Category Archives: Robotics

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:

Designing robotic architectures by coordinating different modules in a data-flow graphical paradigm

April 8, 2019 16:29 ,

Sebastian Buck, Andreas Zell, CS::APEX: A Framework for Algorithm Prototyping and Experimentation with Robotic Systems. Modeling Perception and High Level Robot Control with Activity Flow Graphs, Journal of Intelligent & Robotic Systems (2019) 94:371–387, DOI: 10.1007/s10846-018-0831-7.

Robotic systems differ drastically in their sensory capabilities, their computational power and their designated tasks. For
efficient algorithm development, however, we need to have a common modeling framework that enables us to generalize and
re-use existing solutions. A modular approach, which is coherent across different platforms, also allows faster prototyping
of new systems, given that existing functionality can be reused from already implemented modules. In this paper we develop
a modeling framework based on data flow graphs that achieves the following goal: We first merge synchronous data flow
and reactive programming into hybrid flow graphs, where we explicitly model synchronous and asynchronous data flow.
Then we transfer concepts from finite-state machines to achieve a coherent framework which we call Activity Flow Graphs.
The flow of activity enables us to model high level states directly in the data flow graph. The result is a single computation
graph that can express both perception and high level control aspects of any robotic system. This theoretical foundation is
the core of our open-source software framework CS::APEX, which allows the creation, manipulation and evaluation of
Activity Flow Graphs and enables rapid prototyping and experimentation and can be used with any robot supporting the
Robot Operating System (ROS). We then demonstrate the framework with two high level models for a fetch-and-delivery
robot and a person following robot.

Posted in: Robotic architectures, Robotic software , Tagged: ,

Predicting the structure of indoor environments for mobile robots

March 27, 2019 09:50 ,

Matteo Luperto, Francesco Amigoni, Predicting the global structure of indoor environments: A constructive machine learning approach, Autonomous Robots, April 2019, Volume 43, Issue 4, pp 813–835, DOI: 10.1007/s10514-018-9732-7.

Consider a mobile robot exploring an initially unknown school building and assume that it has already discovered some corridors, classrooms, offices, and bathrooms. What can the robot infer about the presence and the locations of other classrooms and offices and, more generally, about the structure of the rest of the building? This paper presents a system that makes a step towards providing an answer to the above question. The proposed system is based on a generative model that is able to represent the topological structures and the semantic labeling schemas of buildings and to generate plausible hypotheses for unvisited portions of these environments. We represent the buildings as undirected graphs, whose nodes are rooms and edges are physical connections between them. Given an initial knowledge base of graphs, our approach, relying on constructive machine learning techniques, segments each graph for finding significant subgraphs and clusters them according to their similarity, which is measured using graph kernels. A graph representing a new building or an unvisited part of a building is eventually generated by sampling subgraphs from clusters and connecting them.

Posted in: Mobile robot mapping , Tagged: ,

On the definition of “action” in robotics and other fields

March 20, 2019 10:31 ,

Philipp Zech Erwan Renaudo, Simon Haller, Xiang Zhang, Justus Piater, Action representations in robotics: A taxonomy and systematic classification, The International Journal of Robotics Research, 2019, DOI: 10.1177/0278364919835020.

Understanding and defining the meaning of “action” is substantial for robotics research. This becomes utterly evident when aiming at equipping autonomous robots with robust manipulation skills for action execution. Unfortunately, to this day we still lack both a clear understanding of the concept of an action and a set of established criteria that ultimately characterize an action. In this survey, we thus first review existing ideas and theories on the notion and meaning of action. Subsequently, we discuss the role of action in robotics and attempt to give a seminal definition of action in accordance with its use in robotics research. Given this definition we then introduce a taxonomy for categorizing action representations in robotics along various dimensions. Finally, we provide a meticulous literature survey on action representations in robotics where we categorize relevant literature along our taxonomy. After discussing the current state of the art we conclude with an outlook towards promising research directions.

Posted in: Robotics , Tagged: ,

Improving Q-learning by initialization of the Q matrix and a nice related work of that approach

March 15, 2019 11:28 ,

Ee Soong Low, Pauline Ong, Kah Chun Cheah, Solving the optimal path planning of a mobile robot using improved Q-learning, Robotics and Autonomous Systems, Volume 115, 2019, Pages 143-161, DOI: 10.1016/j.robot.2019.02.013.

