Monthly Archives: July 2017

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Real-time modification of user inputs in the teleoperation of an UAV in order to avoid obstacles with a reactive algorithm, transparently from the user control

Daman Bareiss, Joseph R. Bourne & Kam K. Leang, On-board model-based automatic collision avoidance: application in remotely-piloted unmanned aerial vehicles, Auton Robot (2017) 41:1539–1554, DOI: 10.1007/s10514-017-9614-4.

This paper focuses on real-world implementation and verification of a local, model-based stochastic automatic collision avoidance algorithm, with application in
remotely-piloted (tele-operated) unmanned aerial vehicles (UAVs). Automatic collision detection and avoidance for tele-operated UAVs can reduce the workload of pilots to allow them to focus on the task at hand, such as searching for victims in a search and rescue scenario following a natural disaster. The proposed algorithm takes the pilot’s input and exploits the robot’s dynamics to predict the robot’s trajectory for determining whether a collision will occur. Using on-board sensors for obstacle detection, if a collision is imminent, the algorithm modifies the pilot’s input to avoid the collision while attempting to maintain the pilot’s intent. The algorithm is implemented using a low-cost on-board computer, flight-control system, and a two-dimensional laser illuminated detection and ranging sensor for obstacle detection along the trajectory of the robot. The sensor data is processed using a split-and-merge segmentation algorithm and an approximate Minkowski difference. Results from flight tests demonstrate the algorithm’s capabilities for teleoperated collision-free control of an experimental UAV.

Learning basic motion skills through modeling them as parameterized modules (learned by demonstration and babbling), and a nice state of the art of the development of motion skills

René Felix Reinhart, Autonomous exploration of motor skills by skill babbling, Auton Robot (2017) 41:1521–1537, DOI: 10.1007/s10514-016-9613-x.

Autonomous exploration of motor skills is a key capability of learning robotic systems. Learning motor skills can be formulated as inverse modeling problem, which targets at finding an inverse model that maps desired outcomes in some task space, e.g., via points of a motion, to appropriate actions, e.g., motion control policy parameters. In this paper, autonomous exploration of motor skills is achieved by incrementally learning inverse models starting from an initial demonstration. The algorithm is referred to as skill babbling, features sample-efficient learning, and scales to high-dimensional action spaces. Skill babbling extends ideas of goal-directed exploration, which organizes exploration in the space of goals. The proposed approach provides a modular framework for autonomous skill exploration by separating the learning of the inverse model from the exploration mechanism and a model of achievable targets, i.e. the workspace. The effectiveness of skill babbling is demonstrated for a range of motor tasks comprising the autonomous bootstrapping of inverse kinematics and parameterized motion primitives.

A new feature for 3D point clouds that is more efficient than the state-of-the-art SHOT

Sai Manoj Prakhya, Bingbing Liu, Weisi Lin, Vinit Jakhetiya & Sharath Chandra Guntuku, B-SHOT: a binary 3D feature descriptor for fast Keypoint matching on 3D point clouds, Auton Robot (2017) 41:1501–1520, DOI: 10.1007/s10514-016-9612-y.

We present the first attempt in creating a binary 3D feature descriptor for fast and efficient keypoint matching on 3D point clouds. Specifically, we propose a linarization technique and apply it on the state-of-the-art 3D feature descriptor, SHOT to create the first binary 3D feature descriptor, which we call B-SHOT. B-SHOT requires 32 times lesser memory for its representation while being six times faster in feature descriptor matching, when compared to the SHOT feature descriptor. Next, we propose a robust evaluation metric, specifically for 3D feature descriptors. A comprehensive evaluation on standard benchmarks reveals that B-SHOT offers comparable keypoint matching performance to that of the state-of-the-art real valued 3D feature descriptors, albeit at dramatically lower computational and memory costs.

Testbed for comparisons of different UWB sensors applied to localization

A. R. Jiménez Ruiz and F. Seco Granja, “Comparing Ubisense, BeSpoon, and DecaWave UWB Location Systems: Indoor Performance Analysis,” in IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 8, pp. 2106-2117, Aug. 2017.DOI: 10.1109/TIM.2017.2681398.

