Models of motion patterns for mobile robotic systems

Human robot interaction is an emerging area of research with many challenges. Knowledge about human behaviors could lead to more effective and efficient interactions of a robot in populated environments. This paper presents a probabilistic framework for the learning and representation of human motion patterns in an office environment. It is based on the observation that most human trajectories are not random. Instead people plan trajectories based on many considerations, such as social rules and path length. Motion patterns are learned using an incrementally growing Sampled Hidden Markov Model. This model has a number of interesting properties which can be of use in many applications. For example, the learned knowledge can be used to predict motion, infer social rules, thus improve a robot's operation and its interaction with people in a populated space. The proposed learning method is extensively validated in real world experiments. ©2010 IEEE

Robot path planning in a social context

Human robot interaction has attracted significant attention over the last couple of years. An important aspect of such robotic systems is to share the working space with humans and carry out the tasks in a socially acceptable way. In this paper, we address the problem of fusing socially acceptable behaviours into robot path planning. By observing an environment for a while, the robot learns human motion patterns based on sampled Hidden Markov Models and utilises them in a Probabilistic Roadmap based path planning algorithm. This will minimise the social distractions, such as going through someone else's working space (due to the shortest path), by planning the path through minimal distractions, leading to human-like behaviours. The algorithm is implemented in Orca/C++ with appealing results in real world experiments. ©2010 IEEE

Socially aware path planning for mobile robots

© 2014 Cambridge University Press. Human-robot interaction is an emerging area of research where a robot may need to be working in human-populated environments. Human trajectories are generally not random and can belong to gross patterns. Knowledge about these patterns can be learned through observation. In this paper, we address the problem of a robot's social awareness by learning human motion patterns and integrating them in path planning. The gross motion patterns are learned using a novel Sampled Hidden Markov Model, which allows the integration of partial observations in dynamic model building. This model is used in the modified A∗ path planning algorithm to achieve socially aware trajectories. Novelty of the proposed method is that it can be used on a mobile robot for simultaneous online learning and path planning. The experiments carried out in an office environment show that the paths can be planned seamlessly, avoiding personal spaces of occupants

Using common motion patterns to improve a robot's operation in populated environments

Robotic devices are increasingly penetrating the human work spaces as stand alone units and helpers. It is believed that a robot could be easily integrated with humans, if the robot can learn how to behave in a socially acceptable manner. This involves a robot to observe, learn and comply with basic rules of human behaviors. As an example, one would expect a robot to travel in an environment without intruding human workspaces unnecessarily. Thus, identifying common motion patterns of people by observing a specific environment is an important task as people's trajectories are usually not random, however are tailored to the way the environment is structured. We propose a learning algorithm to construct a Sampled Hidden Markov Model (SHMM) that captures behavior of people through observations and then demonstrate how this model could be exploited for planning socially aware paths. Experimental results are presented to demonstrate the viability of the proposed approach. ©2010 IEEE

Multiple Cue Based Vehicle Detection and Tracking for Road Safety

With the rise in accident related fatalities on roads, the researchers around the world are looking for solutions including integrating intelligence to vehicles. One cruicial aspects of it is the robust detection and tracking of other vehicles in the visinity. In this paper, we have proposed a probabilistic way of incorporation of several visual cues in vehicle detection and a particle filter based tracking strategy. Visual cues used are, lane markings, symmetry, entropy and shadows. Combination of visual cues provided us with robust results when compared with their individual counterparts. The definition of a region of interest lowers the computational requirements with improved robustness. Experimental results of the algorithm in Sydney urban areas are presente

Towards improving driver situation awareness at intersections

Providing safety critical information to the driver is vital in reducing road accidents, especially at intersections. Intersections are complex to deal with due to the presence of large number of vehicle and pedestrian activities, and possible occlusions. Information available from only the sensors onboard a vehicle has limited value in this scenario. In this paper, we propose to utilize sensors on-board the vehicle of interest as well as the sensors that are mounted on nearby vehicles to enhance the driver situation awareness. The resulting major research challenge of sensor registration with moving observers is solved using a mutual information based technique. The response of the sensors to common causes are identified and exploited for computing their unknown relative locations. Experimental results, for a mock up traffic intersection in which mobile robots equipped with laser range finders are used, are presented to demonstrate the efficacy of the proposed technique. ©2007 IEEE

Robust lane detection in urban environments

Most of the lane marking detection algorithms reported in the literature are suitable for highway scenarios. This paper presents a novel clustered particle filter based approach to lane detection, which is suitable for urban streets in normal traffic conditions. Furthermore, a quality measure for the detection is calculated as a measure of reliability. The core of this approach is the usage of weak models, i.e. the avoidance of strong assumptions about the road geometry. Experiments were carried out in Sydney urban areas with a vehicle mounted laser range scanner and a ccd camera. Through experimentations, we have shown that a clustered particle filter can be used to efficiently extract lane markings. ©2007 IEEE

Prior-knowledge assisted fast 3D map building of structured environments for steel bridge maintenance

Practical application of a robot in a structured, yet unknown environment, such as in bridge maintenance, requires the robot to quickly generate an accurate map of the surfaces in the environment. A consistent and complete map is fundamental to achieving reliable and robust operation. In a real-world and field application, sensor noise and insufficient exploration oftentimes result in an incomplete map. This paper presents a robust environment mapping approach using prior knowledge in combination with a single depth camera mounted on the end-effector of a robotic manipulator. The approach has been successfully implemented in an industrial setting for the purpose of steel bridge maintenance. A prototype robot, which includes the presented map building approach in its software package, has recently been delivered to industry. © 2013 IEEE

Towards an enhanced driver situation awareness system

This paper outlines our current research agenda to achieve enhanced driver situation awareness. A novel approach that incorporates information gathered from sensors mounted on the neighboring vehicles, in the road infrastructure as well as onboard sensory information is proposed. A solution to the fundamental issue of registering data into a common reference frame when the relative locations of the sensors themselves are changing is outlined. A description of the vehicle test bed, experimental results from information gathered from various onboard sensors, and preliminary results from the sensor registration algorithm are presented. ©2007 IEEE

Probabilistic lane estimation for autonomous driving using basis curves

Lane estimation for autonomous driving can be formulated as a curve estimation problem, where local sensor data provides partial and noisy observations of spatial curves forming lane boundaries. The number of lanes to estimate are initially unknown and many observations may be outliers or false detections (due e.g. to shadows or non-boundary road paint). The challenges lie in detecting lanes when and where they exist, and updating lane estimates as new observations are made. This paper describes an efficient probabilistic lane estimation algorithm based on a novel curve representation. The key advance is a principled mechanism to describe many similar curves as variations of a single basis curve. Locally observed road paint and curb features are then fused to detect and estimate all nearby travel lanes. The system handles roads with complex multi-lane geometries and makes no assumptions about the position and orientation of the vehicle with respect to the roadway. We evaluate our algorithm using a ground truth dataset containing manually-labeled, fine-grained lane geometries for vehicle travel in two large and diverse datasets that include more than 300,000 images and 44 km of roadway. The results illustrate the capabilities of our algorithm for robust lane estimation in the face of challenging conditions and unknown roadways.United States. Defense Advanced Research Projects Agency (Urban Challenge, ARPA Order No. W369/00, Program Code DIRO, issued by DARPA/CMO under Contract No. HR0011-06-C-0149