Category Archives: Robotics

A parallel implementation of a new probabilistic filter for occupancy grid maps that deals with non-static environments

July 25, 2018 08:10 ,

Dominik Nuss, Stephan Reuter, Markus Thom, …, A random finite set approach for dynamic occupancy grid maps with real-time application, The International Journal of Robotics Research DOI: 10.1177/0278364918775523.

Grid mapping is a well-established approach for environment perception in robotic and automotive applications. Early work suggests estimating the occupancy state of each grid cell in a robot’s environment using a Bayesian filter to recursively combine new measurements with the current posterior state estimate of each grid cell. This filter is often referred to as binary Bayes filter. A basic assumption of classical occupancy grid maps is a stationary environment. Recent publications describe bottom-up approaches using particles to represent the dynamic state of a grid cell and outline prediction-update recursions in a heuristic manner. This paper defines the state of multiple grid cells as a random finite set, which allows to model the environment as a stochastic, dynamic system with multiple obstacles, observed by a stochastic measurement system. It motivates an original filter called the probability hypothesis density / multi-instance Bernoulli (PHD/MIB) filter in a top-down manner. The paper presents a real-time application serving as a fusion layer for laser and radar sensor data and describes in detail a highly efficient parallel particle filter implementation. A quantitative evaluation shows that parameters of the stochastic process model affect the filter results as theoretically expected and that appropriate process and observation models provide consistent state estimation results.

Posted in: Mobile robot mapping , Tagged: ,

High performance robotic computing (HPRC) vs. High performance computing, and its application to multirobot systems

July 17, 2018 11:43 ,

Leonardo Camargo-Forero, Pablo Royo, Xavier Prats, Towards high performance robotic computing, Robotics and Autonomous Systems, Volume 107, 2018, Pages 167-181 DOI: 10.1016/j.robot.2018.05.011.

Embedding a robot with a companion computer is becoming a common practice nowadays. Such computer is installed with an operatingsystem, often a Linux distribution. Moreover, Graphic Processing Units (GPUs) can be embedded on a robot, giving it the capacity of performing complex on-board computing tasks while executing a mission. It seems that a next logical transition, consist of deploying a cluster of computers among embedded computing cards. With this approach, a multi-robot system can be set as a High Performance Computing (HPC) cluster. The advantages of such infrastructure are many, from providing higher computing power up to setting scalable multi-robot systems. While HPC has been always seen as a speeding-up tool, we believe that HPC in the world of robotics can do much more than simply accelerating the execution of complex computing tasks. In this paper, we introduce the novel concept of High Performance Robotic Computing — HPRC, an augmentation of the ideas behind traditional HPC to fit and enhance the world of robotics. As a proof of concept, we introduce novel HPC software developed to control the motion of a set of robots using the standard parallel MPI (Message Passing Interface) library. The parallel motion software includes two operation modes: Parallel motion to specific target and swarm-like behavior. Furthermore, the HPC software is virtually scalable to control any quantity of moving robots, including Unmanned Aerial Vehicles, Unmanned Ground Vehicles, etc.

Posted in: Computer science, Robotic software , Tagged: ,

Filters with quaternions for localization

July 3, 2018 11:35 ,

Rangaprasad Arun Srivatsan, Mengyun Xu, Nicolas Zevallos, and Howie Choset, Probabilistic pose estimation using a Bingham distribution-based linear filter, The International Journal of Robotics Research DOI: 10.1177/0278364918778353.

Pose estimation is central to several robotics applications such as registration, hand–eye calibration, and simultaneous localization and mapping (SLAM). Online pose estimation methods typically use Gaussian distributions to describe the uncertainty in the pose parameters. Such a description can be inadequate when using parameters such as unit quaternions that are not unimodally distributed. A Bingham distribution can effectively model the uncertainty in unit quaternions, as it has antipodal symmetry, and is defined on a unit hypersphere. A combination of Gaussian and Bingham distributions is used to develop a truly linear filter that accurately estimates the distribution of the pose parameters. The linear filter, however, comes at the cost of state-dependent measurement uncertainty. Using results from stochastic theory, we show that the state-dependent measurement uncertainty can be evaluated exactly. To show the broad applicability of this approach, we derive linear measurement models for applications that use position, surface-normal, and pose measurements. Experiments assert that this approach is robust to initial estimation errors as well as sensor noise. Compared with state-of-the-art methods, our approach takes fewer iterations to converge onto the correct pose estimate. The efficacy of the formulation is illustrated with a number of examples on standard datasets as well as real-world experiments.

