Category Archives: Robotics

Use of Markov Decision Processes to select tasks in a service mobile robots

June 28, 2019 09:06 ,

Lacerda, B., Faruq, F., Parker, D., & Hawes, N., Probabilistic planning with formal performance guarantees for mobile service robots, The International Journal of Robotics Research, DOI: 10.1177/0278364919856695.

We present a framework for mobile service robot task planning and execution, based on the use of probabilistic verification techniques for the generation of optimal policies with attached formal performance guarantees. Our approach is based on a Markov decision process model of the robot in its environment, encompassing a topological map where nodes represent relevant locations in the environment, and a range of tasks that can be executed in different locations. The navigation in the topological map is modeled stochastically for a specific time of day. This is done by using spatio-temporal models that provide, for a given time of day, the probability of successfully navigating between two topological nodes, and the expected time to do so. We then present a methodology to generate cost optimal policies for tasks specified in co-safe linear temporal logic. Our key contribution is to address scenarios in which the task may not be achievable with probability one. We introduce a task progression function and present an approach to generate policies that are formally guaranteed to, in decreasing order of priority: maximize the probability of finishing the task; maximize progress towards completion, if this is not possible; and minimize the expected time or cost required. We illustrate and evaluate our approach with a scalability evaluation in a simulated scenario, and report on its implementation in a robot performing service tasks in an office environment for long periods of time.

Posted in: Robot task planning , Tagged: ,

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 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: ,

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:

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: ,