Category Archives: Robotics

A framework to manage and switch between several sensor modalities in tele-operation

Andrea Cherubini, Robin Passama, Philippe Fraisse, André Crosnier, A unified multimodal control framework for human–robot interaction, Robotics and Autonomous Systems, Volume 70, August 2015, Pages 106-115, ISSN 0921-8890, DOI: 10.1016/j.robot.2015.03.002.

In human–robot interaction, the robot controller must reactively adapt to sudden changes in the environment (due to unpredictable human behaviour). This often requires operating different modes, and managing sudden signal changes from heterogeneous sensor data. In this paper, we present a multimodal sensor-based controller, enabling a robot to adapt to changes in the sensor signals (here, changes in the human collaborator behaviour). Our controller is based on a unified task formalism, and in contrast with classical hybrid visicn–force–position control, it enables smooth transitions and weighted combinations of the sensor tasks. The approach is validated in a mock-up industrial scenario, where pose, vision (from both traditional camera and Kinect), and force tasks must be realized either exclusively or simultaneously, for human–robot collaboration.

Robot kidnapping detection based on support vector machines

Dylan Campbell, Mark Whitty, Metric-based detection of robot kidnapping with an SVM classifier, Robotics and Autonomous Systems, Volume 69, July 2015, Pages 40-51, ISSN 0921-8890, DOI: 10.1016/j.robot.2014.08.004.

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.

A nice SLAM approach based on hybrid Normal Distribution Transform (NDT) + occupancy grid maps intended for long term operation in dynamic environments

Erik Einhorn, Horst-Michael Gross, Generic NDT mapping in dynamic environments and its application for lifelong SLAM, Robotics and Autonomous Systems, Volume 69, July 2015, Pages 28-39, ISSN 0921-8890, DOI: 10.1016/j.robot.2014.08.008.

In this paper, we present a new, generic approach for Simultaneous Localization and Mapping (SLAM). First of all, we propose an abstraction of the underlying sensor data using Normal Distribution Transform (NDT) maps that are suitable for making our approach independent from the used sensor and the dimension of the generated maps. We present several modifications for the original NDT mapping to handle free-space measurements explicitly. We additionally describe a method to detect and handle dynamic objects such as moving persons. This enables the usage of the proposed approach in highly dynamic environments. In the second part of this paper we describe our graph-based SLAM approach that is designed for lifelong usage. Therefore, the memory and computational complexity is limited by pruning the pose graph in an appropriate way.

Interesting paper on fault tolerance applied to robotics, with good survey of the subject

D. Crestani, K. Godary-Dejean, L. Lapierre, Enhancing fault tolerance of autonomous mobile robots, Robotics and Autonomous Systems, Volume 68, June 2015, Pages 140-155, ISSN 0921-8890, DOI: 10.1016/j.robot.2014.12.015.

Experience demonstrates that autonomous mobile robots running in the field in a dynamic environment often breakdown. Generally, mobile robots are not designed to efficiently manage faulty or unforeseen situations. Even if some research studies exist, there is a lack of a global approach that really integrates dependability and particularly fault tolerance into the mobile robot design.
This paper presents an approach that aims to integrate fault tolerance principles into the design of a robot real-time control architecture. A failure mode analysis is firstly conducted to identify and characterize the most relevant faults. Then the fault detection and diagnosis mechanisms are explained. Fault detection is based on dedicated software components scanning faulty behaviors. Diagnosis is based on the residual principle and signature analysis to identify faulty software or hardware components and faulty behaviors. Finally, the recovery mechanism, based on the modality principle, proposes to adapt the robot’s control loop according to the context and current operational functions of the robot.
This approach has been applied and implemented in the control architecture of a Pioneer 3DX mobile robot.

Novelty detection as a way for enhancing learning capabilities of a robot, and a brief but interesting survey of motivational theories and their difference with attention

Y. Gatsoulis, T.M. McGinnity, Intrinsically motivated learning systems based on biologically-inspired novelty detection, Robotics and Autonomous Systems, Volume 68, June 2015, Pages 12-20, ISSN 0921-8890, DOI: 10.1016/j.robot.2015.02.006.

Intrinsic motivations play an important role in human learning, particularly in the early stages of childhood development, and ideas from this research field have influenced robotic learning and adaptability. In this paper we investigate one specific type of intrinsic motivation, that of novelty detection and we discuss the reasons that make it a powerful facility for continuous learning. We formulate and present one original type of biologically inspired novelty detection architecture and implement it on a robotic system engaged in a perceptual classification task. The results of real-world robot experiments we conducted show how this original architecture conforms to behavioural observations and demonstrate its effectiveness in terms of focusing the system’s attention in areas that are potential for effective learning.

On the history of IEEE Transactions on Robotics and Automation, ICRA, and others

Sabanovic, S.; Milojevic, S.; Asaro, P.; Francisco, M., Robotics Narratives and Networks [History], Robotics & Automation Magazine, IEEE , vol.22, no.1, pp.137,146, March 2015, DOI: 10.1109/MRA.2014.2385564.

Somewhere around 1983, maybe late 1982, there was talk beginning about doing something more formal within IEEE that dealt with robotics and automation. Informally, activity was getting started through the Control Society,…also Systems, Man and Cybernetics, which obviously makes a lot of sense with the telerobotics things and a few others. But we wanted to build a more permanent home for it, so there was one of the first meetings. George Saridis chaired the meeting. I know George Bekey was there, Tony Bejczy, Lou Paul, probably another half dozen people.

