A related work with a nice taxonomy of robot navigation algorithms

June 17, 2019 12:18 ,

Eduardo J. Molinos, Ángel Llamazares, Manuel Ocaña Dynamic window based approaches for avoiding obstacles in moving, Robotics and Autonomous Systems,
Volume 118, 2019, Pages 112-130 DOI: 10.1016/j.robot.2019.05.003.

In recent years, Unmanned Ground Vehicles (UGVs) have been widely used as service robots. Unlike industrial robots, which are situated in fixed and controlled positions, UGVs work in dynamic environments, sharing the environment with other vehicles and humans. These robots should be able to move without colliding with any obstacle, assuring its integrity and the environment safety. In this paper, we propose two adaptations of the classical Dynamic Window algorithm for dealing with dynamic obstacles like Dynamic Window for Dynamic Obstacles (DW4DO) and Dynamic Window for Dynamic Obstacles Tree (DW4DOT). These new algorithms are compared with our previous algorithms based on Curvature Velocity Methods: Predicted Curvature Velocity Method (PCVM) and Dynamic Curvature Velocity Method (DCVM). Proposals have been validated in both simulated and real environment using several robotic platforms.

Posted in: Robot motion planning , Tagged: ,

A Survey of Knowledge Representation in Service Robotics

June 17, 2019 12:14 ,

avid Paulius, Yu Sun, A Survey of Knowledge Representation in Service Robotics,Robotics and Autonomous Systems, Volume 118, 2019, Pages 13-30 DOI: 10.1016/j.robot.2019.03.005.

Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots. The task of robot learning and problem-solving is very broad, as it integrates a variety of tasks such as object detection, activity recognition, task/motion planning, localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques. In this paper, we solely focus on knowledge representations and notably how knowledge is typically gathered, represented, and reproduced to solve problems as done by researchers in the past decades. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models that have extensively been introduced and studied in recent years, such as machine learning, deep learning, probabilistic modeling, and semantic graphical structures. Along with an overview of such tools, we discuss the problems which have existed in robot learning and how they have been built and used as solutions, technologies or developments (if any) which have contributed to solving them. Finally, we discuss key principles that should be considered when designing an effective knowledge representation.

Posted in: Artificial Intelligence , Tagged: ,

A novel path planning method for both global and local planning with provable behavior, and a nice survey of existing navigation methods

May 24, 2019 11:06 ,

Sgorbissa, A., Integrated robot planning, path following, and obstacle avoidance in two and three dimensions: wheeled robots, underwater vehicles, and multicopters, The International Journal of Robotics Research, DOI: 10.1177/0278364919846910.

We propose an innovative, integrated solution to path planning, path following, and obstacle avoidance that is suitable both for 2D and 3D navigation. The proposed method takes as input a generic curve connecting a start and a goal position, and is able to find a corresponding path from start to goal in a maze-like environment even in the absence of global information, it guarantees convergence to the path with kinematic control, and finally avoids locally sensed obstacles without becoming trapped in deadlocks. This is achieved by computing a closed-form expression in which the control variables are a continuous function of the input curve, the robot’s state, and the distance of all the locally sensed obstacles. Specifically, we introduce a novel formalism for describing the path in two and three dimensions, as well as a computationally efficient method for path deformation (based only on local sensor readings) that is able to find a path to the goal even when such path cannot be produced through continuous deformations of the original. The article provides formal proofs of all the properties above, as well as simulated results in a simulated environment with a wheeled robot, an underwater vehicle, and a multicopter.

Posted in: Robot motion planning , Tagged: ,

Grid maps with confidence levels

May 24, 2019 11:03 ,

Agha-mohammadi, A., Heiden, E., Hausman, K., & Sukhatme, G., Confidence-rich grid mapping, The International Journal of Robotics Research, DOI: 10.1177/0278364919839762.