Posted in: Mobile Robot Localization

A robot architecture for humanoids able to coordinate different cognitive processes (perception, decision-making, etc.) in a hierarchical fashion

June 14, 2018 08:59 ,

J. Hwang and J. Tani, Seamless Integration and Coordination of Cognitive Skills in Humanoid Robots: A Deep Learning Approach, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 2, pp. 345-358 DOI: 10.1109/TCDS.2017.2714170.

This paper investigates how adequate coordination among the different cognitive processes of a humanoid robot can be developed through end-to-end learning of direct perception of visuomotor stream. We propose a deep dynamic neural network model built on a dynamic vision network, a motor generation network, and a higher-level network. The proposed model was designed to process and to integrate direct perception of dynamic visuomotor patterns in a hierarchical model characterized by different spatial and temporal constraints imposed on each level. We conducted synthetic robotic experiments in which a robot learned to read human’s intention through observing the gestures and then to generate the corresponding goal-directed actions. Results verify that the proposed model is able to learn the tutored skills and to generalize them to novel situations. The model showed synergic coordination of perception, action, and decision making, and it integrated and coordinated a set of cognitive skills including visual perception, intention reading, attention switching, working memory, action preparation, and execution in a seamless manner. Analysis reveals that coherent internal representations emerged at each level of the hierarchy. Higher-level representation reflecting actional intention developed by means of continuous integration of the lower-level visuo-proprioceptive stream.

Posted in: Developmental robotics , Tagged: , ,

An interesting model of Basal Ganglia that performs similarly to Q learning when applied to a robot

June 14, 2018 08:55 ,

Y. Zeng, G. Wang and B. Xu, A Basal Ganglia Network Centric Reinforcement Learning Model and Its Application in Unmanned Aerial Vehicle, IEEE Transactions on Cognitive and Developmental Systems, vol. 10, no. 2, pp. 290-303 DOI: 10.1109/TCDS.2017.2649564.

Reinforcement learning brings flexibility and generality for machine learning, while most of them are mathematical optimization driven approaches, and lack of cognitive and neural evidence. In order to provide a more cognitive and neural mechanisms driven foundation and validate its applicability in complex task, we develop a basal ganglia (BG) network centric reinforcement learning model. Compared to existing work on modeling BG, this paper is unique from the following perspectives: 1) the orbitofrontal cortex (OFC) is taken into consideration. OFC is critical in decision making because of its responsibility for reward representation and is critical in controlling the learning process, while most of the BG centric models do not include OFC; 2) to compensate the inaccurate memory of numeric values, precise encoding is proposed to enable working memory system remember important values during the learning process. The method combines vector convolution and the idea of storage by digit bit and is efficient for accurate value storage; and 3) for information coding, the Hodgkin-Huxley model is used to obtain a more biological plausible description of action potential with plenty of ionic activities. To validate the effectiveness of the proposed model, we apply the model to the unmanned aerial vehicle (UAV) autonomous learning process in a 3-D environment. Experimental results show that our model is able to give the UAV the ability of free exploration in the environment and has comparable learning speed as the Q learning algorithm, while the major advances for our model is that it is with solid cognitive and neural basis.

Posted in: Applications of reinforcement learning to robots, Psycho-physiological bases of engineering , Tagged:

Interesting study about how to quantify the uncertainty in SLAM and the preservation of its monotonic growth, which is needed to good decision making in active SLAM

June 14, 2018 08:48 ,

M. L. Rodríguez-Arévalo, J. Neira and J. A. Castellanos, On the Importance of Uncertainty Representation in Active SLAM, IEEE Transactions on Robotics, vol. 34, no. 3, pp. 829-834 DOI: 10.1109/TRO.2018.2808902.

The purpose of this work is to highlight the paramount importance of representing and quantifying uncertainty to correctly report the associated confidence of the robot’s location estimate at each time step along its trajectory and therefore decide the correct course of action in an active SLAM mission. We analyze the monotonicity property of different decision-making criteria, both in 2-D and 3-D, with respect to the representation of uncertainty and of the orientation of the robot’s pose. Monotonicity, the property that uncertainty increases as the robot moves, is essential for adequate decision making. We analytically show that, by using differential representations to propagate spatial uncertainties, monotonicity is preserved for all optimality criteria, A-opt, D-opt, and E-opt, and for Shannon’s entropy. We also show that monotonicity does not hold for any criteria in absolute representations using Roll-Pitch-Yaw and Euler angles. Finally, using unit quaternions in absolute representations, the only criteria that preserve monotonicity are D-opt and Shannon’s entropy.

