questions that are typically not addressed by the computer vision and machine learning communities. In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning. We explain the need for better evaluation metrics, highlight the importance and unique challenges for deep robotic learning in simulation, and explore the spectrum between purely data-driven and model-driven approaches. We hope this paper provides a motivating overview of important research directions to overcome the current limitations, and helps to fulfill the promising potentials of deep learning in robotics.

Some quotes beyond the abstract:

Deep learning systems, e.g. for classification or detection, typically return scores from their softmax layers that are proportional to the system’s confidence, but are not calibrated probabilities, and therefore not useable in a Bayesian sensor fusion framework

If, for example, an object detection system is fooled by data outside of its training data distribution (Goodfellow et al., 2014; Nguyen et al., 2015a), the consequences for a robot acting on false, but high-confidence detections can be catastrophic

As the robot moves in its environment, the camera will observe the scene from different viewpoints, which poses both challenges and opportunities to a robotic vision system […] One of the biggest advantages robotic vision can draw from its embodiment is the potential to control the camera, move it, and change its viewpoint to improve its perception or gather additional information about the scene. This is in stark contrast to most computer vision scenarios […] As an extension of active vision, a robotic system could purposefully manipulate the scene to aid its perception

In his influential 1867 book on physiological optics, Von Helmholtz (1867) formulated the idea that humans use unconscious reasoning, inference or conclusion, when processing visual information. Since then, psychologists have devised various experiments to investigate these unconscious mechanisms (Goldstein and Brockmole, 2016), modernized Helmholtz’s original ideas (Rock, 1983), and reformulated them in the framework of Bayesian inference (Kersten et al., 2004).

The properties of model-based and deep-learned approaches can be measured along multiple dimensions, including the kind of representations used for reasoning, how generally applicable their solutions are, how robust they are in real-world settings, how efficiently they make use of data, and how computationally efficient they are during operation. Model-based approaches often rely on explicit models of objects and their shape, surface, and mass properties, and use these to predict and control motion through time. In deep learning, models are typically implicitly encoded via networks and their parameters. As a consequence, model-based approaches have wide applicability, since the physics underlying them are universal. However, at the same time, the parameters of these models are difficult to estimate from perception, resulting in rather brittle performance operating only in local basins of convergence. Deep learning, on the other hand, enables highly robust performance when trained on sufficiently large data sets that are representative of the operating regime of the system. However, the implicit models learned by current deep learning techniques do not have the general applicability of physics-based reasoning. Model-based approaches are significantly more data efficient, related to their smaller number of parameters. The optimizations required for model-based approaches can be performed efficiently, but the basin of convergence can be rather small. In contrast, deep-learned solutions are often very fast and can have very large basins of convergence. However, they do not perform well if applied in a regime outside the training data

Posted in: Robotics , Tagged:

A mathematical study of controllers that produce paths with beautfiul shapes to reach a target point by a unicycle vehicle

April 26, 2018 07:25 ,

T. Tripathy and A. Sinha, Unicycle With Only Range Input: An Array of Patterns, IEEE Transactions on Automatic Control, vol. 63, no. 5, pp. 1300-1312, DOI: 10.1109/TAC.2017.2736940.

The objective of this paper is to generate planar patterns using an autonomous agent modeled as a unicycle. The patterns are generated about a stationary point referred to as the target. To achieve the same, the paper proposes a family of control inputs that are continuous functions of range, which is the distance between the unicycle and the target. The paper studies in detail a characterization of the resulting trajectories, which are a plethora of patterns of parametric curves (circles, spirals, epicyclic curves like hypotrochoids) and more. These appealing patterns find applications in exploration, coverage, land mine detection, etc., where the target represents any point of interest like a landmark or a beacon. The paper also investigates the necessary conditions on the control laws in order to generate patterns of desired shapes and bounds. Furthermore, to generate desired patterns with arbitrary initial conditions, a switching strategy is proposed which is illustrated using an algorithm. The paper presents a series of simulations of appealing patterns generated using the proposed control laws.

Posted in: Control Engineering, Robot motion planning

How a robot can learn to recognize itself on a mirror

April 20, 2018 07:50 ,

Zeng, Y., Zhao, Y., Bai, J. et al., Toward Robot Self-Consciousness (II): Brain-Inspired Robot Bodily Self Model for Self-Recognition, Cogn Comput (2018) 10: 307, DOI: 10.1007/s12559-017-9505-1.

The neural correlates and nature of self-consciousness is an advanced topic in Cognitive Neuroscience. Only a few animal species have been testified to be with this cognitive ability. From artificial intelligence and robotics point of view, few efforts are deeply rooted in the neural correlates and brain mechanisms of biological self-consciousness. Despite the fact that the scientific understanding of biological self-consciousness is still in preliminary stage, we make our efforts to integrate and adopt known biological findings of self-consciousness to build a brain-inspired model for robot self-consciousness. In this paper, we propose a brain-inspired robot bodily self model based on extensions to primate mirror neuron system and apply it to humanoid robot for self recognition. In this model, the robot firstly learns the correlations between self-generated actions and visual feedbacks in motion by learning with spike timing dependent plasticity (STDP), and then learns the appearance of body part with the expectation that the visual feedback is consistent with its motion. Based on this model, the robot uses multisensory integration to learn its own body in real world and in mirror. Then it can distinguish itself from others. In a mirror test setting with three robots with the same appearance, with the proposed brain-inspired robot bodily self model, each of them can recognize itself in the mirror after these robots make random movements at the same time. The theoretic modeling and experimental validations indicate that the brain-inspired robot bodily self model is biologically inspired, and computationally feasible as a foundation for robot self recognition.