Q-learning, a type of reinforcement learning, has gained increasing popularity in autonomous mobile robot path planning recently, due to its self-learning ability without requiring a priori model of the environment. Yet, despite such advantage, Q-learning exhibits slow convergence to the optimal solution. In order to address this limitation, the concept of partially guided Q-learning is introduced wherein, the flower pollination algorithm (FPA) is utilized to improve the initialization of Q-learning. Experimental evaluation of the proposed improved Q-learning under the challenging environment with a different layout of obstacles shows that the convergence of Q-learning can be accelerated when Q-values are initialized appropriately using the FPA. Additionally, the effectiveness of the proposed algorithm is validated in a real-world experiment using a three-wheeled mobile robot.

Posted in: Applications of reinforcement learning to robots

A MATLAB toolbox for controlling and programming KUKA robots and a list of robotics toolboxes

March 15, 2019 10:59 ,

M. Safeea and P. Neto, KUKA Sunrise Toolbox: Interfacing Collaborative Robots With MATLAB, IEEE Robotics & Automation Magazine, vol. 26, no. 1, pp. 91-96, 2019 DOI: 10.1109/MRA.2018.2877776.

Collaborative robots are increasingly present in our lives. The KUKA LBR iiwa, equipped with the KUKA Sunrise.OS controller, is one example of a collaborative/sensitive robot. This tutorial presents the KUKA Sunrise Toolbox (KST), a MATLAB toolbox that interfaces with KUKA Sunrise.OS. KST contains functionalities for networking, soft control in real time, point-to-point motion, parameter setters/getters, general purpose, and physical interaction. It includes approximately 100 functions and runs on a remote computer connected with the KUKA Sunrise controller via Transmission Control Protocol/Internet Protocol (TCP/IP). The potentialities of the KST are demonstrated in nine application examples.

Posted in: Education, Industrial robots , Tagged:

A new pose-graph optimization algorithm for SLAM and other problems whose, through a formulation as global optimization in SE(3), results are certifiable and more robust than standard approaches, and a curious relation between this problem and the clock synchronization problem

March 6, 2019 17:53 ,

Rosen, D. M., Carlone, L., Bandeira, A. S., & Leonard, J. J., SE-Sync: A certifiably correct algorithm for synchronization over the special Euclidean group, The International Journal of Robotics Research, 38(2–3), 95–125, 2019 DOI: 10.1177/0278364918784361.

Many important geometric estimation problems naturally take the form of synchronization over the special Euclidean group: estimate the values of a set of unknown group elements x1,…,xn∈SE(d) given noisy measurements of a subset of their pairwise relative transforms x−1ixj. Examples of this class include the foundational problems of pose-graph simultaneous localization and mapping (SLAM) (in robotics), camera motion estimation (in computer vision), and sensor network localization (in distributed sensing), among others. This inference problem is typically formulated as a non-convex maximum-likelihood estimation that is computationally hard to solve in general. Nevertheless, in this paper we present an algorithm that is able to efficiently recover certifiably globally optimal solutions of the special Euclidean synchronization problem in a non-adversarial noise regime. The crux of our approach is the development of a semidefinite relaxation of the maximum-likelihood estimation (MLE) whose minimizer provides an exact maximum-likelihood estimate so long as the magnitude of the noise corrupting the available measurements falls below a certain critical threshold; furthermore, whenever exactness obtains, it is possible to verify this fact a posteriori, thereby certifying the optimality of the recovered estimate. We develop a specialized optimization scheme for solving large-scale instances of this semidefinite relaxation by exploiting its low-rank, geometric, and graph-theoretic structure to reduce it to an equivalent optimization problem defined on a low-dimensional Riemannian manifold, and then design a Riemannian truncated-Newton trust-region method to solve this reduction efficiently. Finally, we combine this fast optimization approach with a simple rounding procedure to produce our algorithm, SE-Sync. Experimental evaluation on a variety of simulated and real-world pose-graph SLAM datasets shows that SE-Sync is capable of recovering certifiably globally optimal solutions when the available measurements are corrupted by noise up to an order of magnitude greater than that typically encountered in robotics and computer vision applications, and does so significantly faster than the Gauss–Newton-based approach that forms the basis of current state-of-the-art techniques.

Posted in: Mobile robot SLAM , Tagged: , ,

Improving on-line Monte Carlo POMDP (DESTOP in particular) in discrete spaces through the use of importance sampling, and a nice summary of the problem and of current on-line POMDP approaches

March 6, 2019 16:36 ,

Luo, Y., Bai, H., Hsu, D., & Lee, W. S., Importance sampling for online planning under uncertainty, The International Journal of Robotics Research, 38(2–3), 162–181, 2019 DOI: 10.1177/0278364918780322.

