Category Archives: Robotics

An excellent survey of metrical SLAM (and of map representations and other issues related to SLAM) as of 2016

June 21, 2017 11:13 ,

C. Cadena et al., “Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age,” in IEEE Transactions on Robotics, vol. 32, no. 6, pp. 1309-1332, Dec. 2016. DOI: 10.1109/TRO.2016.2624754.

Simultaneous localization and mapping (SLAM) consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications and witnessing a steady transition of this technology to industry. We survey the current state of SLAM and consider future directions. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors’ take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?

Posted in: Mobile robot SLAM , Tagged: , ,

A novel particle filter algorithm with an adaptive number of particles, and a curious and interesting table I about the pros and cons of different sensors

June 20, 2017 10:46 ,

T. de J. Mateo Sanguino and F. Ponce Gómez, “Toward Simple Strategy for Optimal Tracking and Localization of Robots With Adaptive Particle Filtering,” in IEEE/ASME Transactions on Mechatronics, vol. 21, no. 6, pp. 2793-2804, Dec. 2016.DOI: 10.1109/TMECH.2016.2531629.

The ability of robotic systems to autonomously understand and/or navigate in uncertain environments is critically dependent on fairly accurate strategies, which are not always optimally achieved due to effectiveness, computational cost, and parameter settings. In this paper, we propose a novel and simple adaptive strategy to increase the efficiency and drastically reduce the computational effort in particle filters (PFs). The purpose of the adaptive approach (dispersion-based adaptive particle filter – DAPF) is to provide higher number of particles during the initial searching state (when the localization presents greater uncertainty) and fewer particles during the subsequent state (when the localization exhibits less uncertainty). With the aim of studying the dynamical PF behavior regarding others and putting the proposed algorithm into practice, we designed a methodology based on different target applications and a Kinect sensor. The various experiments conducted for both color tracking and mobile robot localization problems served to demonstrate that the DAPF algorithm can be further generalized. As a result, the DAPF approach significantly improved the computational performance over two well-known filtering strategies: 1) the classical PF with fixed particle set sizes, and 2) the adaptive technique named Kullback-Leiber distance.

Posted in: Mobile Robot Localization , Tagged: ,

Survey and taxonomy of path planning algorithms

June 20, 2017 10:21 ,

Thi Thoa Mac, Cosmin Copot, Duc Trung Tran, Robin De Keyser, Heuristic approaches in robot path planning: A survey, Robotics and Autonomous Systems, Volume 86, 2016, Pages 13-28, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.08.001.

Autonomous navigation of a robot is a promising research domain due to its extensive applications. The navigation consists of four essential requirements known as perception, localization, cognition and path planning, and motion control in which path planning is the most important and interesting part. The proposed path planning techniques are classified into two main categories: classical methods and heuristic methods. The classical methods consist of cell decomposition, potential field method, subgoal network and road map. The approaches are simple; however, they commonly consume expensive computation and may possibly fail when the robot confronts with uncertainty. This survey concentrates on heuristic-based algorithms in robot path planning which are comprised of neural network, fuzzy logic, nature-inspired algorithms and hybrid algorithms. In addition, potential field method is also considered due to the good results. The strengths and drawbacks of each algorithm are discussed and future outline is provided.

Posted in: Robot motion planning , Tagged: , ,

Globally optimal ICP

June 19, 2017 11:55 ,

J. Yang, H. Li, D. Campbell and Y. Jia, “Go-ICP: A Globally Optimal Solution to 3D ICP Point-Set Registration,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 11, pp. 2241-2254, Nov. 1 2016. DOI: 10.1109/TPAMI.2015.2513405.