Posted in: Developmental robotics, Psycho-physiological bases of engineering , Tagged:

A novel algorithm for coverage path planning with very strong guarantees

April 19, 2018 07:38 ,

J. Song and S. Gupta, $varepsilon ^{star }$: An Online Coverage Path Planning Algorithm, IEEE Transactions on Robotics, vol. 34, no. 2, pp. 526-533, DOI: 10.1109/TRO.2017.2780259.

This paper presents an algorithm called ε*, for online coverage path planning of unknown environment. The algorithm is built upon the concept of an Exploratory Turing Machine (ETM), which acts as a supervisor to the autonomous vehicle to guide it with adaptive navigation commands. The ETM generates a coverage path online using Multiscale Adaptive Potential Surfaces (MAPS), which are hierarchically structured and dynamically updated based on sensor information. The ε*-algorithm is computationally efficient, guarantees complete coverage, and does not suffer from the local extrema problem. Its performance is validated by 1) high-fidelity simulations on Player/Stage and 2) actual experiments in a laboratory setting on autonomous vehicles.

Posted in: Robot motion planning, Robot task planning , Tagged:

Rao-Blackwellized Particle Filter SLAM with grid maps in which particles do not contain the whole map but only a part

April 19, 2018 07:32 ,

H. Jo, H. M. Cho, S. Jo and E. Kim, Efficient Grid-Based Rao–Blackwellized Particle Filter SLAM With Interparticle Map Sharing, IEEE/ASME Transactions on Mechatronics, vol. 23, no. 2, pp. 714-724, DOI: 10.1109/TMECH.2018.2795252.

In this paper, we propose a novel and efficient grid-based Rao-Blackwellized particle filter simultaneous localization and mapping (RBPF-SLAM) with interparticle map shaping (IPMS). The proposed method aims at saving the computational memory in the grid-based RBPF-SLAM while maintaining the mapping accuracy. Unlike conventional RBPF-SLAM in which each particle has its own map of the whole environment, each particle has only a small map of the nearby environment called an individual map in the proposed method. Instead, the map of the remaining large environment is shared by the particles. The part shared by the particles is called a base map. If the individual small maps become reliable enough to trust, they are merged with the base map. To determine when and which part of an individual map should be merged with the base map, we propose two map sharing criteria. Finally, the proposed IPMS RBPF-SLAM is applied to the real-world datasets and benchmark datasets. The experimental results show that our method outperforms conventional methods in terms of map accuracy versus memory consumption.

Posted in: Mobile robot SLAM , Tagged: , ,

Robot topological navigation

April 13, 2018 07:25 ,

Sergio Miguel-Tomé, Navigation through unknown and dynamic open spaces using topological notions,Connection Science vol. 30, iss. 2, DOI: 10.1080/09540091.2016.1277691.

Until now, most algorithms used for navigation have had the purpose of directing system towards one point in space. However, humans communicate tasks by specifying spatial relations among elements or places. In addition, the environments in which humans develop their activities are extremely dynamic. The only option that allows for successful navigation in dynamic and unknown environments is making real-time decisions. Therefore, robots capable of collaborating closely with human beings must be able to make decisions based on the local information registered by the sensors and interpret and express spatial relations. Furthermore, when one person is asked to perform a task in an environment, this task is communicated given a category of goals so the person does not need to be supervised. Thus, two problems appear when one wants to create multifunctional robots: how to navigate in dynamic and unknown environments using spatial relations and how to accomplish this without supervision. In this article, a new architecture to address the two cited problems is presented, called the topological qualitative navigation architecture. In previous works, a qualitative heuristic called the heuristic of topological qualitative semantics (HTQS) has been developed to establish and identify spatial relations. However, that heuristic only allows for establishing one spatial relation with a specific object. In contrast, navigation requires a temporal sequence of goals with different objects. The new architecture attains continuous generation of goals and resolves them using HTQS. Thus, the new architecture achieves autonomous navigation in dynamic or unknown open environments.

Posted in: Robot task planning , Tagged:

POMDPs aware of the data association problem

March 29, 2018 09:32 ,

Shashank Pathak, Antony Thomas, and Vadim Indelman, A unified framework for data association aware robust belief space planning and perception, The International Journal of Robotics Research Vol 37, Issue 2-3, pp. 287 – 315, DOI: 10.1177/0278364918759606.

We develop a belief space planning approach that advances the state of the art by incorporating reasoning about data association within planning, while considering additional sources of uncertainty. Existing belief space planning approaches typically assume that data association is given and perfect, an assumption that can be harder to justify during operation in the presence of localization uncertainty, or in ambiguous and perceptually aliased environments. By contrast, our data association aware belief space planning (DA-BSP) approach explicitly reasons about data association within belief evolution owing to candidate actions, and as such can better accommodate these challenging real-world scenarios. In particular, we show that, owing to perceptual aliasing, a posterior belief can become a mixture of probability distribution functions and design cost functions, which measure the expected level of ambiguity and posterior uncertainty given candidate action. Furthermore, we also investigate more challenging situations, such as when prior belief is multimodal and when data association aware planning is performed over several look-ahead steps. Our framework models the belief as a Gaussian mixture model. Another unique aspect of this approach is that the number of components of this Gaussian mixture model can increase as well as decrease, thereby reflecting reality more accurately. Using these and standard costs (e.g. control penalty, distance to goal) within the objective function yields a general framework that reliably represents action impact and, in particular, is capable of active disambiguation. Our approach is thus applicable to both robust perception in a passive setting with data given a priori and in an active setting, such as in autonomous navigation in perceptually aliased environments. We demonstrate key aspects of DA-BSP in a theoretical example, in a Gazebo-based realistic simulation, and also on the real robotic platform using a Pioneer robot in an office environment.

Posted in: Probability and statistics, Robot task planning , Tagged: ,