Representing the environment is a fundamental task in enabling robots to act autonomously in unknown environments. In this work, we present confidence-rich mapping (CRM), a new algorithm for spatial grid-based mapping of the 3D environment. CRM augments the occupancy level at each voxel by its confidence value. By explicitly storing and evolving confidence values using the CRM filter, CRM extends traditional grid mapping in three ways: first, it partially maintains the probabilistic dependence among voxels; second, it relaxes the need for hand-engineering an inverse sensor model and proposes the concept of sensor cause model that can be derived in a principled manner from the forward sensor model; third, and most importantly, it provides consistent confidence values over the occupancy estimation that can be reliably used in collision risk evaluation and motion planning. CRM runs online and enables mapping environments where voxels might be partially occupied. We demonstrate the performance of the method on various datasets and environments in simulation and on physical systems. We show in real-world experiments that, in addition to achieving maps that are more accurate than traditional methods, the proposed filtering scheme demonstrates a much higher level of consistency between its error and the reported confidence, hence, enabling a more reliable collision risk evaluation for motion planning.

Posted in: Mobile robot mapping , Tagged: ,

The concepts of agency and ownership in cognitive maps, and a nice survey of cognitive maps

May 24, 2019 11:00 ,

Shahar Arzy, Daniel L. Schacter, Self-Agency and Self-Ownership in Cognitive Mapping, Trends in Cognitive Sciences, Volume 23, Issue 6, 2019, Pages 476-487, DOI: 10.1016/j.tics.2019.04.003.

The concepts of agency of one’s actions and ownership of one’s experience have proved useful in relating body representations to bodily consciousness. Here we apply these concepts to cognitive maps. Agency is defined as ‘the sense that I am the one who is generating the experience represented on a cognitive map’, while ownership is defined as ‘the sense that I am the one who is undergoing an experience, represented on a cognitive map’. The roles of agency and ownership are examined with respect to the transformation between egocentric and allocentric representations and the underlying neurocognitive and computational mechanisms; and within the neuropsychiatric domain, including Alzheimer’s disease (AD) and other memory-related disorders, in which the senses of agency and ownership may be disrupted.

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

An orientation sensor for robot navigation that uses the sky

May 17, 2019 11:31 ,

Julien Dupeyroux, Stéphane Viollet, Julien R. Serres, An ant-inspired celestial compass applied to autonomous outdoor robot navigation, Robotics and Autonomous Systems, Volume 117, 2019, Pages 40-56, DOI: 10.1016/j.robot.2019.04.007.

Desert ants use the polarization of skylight and a combination of stride and ventral optic flow integration processes to track the nest and food positions when traveling, achieving outstanding performances. Navigation sensors such as global positioning systems and inertial measurement units still have disadvantages such as their low resolution and drift. Taking our inspiration from ants, we developed a 2-pixel celestial compass which computes the heading angle of a mobile robot in the ultraviolet range. The output signals obtained with this optical compass were investigated under various weather and ultraviolet conditions and compared with those obtained on a magnetometer in the vicinity of our laboratory. After being embedded on-board the robot, the sensor was first used to compensate for random yaw disturbances. We then used the compass to keep the Hexabot robot’s heading angle constant in a straight forward walking task over a flat terrain while its walking movements were imposing yaw disturbances. Experiments performed under various meteorological conditions showed the occurrence of steady state heading angle errors ranging from 0.3∘ (with a clear sky) to 2.9∘ (under changeable sky conditions). The compass was also tested under canopies and showed a strong ability to determine the robot’s heading while most of the sky was hidden by the foliage. Lastly, a waterproof, mono-pixel version of the sensor was designed and successfully tested in a preliminary underwater benchmark test. These results suggest this new optical compass shows great precision and reliability in a wide range of outdoor conditions, which makes it highly suitable for autonomous robotic outdoor navigation tasks. A celestial compass and a minimalistic optic flow sensor called M2APix (based on Michaelis–Menten Auto-adaptive Pixels) were therefore embedded on-board our latest insectoid robot called AntBot, to complete the previously mentioned ant-like homing navigation processes. First the robot was displaced manually and made to return to its starting-point on the basis of its absolute knowledge of the coordinates of this point. Lastly, AntBot was tested in fully autonomous navigation experiments, in which it explored its environment and then returned to base using the same sensory modes as those on which desert ants rely. AntBot produced robust, precise localization performances with a homing error as small as 0.7% of the entire trajectory.