Abstract data-type for exchanging information in real-time systems, prioritizing the access to newest data rather than to oldest

Dantam, N.T.; Lofaro, D.M.; Hereid, A.; Oh, P.Y.; Ames, A.D.; Stilman, M., The Ach Library: A New Framework for Real-Time Communication, Robotics & Automation Magazine, IEEE , vol.22, no.1, pp.76,85, March 2015, DOI: 10.1109/MRA.2014.2356937.

Correct real-time software is vital for robots in safety-critical roles such as service and disaster response. These systems depend on software for locomotion, navigation, manipulation, and even seemingly innocuous tasks such as safely regulating battery voltage. A multiprocess software design increases robustness by isolating errors to a single process, allowing the rest of the system to continue operation. This approach also assists with modularity and concurrency. For real-time tasks, such as dynamic balance and force control of manipulators, it is critical to communicate the latest data sample with minimum latency. There are many communication approaches intended for both general-purpose and real-time needs [9], [13], [15], [17], [19]. Typical methods focus on reliable communication or network transparency and accept a tradeoff of increased message latency or the potential to discard newer data. By focusing instead on the specific case of real-time communication on a single host, we reduce communication latency and guarantee access to the latest sample. We present a new interprocess communication (IPC) library, Ach which addresses this need, and discuss its application for real-time multiprocess control on three humanoid robots (Figure 1). (Ach is available at http://www.golems.org/projects/ach.html. The name Ach comes from the common abbreviation for the motor neurotransmitter Acetylcholine and the computer networking term ACK.).

Reinforcement learning used for an adaptive attention mechanism, and integrated in an architecture with both top-down and bottom-up vision processing

Ognibene, D.; Baldassare, G., Ecological Active Vision: Four Bioinspired Principles to Integrate Bottom–Up and Adaptive Top–Down Attention Tested With a Simple Camera-Arm Robot, Autonomous Mental Development, IEEE Transactions on , vol.7, no.1, pp.3,25, March 2015. DOI: 10.1109/TAMD.2014.2341351.

Vision gives primates a wealth of information useful to manipulate the environment, but at the same time it can easily overwhelm their computational resources. Active vision is a key solution found by nature to solve this problem: a limited fovea actively displaced in space to collect only relevant information. Here we highlight that in ecological conditions this solution encounters four problems: 1) the agent needs to learn where to look based on its goals; 2) manipulation causes learning feedback in areas of space possibly outside the attention focus; 3) good visual actions are needed to guide manipulation actions, but only these can generate learning feedback; and 4) a limited fovea causes aliasing problems. We then propose a computational architecture (“BITPIC”) to overcome the four problems, integrating four bioinspired key ingredients: 1) reinforcement-learning fovea-based top-down attention; 2) a strong vision-manipulation coupling; 3) bottom-up periphery-based attention; and 4) a novel action-oriented memory. The system is tested with a simple simulated camera-arm robot solving a class of search-and-reach tasks involving color-blob “objects.” The results show that the architecture solves the problems, and hence the tasks, very efficiently, and highlight how the architecture principles can contribute to a full exploitation of the advantages of active vision in ecological conditions.

Automatic synthetis of controllers for robotic tasks from the specification of state-machine-like missions, nonlinear models of the robot and a representation of the robot workspace

Jonathan A. DeCastro and Hadas Kress-Gazit, 2015, Synthesis of nonlinear continuous controllers for verifiably correct high-level, reactive behaviors, The International Journal of Robotics Research, 34: 378-394, DOI: 10.1177/0278364914557736.

Planning robotic missions in environments shared by humans involves designing controllers that are reactive to the environment yet able to fulfill a complex high-level task. This paper introduces a new method for designing low-level controllers for nonlinear robotic platforms based on a discrete-state high-level controller encoding the behaviors of a reactive task specification. We build our method upon a new type of trajectory constraint which we introduce in this paper, reactive composition, to provide the guarantee that any high-level reactive behavior may be fulfilled at any moment during the continuous execution. We generate pre-computed motion controllers in a piecewise manner by adopting a sample-based synthesis method that associates a certificate of invariance with each controller in the sample set. As a demonstration of our approach, we simulate different robotic platforms executing complex tasks in a variety of environments.

A nice review of the problem of kinematic modeling of wheeled mobile robots and a new approach that delays the use of coordinate frames

Alonzo Kelly and Neal Seegmiller, 2015, Recursive kinematic propagation for wheeled mobile robots, The International Journal of Robotics Research, 34: 288-313, DOI: 10.1177/0278364914551773.

The problem of wheeled mobile robot kinematics is formulated using the transport theorem of vector algebra. Doing so postpones the introduction of coordinates until after the expressions for the relevant Jacobians have been derived. This approach simplifies the derivation while also providing the solution to the general case in 3D, including motion over rolling terrain. Angular velocity remains explicit rather than encoded as the time derivative of a rotation matrix. The equations are derived and can be implemented recursively using a single equation that applies to all cases. Acceleration kinematics are uniquely derivable in reasonable effort. The recursive formulation also leads to efficient computer implementations that reflect the modularity of real mechanisms.