Author Archives: Juan-antonio Fernández-madrigal

Analyzing effects of loads and terrain on wheel shapes in order to reduce errors in position estimation of a mobile wheeled robot

October 10, 2019 15:09 ,

Smieszek, M., Dobrzanska, M. & Dobrzanski, P. , The impact of load on the wheel rolling radius and slip in a small mobile platform. Auton Robot (2019) 43: 2095, DOI: 10.1007/s10514-019-09857-0.

Automated guided vehicles are used in a variety of applications. Their major purpose is to replace humans in onerous, monotonous and sometimes dangerous operations. Such vehicles are controlled and navigated by application-specific software. In the case of vehicles used in multiple environments and operating conditions, such as the vehicles which are the subject of this study, a reasonable approach is required when selecting the navigation system. The vehicle may travel around an enclosed hall and around an open yard. The pavement surface may be smooth or uneven. Vehicle wheels should be flexible and facilitate the isolation and absorption of vibrations in order to reduce the effect of surface unevenness to the load. Another important factor affecting the operating conditions are changes to vehicle load resulting from the distribution of the load and the weight carried. Considering all of the factors previously mentioned, the vehicle’s navigation and control system is required to meet two opposing criteria. One of them is low price and simplicity, the other is ensuring the required accuracy when following the preset route. In the course of this study, a methodology was developed and tested which aims to obtain a satisfactory compromise between those two conflicting criteria. During the study a vehicle made in Technical University of Rzeszow was used. The results of the experimental research have been analysed. The results of the analysis provided a foundation for the development of a methodology leading to a reduction in navigation errors. Movement simulations for the proposed vehicle system demonstrated the potential for a significant reduction in the number of positioning errors.

Posted in: Mobile Robot Localization, Robot motion planning

A kind of reinforcement learning that decouples modelling from planning using Gaussian Processes for the former

October 10, 2019 15:07 ,

Rakicevic, N. & Kormushev, P., Active learning via informed search in movement parameter space for efficient robot task learning and transfer. Auton Robot (2019) 43: 1917, DOI: 10.1007/s10514-019-09842-7.

Learning complex physical tasks via trial-and-error is still challenging for high-degree-of-freedom robots. Greatest challenges are devising a suitable objective function that defines the task, and the high sample complexity of learning the task. We propose a novel active learning framework, consisting of decoupled task model and exploration components, which does not require an objective function. The task model is specific to a task and maps the parameter space, defining a trial, to the trial outcome space. The exploration component enables efficient search in the trial-parameter space to generate the subsequent most informative trials, by simultaneously exploiting all the information gained from previous trials and reducing the task model’s overall uncertainty. We analyse the performance of our framework in a simulation environment and further validate it on a challenging bimanual-robot puck-passing task. Results show that the robot successfully acquires the necessary skills after only 100 trials without any prior information about the task or target positions. Decoupling the framework’s components also enables efficient skill transfer to new environments which is validated experimentally.

Posted in: Applications of reinforcement learning to robots , Tagged:

On the formalization and conceptualization of real-time basic concepts and methods (RMS, EDF) for robots

October 10, 2019 14:39 ,

Nicolas Gobillot, Charles Lesire, David Doose, A Design and Analysis Methodology for Component-Based Real-Time Architectures of Autonomous Systems. Journal of Intelligent & Robotic Systems, October 2019, Volume 96, Issue 1, pp 123–138, DOI: 10.1007/s10846-018-0967-5.

The integration of autonomous robots in real applications is a challenge. It needs that the behaviour of these robots is proved to be safe. In this paper, we focus on the real-time software embedded on the robot, and that supports the execution of safe and autonomous behaviours. We propose a methodology that goes from the design of component-based software architectures using a Domain Specific Language, to the analysis of the real-time constraints that arise when considering the safety of software applications. This methodology is supported by a code generation toolchain that ensures that the code eventually executed on the robot is consistent with the analysis performed. This methodology is applied on a ground robot exploring an area.