Posted in: Mobile robot SLAM , Tagged:

Evaluating the safeness of a motion plan for mobile robot navigation

June 12, 2018 07:54 ,

Brian Axelrod, Leslie Pack Kaelbling, and Tomás Lozano-Pérez Provably safe robot navigation with obstacle uncertainty, The International Journal of Robotics Research Vol 37, Issue 7 DOI: 10.1177/0278364918778338.

As drones and autonomous cars become more widespread, it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy has at most ϵ collision probability (ϵ-safe) with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information. We explore the idea of shadows that generalize the notion of a confidence set to estimated shapes and present a theorem that allows us to understand the relationship between shadows and their classical statistical equivalents such as confidence and credible sets. We present efficient algorithms that use shadows to prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our method’s ability to evaluate whether a trajectory or policy is safe. We then use these safety-checking methods to design a safe variant of the rapidly exploring random tree (RRT) planning algorithm.

Posted in: Robot motion planning , Tagged:

Shared autonomy where the target is predicted with POMDPs to cope with uncertain predictions

June 12, 2018 07:52 ,

Shervin Javdani, Henny Admoni, Stefania Pellegrinelli, Siddhartha S. Srinivasa, and J. Andrew Bagnell Shared autonomy via hindsight optimization for teleoperation and teaming, The International Journal of Robotics Research Vol 37, Issue 7, pp. 717 – 742 DOI: 10.1177/0278364918776060.

In shared autonomy, a user and autonomous system work together to achieve shared goals. To collaborate effectively, the autonomous system must know the user’s goal. As such, most prior works follow a predict-then-act model, first predicting the user’s goal with high confidence, then assisting given that goal. Unfortunately, confidently predicting the user’s goal may not be possible until they have nearly achieved it, causing predict-then-act methods to provide little assistance. However, the system can often provide useful assistance even when confidence for any single goal is low (e.g. move towards multiple goals). In this work, we formalize this insight by modeling shared autonomy as a partially observable Markov decision process (POMDP), providing assistance that minimizes the expected cost-to-go with an unknown goal. As solving this POMDP optimally is intractable, we use hindsight optimization to approximate. We apply our framework to both shared-control teleoperation and human–robot teaming. Compared with predict-then-act methods, our method achieves goals faster, requires less user input, decreases user idling time, and results in fewer user–robot collisions.

Posted in: Human teleoperation , Tagged: ,

A new mathematical formulation of manipulator motion that simplifies dynamics and kinematics

May 28, 2018 08:08 ,

Labbé, M. & Michaud, F., Comprehensive theory of differential kinematics and dynamics towards extensive motion optimization framework, The International Journal of Robotics Research First Published May 20, 2018 DOI: 10.1177/0278364918772893.

This paper presents a novel unified theoretical framework for differential kinematics and dynamics for the optimization of complex robot motion. By introducing an 18×18 comprehensive motion transformation matrix, the forward differential kinematics and dynamics, including velocity and acceleration, can be written in a simple chain product similar to an ordinary rotational matrix. This formulation enables the analytical computation of derivatives of various physical quantities (e.g. link velocities, link accelerations, or joint torques) with respect to joint coordinates, velocities and accelerations for a robot trajectory in an efficient manner (O(NJ), where NJ is the number of the robot’s degree of freedom), which is useful for motion optimization. Practical implementation of gradient computation is demonstrated together with simulation results of robot motion optimization to validate the effectiveness of the proposed framework.

Posted in: Industrial robots , Tagged: ,

Using short- and long-term memories in SLAM

May 26, 2018 08:00 ,

Labbé, M. & Michaud, F., Long-term online multi-session graph-based SPLAM with memory management, Auton Robot (2018) 42: 1133. DOI: 10.1007/s10514-017-9682-5.

For long-term simultaneous planning, localization and mapping (SPLAM), a robot should be able to continuously update its map according to the dynamic changes of the environment and the new areas explored. With limited onboard computation capabilities, a robot should also be able to limit the size of the map used for online localization and mapping. This paper addresses these challenges using a memory management mechanism, which identifies locations that should remain in a Working Memory (WM) for online processing from locations that should be transferred to a Long-Term Memory (LTM). When revisiting previously mapped areas that are in LTM, the mechanism can retrieve these locations and place them back in WM for online SPLAM. The approach is tested on a robot equipped with a short-range laser rangefinder and a RGB-D camera, patrolling autonomously 10.5 km in an indoor environment over 11 sessions while having encountered 139 people.

Posted in: Mobile robot SLAM , Tagged: