Category Archives: Robotics

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: ,

Mapping the wifi signal for robot localization both precisely and accurately through a complex model of the signal

March 28, 2018 09:22 ,

Renato Miyagusuku, Atsushi Yamashita, Hajime Asama, Precise and accurate wireless signal strength mappings using Gaussian processes and path loss models, Robotics and Autonomous Systems, Volume 103, 2018, Pages 134-150, DOI: 10.1016/j.robot.2018.02.011.

In this work, we present a new modeling approach that generates precise (low variance) and accurate (low mean error) wireless signal strength mappings. In robot localization, these mappings are used to compute the likelihood of locations conditioned to new sensor measurements. Therefore, both mean and variance predictions are required. Gaussian processes have been successfully used for learning highly accurate mappings. However, they generalize poorly at locations far from their training inputs, making those predictions have high variance (low precision). In this work, we address this issue by incorporating path loss models, which are parametric functions that although lacking in accuracy, generalize well. Path loss models are used together with Gaussian processes to compute mean predictions and most importantly, to bound Gaussian processes’ predicted variances. Through extensive testing done with our open source framework, we demonstrate the ability of our approach to generating precise and accurate mappings, and the increased localization accuracy of Monte Carlo localization algorithms when using them; with all our datasets and software been made readily available online for the community.

Posted in: Communication networks, Mobile Robot Localization , Tagged: ,

A novel motion planning algorithm for robot navigation taking into account the robot kinematic constraints and shape

March 28, 2018 09:18 ,

Muhannad Mujahed, Dirk Fischer, Bärbel Mertsching, Admissible gap navigation: A new collision avoidance approach, Robotics and Autonomous Systems,
Volume 103, 2018, Pages 93-110, DOI: 10.1016/j.robot.2018.02.008.

This paper proposes a new concept, the Admissible Gap (AG), for reactive collision avoidance. A gap is called admissible if it is possible to find a collision-free motion control that guides a robot through it, while respecting the vehicle constraints. By utilizing this concept, a new navigation approach was developed, achieving an outstanding performance in unknown dense environments. Unlike the widely used gap-based methods, our approach directly accounts for the exact shape and kinematics, rather than finding a direction solution and turning it later into a collision-free admissible motion. The key idea is to analyze the structure of obstacles and virtually locate an admissible gap, once traversed, the robot makes progress towards the goal. For this purpose, we introduce a strategy of traversing gaps that respect the kinematic constraints and provides a compromise between path length and motion safety. We also propose a new methodology for extracting gaps that eliminates useless ones, thus reducing oscillations. Experimental results along with performance evaluation demonstrate the outstanding behavior of the proposed AG approach. Furthermore, a comparison with existing state-of-the-art methods shows that the AG approach achieves the best results in terms of efficiency, robustness, safety, and smoothness.

Posted in: Robot motion planning , Tagged: ,

A robot designed to integrate socially with a group of chickens and study their behaviour

March 28, 2018 09:10 ,

A. Gribovskiy, J. Halloy, J.L. Deneubourg, F. Mondada, Designing a socially integrated mobile robot for ethological research, Robotics and Autonomous Systems, Volume 103, 2018, Pages 42-55, DOI: 10.1016/j.robot.2018.02.003.

A robot introduced into an animal group, accepted by the animals as conspecifics, and capable of interacting with them is an efficient tool for ethological research, particularly in studies of collective and social behaviour. In this paper, we present the implementation of an autonomous mobile robot developed by the authors to study group behaviour of chicks of the domestic chicken (Gallus gallus domesticus). We discuss the design of the robot and of the experimental framework that we built to run animal–robot experiments. The robot design was experimentally validated, we demonstrated that the robot can be socially integrated into animal groups. The designed system extends the current state of the art in the field of animal–robot interaction in general and the birds study in particular by combining such advantages as (1) the robot being a part of the group, (2) the possibility of mixed multi-robot, multi-animal groups, and (3) close-loop control of robots. It opens new opportunities in the study of behaviour in domestic fowl by using mobile robots; being socially integrated into the animal group, robots can profit from the positive feedback mechanism that plays key roles in animal collective behaviour. They have potential applications in various domains, from pure scientific research to applied areas such as control and ensuring welfare of poultry.

Posted in: Robot applications , Tagged: ,

A robotic wheelchair navigation algorithm that plans paths taking into account the discomfort of the user

March 28, 2018 09:07 ,

Yoichi Morales, Atsushi Watanabe, Florent Ferreri, Jani Even, Kazuhiro Shinozawa, Norihiro Hagita, Passenger discomfort map for autonomous navigation in a robotic wheelchair, Robotics and Autonomous Systems, Volume 103, 2018, Pages 13-26, DOI: 10.1016/j.robot.2018.02.002.

This work presents a navigational approach that takes into consideration the perception of comfort by a human passenger. Comfort is the state of being at ease and free from stress; thus, comfortable navigation is a ride that, in addition to being safe, is perceived by the passenger as being free from anxiety and stress. This study considers how to compute passenger comfortable paths. To compute such paths, passenger discomfort is studied in locations with good visibility and those with no visibility. In locations with good visibility, passenger preference to ride in the road is studied. For locations with non-visible areas, the relationship between passenger visibility and discomfort is studied. Autonomous-navigation experiments are performed to build a map of human discomfort that is used to compute global paths. A path planner is proposed that minimizes a three-variable cost function: location discomfort cost, area visibility cost, and path length cost. Planner parameters are calibrated toward a composite trajectory histogram built with data taken from participant self-driving trajectories. Finally, autonomous navigation experiments with 30 participants show that the proposed approach is rated as more comfortable than the state-of-the-art shortest planner approach.

Posted in: Robot motion planning , Tagged: ,