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Anonymization is a common technique for publishing a location data set in a privacy-preserving way. However, such an anonymized data set lacks trajectory information of users, which could be beneficial to many location-based analytic services. In this paper, we present a dynamic pseudonym scheme for constructing alternate possible paths of mobile users to protect their location privacy. We introduce...
Smart powered wheelchairs offer the possibility of enhanced mobility to a large and growing population—most notably older adults—and a key feature of such a chair is collision avoidance. Sensors are required to detect nearby obstacles; however, complete sensor coverage of the immediate neighbourhood is challenging for reasons including financial, computational, aesthetic, user identity and sensor...
In this paper we introduce a novel RRT extend function for wheeled mobile robots. The approach computes closed-loop forward simulations based on the kinematic model of the robot and enables the planner to efficiently generate smooth and feasible paths that connect any pairs of states. We extend the control law of an existing discontinuous state feedback controller to make it usable as an RRT extend...
The availability of massive trajectory data collected from GPS devices has received significant attentions in recent years. A hot topic is trip recommendation, which focuses on searching trajectories that connect (or are close to) a set of query locations, e.g., several sightseeing places specified by a traveller, from a collection of historic trajectories made by other travellers. However, if we...
Manipulation of error feedback has been of great interest to recent studies in motor control and rehabilitation. Typically, motor adaptation is shown as a change in performance with a single scalar metric for each trial, yet such an approach might overlook details about how error evolves through the movement. We believe that statistical distributions of movement error through the extent of the trajectory...
The context of this work is the online characterization of errors in large scale systems. In particular, we address the following question: Given two successive configurations of the system, can we distinguish massive errors from isolated ones, the former ones impacting a large number of nodes while the second ones affect solely a small number of them, or even a single one? The rationale of this question...
In this paper we present a new approach to the threat assessment problem for semi-autonomous and fully autonomous vehicles, based on the estimation of the control freedom afforded to a vehicle. Given sensor information available about the surrounding environment, an algorithm is described for identifying fields of safe travel through which the vehicle can safely navigate. Within each candidate field,...
A good state-time quantized symbolic abstraction of an already input quantized control system would satisfy three conditions: proximity, soundness and completeness. But instability of systems, whose inputs are bounded and quantized, is an impediment to constructing fully complete state-time quantized symbolic models, even using supervisory feedback. In this paper, we come up with a way of parametrization...
An approach addressing biped locomotion optimization is here introduced. Concepts from Central Pattern Generators (CPGs) and Dynamic Movement Primitives (DMPs) were combined to easily produce complex trajectories for the joints of a simulated DARwIn-OP. A Reinforcement Learning Algorithm, Policy Learning by Weighting Exploration with the Returns (PoWER), was implemented to improve the robot's locomotion...
This paper proposes an expertise-oriented training platform for robotics-assisted minimally invasive surgery. The framework builds on previous work of the authors and makes use of dual-user teleoperation scenario, allowing the presence of an expert in the training loop. A Fuzzy-Logic (FL) methodology is proposed, which specifies the level/mode of the training required for the trainee according to...
Gaussian Processes (GPs) are gaining increasing popularity due to their expressive power for learning the dynamics of non-linear time series data, e.g. for human motion prediction. However, so far they are restricted to Euclidean space: input data such as position and velocity need to be Euclidean. In this paper, we examine GPs over time series of 6D rigid body motions including large rotations. As...
Path planning in continuous spaces has been a central problem in robotics. In the case of systems with complex dynamics, the performance of sampling based techniques relies on identifying a good approximation to the cost-to-go distance metric. We propose a technique that uses reinforcement learning to learn this distance metric on the fly from samples and combine it with existing sampling based planners...
Mobile wheeled- or tracked-robots drive in 2.5-dimensional (2.5D) environments, where the traversable surface can be considered as a 2D-manifold embedded in a three-dimensional (3D) ambient space. In this work, we aim at solving the 2.5D navigation problem solely on point-cloud. The proposed method is independent of traditional surface parametrization or reconstruction methods, such as a meshing process,...
The paper addresses the revisiting (loop closing) problem of simultaneous localization and mapping (SLAM) by investigating spatio-temporal coherence in inertial and perceptual inputs to improve the robustness and convergence of SLAM. The basic idea is to find out coherent subsequences of confidence in trajectory to ensure against error-prone correspondences. It is achieved by leveraging fuzzy matching...
The careful deployment of hotspots in metropolitan areas allow to maximize WiFi offloading, a viable solution to the recent boost up of mobile data consumption. Our proposed strategy considers routine characteristics present on people's daily trajectories, the space-time interaction between them urban locations, and their transportation modes. Using a reallife metropolitan trace, we show our routine-based...
Daily, vehicles in transit in a city and during their trajectories encounter other vehicles. The regularity of these encounters is influenced by several elements, such as: vehicle's speed, destinations, traffic conditions, and the period of the day. It is possible to justify these elements by road conditions and the driver's behavior. People have routines and similar behaviors, which strongly impact...
In this paper we present a novel approach for the estimation of metric velocities and metric distances to landmarks utilizing monocular images and inertial measurements only. The proposed algorithm is based on an Extended Kalman Filter and is closely related to the well known Simultaneous Localization and Mapping (SLAM). In contrast to standard SLAM formulations the state of an agent is expressed...
Recent years have witnessed two major trends in the development of complex real-time embedded systems. First, to reduce cost and enhance flexibility, multiple systems are sharing common computing platforms via virtualization technology, instead of being deployed separately on physically isolated hosts. Second, multicore processors are increasingly being used in real-time systems. The integration of...
We examine the problem of planning the trajectory of a robotic vehicle to gather data from a deployment of stationary sensors monitoring a set of dynamic source signals. The robotic vehicle and the sensors are equipped with wireless modems (e.g., radio in terrestrial environments or acoustic in underwater environments), which provide noisy communication across limited distances. In such scenarios,...
Many adaptive sensing and sensor management strategies seek to determine a sequence of sensor actions that successively optimizes an objective function. Frequently the goal is to adjust a sensor to best estimate a partially observed state variable, for example, the objective function may be the final mean-squared state estimation error. Information-driven sensor planning strategies adopt an objective...
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