The Iterative Closest Point (ICP) algorithm is one of the most widely used methods for point-set registration. However, being based on local iterative optimization, ICP is known to be susceptible to local minima. Its performance critically relies on the quality of the initialization and only local optimality is guaranteed. This paper presents the first globally optimal algorithm, named Go-ICP, for Euclidean (rigid) registration of two 3D point-sets under the $L_2$ error metric defined in ICP. The Go-ICP method is based on a branch-and-bound scheme that searches the entire 3D motion space $SE(3)$ . By exploiting the special structure of $SE(3)$ geometry, we derive novel upper and lower bounds for the registration error function. Local ICP is integrated into the BnB scheme, which speeds up the new method while guaranteeing global optimality. We also discuss extensions, addressing the issue of outlier robustness. The evaluation demonstrates that the proposed method is able to produce reliable registration results regardless of the initialization. Go-ICP can be applied in scenarios where an optimal solution is desirable or where a good initialization is not always available.

Posted in: Mobile Robot Localization , Tagged: ,

Learning from demonstration through inverse reinforcement learning enhaced with neural network for generalizing demonstrations and improve visiting of states

June 19, 2017 11:01 ,

Chen Xia, Abdelkader El Kamel, Neural inverse reinforcement learning in autonomous navigation, Robotics and Autonomous Systems, Volume 84, 2016, Pages 1-14, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.06.003.

Designing intelligent and robust autonomous navigation systems remains a great challenge in mobile robotics. Inverse reinforcement learning (IRL) offers an efficient learning technique from expert demonstrations to teach robots how to perform specific tasks without manually specifying the reward function. Most of existing IRL algorithms assume the expert policy to be optimal and deterministic, and are applied to experiments with relatively small-size state spaces. However, in autonomous navigation tasks, the state spaces are frequently large and demonstrations can hardly visit all the states. Meanwhile the expert policy may be non-optimal and stochastic. In this paper, we focus on IRL with large-scale and high-dimensional state spaces by introducing the neural network to generalize the expert’s behaviors to unvisited regions of the state space and an explicit policy representation is easily expressed by neural network, even for the stochastic expert policy. An efficient and convenient algorithm, Neural Inverse Reinforcement Learning (NIRL), is proposed. Experimental results on simulated autonomous navigation tasks show that a mobile robot using our approach can successfully navigate to the target position without colliding with unpredicted obstacles, largely reduce the learning time, and has a good generalization performance on undemonstrated states. Hence prove the robot intelligence of autonomous navigation transplanted from limited demonstrations to completely unknown tasks.

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

Mapping (and navitating in) outdoor unstructured environments with low-cost and few sensors, using relations between landmarks instead of absolute or metrical positions

June 19, 2017 10:21 ,

Mark McClelland, Mark Campbell, Tara Estlin, Qualitative relational mapping and navigation for planetary rovers, Robotics and Autonomous Systems, Volume 83, 2016, Pages 73-86, ISSN 0921-8890, DOI: j.robot.2016.05.017.

This paper presents a novel method for qualitative mapping of large scale spaces which decouples the mapping problem from that of position estimation. The proposed framework makes use of a graphical representation of the world in order to build a map consisting of qualitative constraints on the geometric relationships between landmark triplets. This process allows a mobile robot to extract information about landmark positions using a set of minimal sensors in the absence of GPS. A novel measurement method based on camera imagery is presented which extends previous work from the field of Qualitative Spatial Reasoning. A Branch-and-Bound approach is taken to solve a set of non-convex feasibility problems required for generating off-line operator lookup tables and on-line measurements, which are fused into the map using an iterative graph update. A navigation approach for travel between distant landmarks is developed, using estimates of the Relative Neighborhood Graph extracted from the qualitative map in order to generate a sequence of landmark objectives based on proximity. Average and asymptotic performance of the mapping algorithm is evaluated using Monte Carlo tests on randomly generated maps, and a data-driven simulation is presented for a robot traversing the Jet Propulsion Laboratory Mars Yard while building a relational map. These results demonstrate that the system can be effectively used to build a map sufficiently complete and accurate for long-distance navigation as well as other applications.