Posted in: Mobile Robot Localization , Tagged:

Human interaction with the RL process

May 17, 2019 11:23 ,

Celemin, C., Ruiz-del-Solar, J. & Kober, A fast hybrid reinforcement learning framework with human corrective feedback, Auton Robot (2019) 43: 1173, DOI: 10.1007/s10514-018-9786-6.

Reinforcement Learning agents can be supported by feedback from human teachers in the learning loop that guides the learning process. In this work we propose two hybrid strategies of Policy Search Reinforcement Learning and Interactive Machine Learning that benefit from both sources of information, the cost function and the human corrective feedback, for accelerating the convergence and improving the final performance of the learning process. Experiments with simulated and real systems of balancing tasks and a 3 DoF robot arm validate the advantages of the proposed learning strategies: (i) they speed up the convergence of the learning process between 3 and 30 times, saving considerable time during the agent adaptation, and (ii) they allow including non-expert feedback because they have low sensibility to erroneous human advice.

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

Aligning maps of different modalities, coverage and scale

May 17, 2019 11:20 ,

Gholami Shahbandi, S. & Magnusson M., 2D map alignment with region decomposition, Auton Robot (2019) 43: 1117, DOI: 10.1007/s10514-018-9785-7.

In many applications of autonomous mobile robots the following problem is encountered. Two maps of the same environment are available, one a prior map and the other a sensor map built by the robot. To benefit from all available information in both maps, the robot must find the correct alignment between the two maps. There exist many approaches to address this challenge, however, most of the previous methods rely on assumptions such as similar modalities of the maps, same scale, or existence of an initial guess for the alignment. In this work we propose a decomposition-based method for 2D spatial map alignment which does not rely on those assumptions. Our proposed method is validated and compared with other approaches, including generic data association approaches and map alignment algorithms. Real world examples of four different environments with thirty six sensor maps and four layout maps are used for this analysis. The maps, along with an implementation of the method, are made publicly available online.

Posted in: Mobile robot mapping , Tagged:

A nice (short) survey of deep RL

May 10, 2019 11:43 ,

Matthew Botvinick, Sam Ritter, Jane X. Wang, Zeb Kurth-Nelson, Charles Blundell, Demis Hassabis, Reinforcement Learning, Fast and Slow, Trends in Cognitive Sciences, Volume 23, Issue 5, 2019, Pages 408-422 DOI: 10.1016/j.tics.2019.02.006.

Deep reinforcement learning (RL) methods have driven impressive advances in artificial intelligence in recent years, exceeding human performance in domains ranging from Atari to Go to no-limit poker. This progress has drawn the attention of cognitive scientists interested in understanding human learning. However, the concern has been raised that deep RL may be too sample-inefficient – that is, it may simply be too slow – to provide a plausible model of how humans learn. In the present review, we counter this critique by describing recently developed techniques that allow deep RL to operate more nimbly, solving problems much more quickly than previous methods. Although these techniques were developed in an AI context, we propose that they may have rich implications for psychology and neuroscience. A key insight, arising from these AI methods, concerns the fundamental connection between fast RL and slower, more incremental forms of learning.

Posted in: Psycho-physiological bases of engineering, Reinforcement learning theory , Tagged: ,

Measuring unconscious behaviour

May 10, 2019 11:39 ,

David Soto, Usman Ayub Sheikh, Clive R. Rosenthal, A Novel Framework for Unconscious Processing, Trends in Cognitive Sciences, Volume 23, Issue 5, 2019, Pages 372-376 DOI: 10.1016/j.tics.2019.03.002.

Understanding the distinction between conscious and unconscious cognition remains a priority in psychology and neuroscience. A comprehensive neurocognitive account of conscious awareness will not be possible without a sound framework to isolate and understand unconscious information processing. Here, we provide a brain-based framework that allows the identification of unconscious processes, even with null effects on behaviour.

Posted in: Cognitive sciences, Psycho-physiological bases of engineering