Posted in: Real-Time Systems, Robotic software , Tagged: , , ,

On the effects of large variances in the transition function for Q-learning

October 10, 2019 14:29 ,

D. Lee and W. B. Powell, Bias-Corrected Q-Learning With Multistate Extension. IEEE Transactions on Automatic Control, vol. 64, no. 10, pp. 4011-4023, DOI: 10.1109/TAC.2019.2912443.

Q-learning is a sample-based model-free algorithm that solves Markov decision problems asymptotically, but in finite time, it can perform poorly when random rewards and transitions result in large variance of value estimates. We pinpoint its cause to be the estimation bias due to the maximum operator in Q-learning algorithm, and present the evidence of max-operator bias in its Q value estimates. We then present an asymptotically optimal bias-correction strategy and construct an extension to bias-corrected Q-learning algorithm to multistate Markov decision processes, with asymptotic convergence properties as strong as those from Q-learning. We report the empirical performance of the bias-corrected Q-learning algorithm with multistate extension in two model problems: A multiarmed bandit version of Roulette and an electricity storage control simulation. The bias-corrected Q-learning algorithm with multistate extension is shown to control max-operator bias effectively, where the bias-resistance can be tuned predictably by adjusting a correction parameter.

Posted in: Reinforcement learning theory , Tagged:

Interesting survey on heart-reat detection through conventional cameras

October 10, 2019 14:15 ,

X. Chen, J. Cheng, R. Song, Y. Liu, R. Ward and Z. J. Wang, Video-Based Heart Rate Measurement: Recent Advances and Future Prospects. IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 10, pp. 3600-3615, DOI: 10.1109/TIM.2018.2879706.

Heart rate (HR) estimation and monitoring is of great importance to determine a person’s physiological and mental status. Recently, it has been demonstrated that HR can be remotely retrieved from facial video-based photoplethysmographic signals captured using professional or consumer-level cameras. Many efforts have been made to improve the detection accuracy of this noncontact technique. This paper presents a timely, systematic survey on such video-based remote HR measurement approaches, with a focus on recent advancements that overcome dominating technical challenges arising from illumination variations and motion artifacts. Representative methods up to date are comparatively summarized with respect to their principles, pros, and cons under different conditions. Future prospects of this promising technique are discussed and potential research directions are described. We believe that such a remote HR measurement technique, taking advantages of unobtrusiveness while providing comfort and convenience, will be beneficial for many healthcare applications.

Posted in: Computer vision , Tagged:

Interesting review of time-to-digital converters with the state-of-the-art and applications

October 10, 2019 14:10 ,

S. Tancock, E. Arabul and N. Dahnoun, A Review of New Time-to-Digital Conversion Techniques. IEEE Transactions on Instrumentation and Measurement, vol. 68, no. 10, pp. 3406-3417, DOI: 10.1109/TIM.2019.2936717.

Time-to-digital converters (TDCs) are vital components in time and distance measurement and frequency-locking applications. There are many architectures for implementing TDCs, from simple counter TDCs to hybrid multi-level TDCs, which use many techniques in tandem. This article completes the review literature of TDCs by describing new architectures along with their benefits and tradeoffs, as well as the terminology and performance metrics that must be considered when choosing a TDC. It describes their implementation from the gate level upward and how it is affected by the fabric of the device [field-programmable gate array (FPGA) or application-specific integrated circuit (ASIC)] and suggests suitable use cases for the various techniques. Based on the results achieved in the current literature, we make recommendations on the appropriate architecture for a given task based on the number of channels and precision required, as well as the target fabric.

Posted in: Electronics, Real-Time Systems , Tagged:

Hardware efficient collision avoidance for mobile robots through the use of interval arithmetics and parallelism

September 16, 2019 16:08 ,

Pranjal Vyas, Leena Vachhani, K Sridharan, Hardware-efficient interval analysis based collision detection and avoidance for mobile robots. Mechatronics, Volume 62, 2019, DOI: 10.1016/j.mechatronics.2019.102258.