Posted in: Mobile robot mapping , Tagged: ,

Sample-based approximation to POMDPs integrated with forward simulation for robot active exploration, with a nice related work about active exploration in robotics

June 19, 2017 10:01 ,

Mikko Lauri, Risto Ritala, Planning for robotic exploration based on forward simulation, Robotics and Autonomous Systems, Volume 83, 2016, Pages 15-31, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.06.008.

We address the problem of controlling a mobile robot to explore a partially known environment. The robot’s objective is the maximization of the amount of information collected about the environment. We formulate the problem as a partially observable Markov decision process (POMDP) with an information-theoretic objective function, and solve it applying forward simulation algorithms with an open-loop approximation. We present a new sample-based approximation for mutual information useful in mobile robotics. The approximation can be seamlessly integrated with forward simulation planning algorithms. We investigate the usefulness of POMDP based planning for exploration, and to alleviate some of its weaknesses propose a combination with frontier based exploration. Experimental results in simulated and real environments show that, depending on the environment, applying POMDP based planning for exploration can improve performance over frontier exploration.

Posted in: Robot task planning , Tagged: ,

Learning concepts from graphs in robotics, through first-order logic and discovery of subgraphs, forming arbitrary hierarchies

June 19, 2017 09:49 ,

Ana C. Tenorio-González, Eduardo F. Morales, Automatic discovery of relational concepts by an incremental graph-based representation, Robotics and Autonomous Systems, Volume 83, 2016, Pages 1-14, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.06.012.

Automatic discovery of concepts has been an elusive area in machine learning. In this paper, we describe a system, called ADC, that automatically discovers concepts in a robotics domain, performing predicate invention. Unlike traditional approaches of concept discovery, our approach automatically finds and collects instances of potential relational concepts. An agent, using ADC, creates an incremental graph-based representation with the information it gathers while exploring its environment, from which common sub-graphs are identified. The subgraphs discovered are instances of potential relational concepts which are induced with Inductive Logic Programming and predicate invention. Several concepts can be induced concurrently and the learned concepts can form arbitrarily hierarchies. The approach was tested for learning concepts of polygons, furniture, and floors of buildings with a simulated robot and compared with concepts suggested by users.

Posted in: Artificial Intelligence, Robotics , Tagged: , ,

Survey of model-based reinforcement learning (and of reinforcement learning in general), for its application to improve learning time in robotics; a lot of references but not so many -or clear- explanations

June 12, 2017 10:58 ,

Athanasios S. Polydoros, Lazaros Nalpantidis, Survey of Model-Based Reinforcement Learning: Applications on Robotics, Journal of Intelligent & Robotic Systems, May 2017, Volume 86, Issue 2, pp 153–173, DOI: 10.1007/s10846-017-0468-y.

Reinforcement learning is an appealing approach for allowing robots to learn new tasks. Relevant literature reveals a plethora of methods, but at the same time makes clear the lack of implementations for dealing with real life challenges. Current expectations raise the demand for adaptable robots. We argue that, by employing model-based reinforcement learning, the—now limited—adaptability characteristics of robotic systems can be expanded. Also, model-based reinforcement learning exhibits advantages that makes it more applicable to real life use-cases compared to model-free methods. Thus, in this survey, model-based methods that have been applied in robotics are covered. We categorize them based on the derivation of an optimal policy, the definition of the returns function, the type of the transition model and the learned task. Finally, we discuss the applicability of model-based reinforcement learning approaches in new applications, taking into consideration the state of the art in both algorithms and hardware.

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

Emergence of symbols in robotics as a “new” area of research in developmental robotics: a survey

June 12, 2017 10:45 ,

Tadahiro Taniguchi, Takayuki Nagai, Tomoaki Nakamura, Naoto Iwahashi, Tetsuya Ogata, Hideki Asoh, Symbol Emergence in Robotics: A Survey, arXiv:1509.08973.

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory–motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.

Posted in: Developmental robotics , Tagged: , ,