The partially observable Markov decision process (POMDP) provides a principled general framework for robot planning under uncertainty. Leveraging the idea of Monte Carlo sampling, recent POMDP planning algorithms have scaled up to various challenging robotic tasks, including, real-time online planning for autonomous vehicles. To further improve online planning performance, this paper presents IS-DESPOT, which introduces importance sampling to DESPOT, a state-of-the-art sampling-based POMDP algorithm for planning under uncertainty. Importance sampling improves DESPOT’s performance when there are critical, but rare events, which are difficult to sample. We prove that IS-DESPOT retains the theoretical guarantee of DESPOT. We demonstrate empirically that importance sampling significantly improves the performance of online POMDP planning for suitable tasks. We also present a general method for learning the importance sampling distribution.

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

On the use of ubiquous supercomputing for robotics

March 4, 2019 10:33 ,

Leonardo Camargo-Forero, Pablo Royo, Xavier Prats, The ARCHADE: Ubiquitous Supercomputing for robotics. Part I: Philosophy, Robotics and Autonomous Systems, Volume 114, 2019, Pages 187-198 DOI: 10.1016/j.robot.2019.01.006.

In this work, we introduce Ubiquitous Supercomputing for robotics with the objective of opening our imagination to the development of new powerful heterogeneous multi-robot systems able to perform all kind of missions. Supercomputing, also known as High Performance computing (HPC) is the tool that allows us to predict the weather, understand the origins of the universe, create incredibly realistic fantasy movies, send personalized advertisement to millions of users worldwide and much more. Robotics has been mostly absent in its use of HPC but some previous works have lightly flirted with it. With the findings presented in here, we propose a ubiquitous supercomputing ontology, which allows describing systems made up of robots, traditional HPC infrastructures, sensors, actuators and people and exhibiting scalability, user-transparency and ultimately higher computing efficiency. Moreover, we present a technology called The ARCHADE, which facilitates the development, implementation and operation of such systems, and we propose a mechanism to define and automatize missions carried out by ubiquitous supercomputing systems. As a proof of concept, we present a system depicted as Tigers VS Hunters, which illustrates the potential of this technology. The results presented in here are part of a two series work introducing The ARCHADE. This first delivery presents its philosophy and main features. Correspondingly the second part will present a set of use cases and a complete performance benchmark. Supercomputing is part of our lives and it can be found in many research and industrial endeavors. With the ubiquitous supercomputing ontology and The ARCHADE, supercomputing will become part of robotics as well, bringing it therefore everywhere.

Posted in: Robotic software , Tagged: ,

RL and Inverse RL based on MDPs for autonomous vehicles, and a nice historical review of the topic of a.v.

March 4, 2019 10:25 ,

Changxi You, Jianbo Lu, Dimitar Filev, Panagiotis Tsiotras, Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning, Robotics and Autonomous Systems, Volume 114, 2019, Pages 1-18 DOI: 10.1016/j.robot.2019.01.003.

Autonomous vehicles promise to improve traffic safety while, at the same time, increase fuel efficiency and reduce congestion. They represent the main trend in future intelligent transportation systems. This paper concentrates on the planning problem of autonomous vehicles in traffic. We model the interaction between the autonomous vehicle and the environment as a stochastic Markov decision process (MDP) and consider the driving style of an expert driver as the target to be learned. The road geometry is taken into consideration in the MDP model in order to incorporate more diverse driving styles. The desired, expert-like driving behavior of the autonomous vehicle is obtained as follows: First, we design the reward function of the corresponding MDP and determine the optimal driving strategy for the autonomous vehicle using reinforcement learning techniques. Second, we collect a number of demonstrations from an expert driver and learn the optimal driving strategy based on data using inverse reinforcement learning. The unknown reward function of the expert driver is approximated using a deep neural-network (DNN). We clarify and validate the application of the maximum entropy principle (MEP) to learn the DNN reward function, and provide the necessary derivations for using the maximum entropy principle to learn a parameterized feature (reward) function. Simulated results demonstrate the desired driving behaviors of an autonomous vehicle using both the reinforcement learning and inverse reinforcement learning techniques.

Posted in: Applications of reinforcement learning to robots, Robot motion planning , Tagged: , ,