Collision detection and avoidance is challenging when the mobile robot is moving among multiple dynamic obstacles. A hardware-efficient architecture supporting parallel implementation is presented in this work for low-power, faster and reliable collision-free motion planning. An approach based on interval analysis is developed for designing an efficient hardware architecture. The proposed architecture achieves parallelism which can be combined with any robotic task involving multiple obstacles. Interval arithmetic is used for representing the pose of the robot and the obstacle as velocity intervals in a fixed time period. These intervals correspond to sub-intervals such as arcs and line-segments. In particular, the collision detection problem for dynamic objects involves the computation of line segment-arc intersections and segment-segment intersections. The intersection of these boundary curves is carried out in a hardware-efficient manner so that it avoids complex arithmetic computations such as multiplication, division etc and exploits parallelism. We develop several results on intersection of these sub-intervals for collision detection and use them to obtain a hardware-efficient collision detection algorithm that requires only shift and add-type of computations. The algorithm is further used in developing a hardware-efficient technique for finding an exhaustive set of solutions for avoiding collision of the robot with dynamic obstacles. Simulation results in MatLab and experiments with a Field Programmable Gate Array (FPGA)-based robot show that a variety of collision avoidance techniques can be implemented using the proposed solution set that guarantees collision avoidance with multiple obstacles.

Posted in: Robot motion planning , Tagged: ,

On rewards and values when the RL theory is applied to human brain

September 16, 2019 16:04 ,

Keno Juechems, Christopher Summerfield, Where Does Value Come From?. Trends in Cognitive Sciences, Volume 23, Issue 10, 2019, Pages 836-850, DOI: 10.1016/j.tics.2019.07.012.

The computational framework of reinforcement learning (RL) has allowed us to both understand biological brains and build successful artificial agents. However, in this opinion, we highlight open challenges for RL as a model of animal behaviour in natural environments. We ask how the external reward function is designed for biological systems, and how we can account for the context sensitivity of valuation. We summarise both old and new theories proposing that animals track current and desired internal states and seek to minimise the distance to a goal across multiple value dimensions. We suggest that this framework readily accounts for canonical phenomena observed in the fields of psychology, behavioural ecology, and economics, and recent findings from brain-imaging studies of value-guided decision-making.

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

On the integer numbers in the brain

September 16, 2019 16:01 ,

Susan Carey, David Barner, Ontogenetic Origins of Human Integer Representations. Trends in Cognitive Sciences, Volume 23, Issue 10, 2019, Pages 823-835, DOI: 10.1016/j.tics.2019.07.004.

Do children learn number words by associating them with perceptual magnitudes? Recent studies argue that approximate numerical magnitudes play a foundational role in the development of integer concepts. Against this, we argue that approximate number representations fail both empirically and in principle to provide the content required of integer concepts. Instead, we suggest that children\u2019s understanding of integer concepts proceeds in two phases. In the first phase, children learn small exact number word meanings by associating words with small sets. In the second phase, children learn the meanings of larger number words by mastering the logic of exact counting algorithms, which implement the successor function and Hume\u2019s principle (that one-to-one correspondence guarantees exact equality). In neither phase do approximate number representations play a foundational role.

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

On the role and limitations of motor internal simulation as a way of predicting the effects of a future action in the brain

September 16, 2019 15:46 ,

Myrthel Dogge, Ruud Custers, Henk Aarts, Moving Forward: On the Limits of Motor-Based Forward Models. Trends in Cognitive Sciences, Volume 23, Issue 9, 2019, Pages 743-753, DOI: 10.1016/j.tics.2019.06.008.

The human ability to anticipate the consequences that result from action is an essential building block for cognitive, emotional, and social functioning. A dominant view is that this faculty is based on motor predictions, in which a forward model uses a copy of the motor command to predict imminent sensory action-consequences. Although this account was originally conceived to explain the processing of action-outcomes that are tightly coupled to bodily movements, it has been increasingly extrapolated to effects beyond the body. Here, we critically evaluate this generalization and argue that, although there is ample evidence for the role of predictions in the processing of environment-related action-outcomes, there is hitherto little reason to assume that these predictions result from motor-based forward models.

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