Most ultrawideband (UWB) location systems already proposed for position estimation have only been individually evaluated for particular scenarios. For a fair performance comparison among different solutions, a common evaluation scenario would be desirable. In this paper, we compare three commercially available UWB systems (Ubisense, BeSpoon, and DecaWave) under the same experimental conditions, in order to do a critical performance analysis. We include the characterization of the quality of the estimated tag-to-sensor distances in an indoor industrial environment. This testing space includes areas under line-of-sight (LOS) and diverse non-LOS conditions caused by the reflection, propagation, and the diffraction of the UWB radio signals across different obstacles. The study also includes the analysis of the estimated azimuth and elevation angles for the Ubisense system, which is the only one that incorporates this feature using an array antenna at each sensor. Finally, we analyze the 3-D positioning estimation performance of the three UWB systems using a Bayesian filter implemented with a particle filter and a measurement model that takes into account bad range measurements and outliers. A final conclusion is drawn about which system performs better under these industrial conditions.

A method to model trajectories that captures its essential parameters (for comparisons, clustering, etc.)

W. Lin et al., “A Tube-and-Droplet-Based Approach for Representing and Analyzing Motion Trajectories,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 8, pp. 1489-1503, Aug. 1 2017.DOI: 10.1109/TPAMI.2016.2608884.

Trajectory analysis is essential in many applications. In this paper, we address the problem of representing motion trajectories in a highly informative way, and consequently utilize it for analyzing trajectories. Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion pattern in a scene. Then, a 3D tube is derived which depicts an input trajectory by integrating its surrounding motion patterns contained in the thermal transfer field. The 3D tube effectively: 1) maintains the movement information of a trajectory, 2) embeds the complete contextual motion pattern around a trajectory, 3) visualizes information about a trajectory in a clear and unified way. We further introduce a droplet-based process. It derives a droplet vector from a 3D tube, so as to characterize the high-dimensional 3D tube information in a simple but effective way. Finally, we apply our tube-and-droplet representation to trajectory analysis applications including trajectory clustering, trajectory classification & abnormality detection, and 3D action recognition. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our approach.

Several strategies for exploring unknown environments based on graphs extracted from Voronoi diagrams

E. G. Tsardoulias, A. Iliakopoulou, A. Kargakos, L. Petrou, Cost-Based Target Selection Techniques Towards Full Space Exploration and Coverage for USAR applications in a Priori Unknown Environments, J Intell Robot Syst (2017) 87:313–340, DOI: 10.1007/s10846-016-0434-0.

Full coverage and exploration of an environment is essential in robot rescue operations where victim identification is required. Three methods of target selection towards full exploration and coverage of an unknown space oriented for Urban Search and Rescue (USAR) applications have been developed. These are the Selection of the closest topological node, the Selection of the minimum cost topological node and the Selection of the minimum cost sub-graph. All methods employ a topological graph extracted from the Generalized Voronoi Diagram (GVD), in order to select the next best target during exploration. The first method utilizes a distance metric for determining the next best target whereas the Selection of the minimum cost topological node method assigns four different weights on the graph’s nodes, based on certain environmental attributes. The Selection of the minimum cost sub-graph uses a similar technique, but instead of single nodes, sets of graph nodes are examined. In addition, a modification of A* algorithm for biased path creation towards uncovered areas, aiming at a faster spatial coverage, is introduced. The proposed methods’ performance is verified by experiments conducted in two heterogeneous simulated environments. Finally, the results are compared with two common exploration methods.

Prediction of changes in behaviors of cars for autohomous driving, based on POMDPs made efficient by separation of multiple policies

Enric Galceran, Alexander G. Cunningham, Ryan M. Eustice, Edwin Olson,Multipolicy decision-making for autonomous driving via changepoint-based behavior prediction: Theory and experiment, Autonomous Robots, August 2017, Volume 41, Issue 6, pp 1367–1382, DOI: 10.1007/s10514-017-9619-z.

This paper reports on an integrated inference and decision-making approach for autonomous driving that models vehicle behavior for both our vehicle and nearby vehicles as a discrete set of closed-loop policies. Each policy captures a distinct high-level behavior and intention, such as driving along a lane or turning at an intersection. We first employ Bayesian changepoint detection on the observed history of nearby cars to estimate the distribution over potential policies that each nearby car might be executing. We then sample policy assignments from these distributions to obtain high-likelihood actions for each participating vehicle, and perform closed-loop forward simulation to predict the outcome for each sampled policy assignment. After evaluating these predicted outcomes, we execute the policy with the maximum expected reward value. We validate behavioral prediction and decision-making using simulated and real-world experiments.