Abstract

Crowded urban environments are composed of different types of dynamic and static elements. Learning and classification of features is a major task in solving the localization problem in such environments. This work presents a gradual learning methodology to learn the useful features using multiple experiences. The usefulness of an observed element is evaluated by a scoring mechanism which uses two scores – reliability and distinctiveness. The visual features thus learned are used to partition the visual map into smaller regions. The robot is efficiently localized in such a partitioned environment using two-level localization. The concept of active map (AM) is proposed here, which is a map that represents one partition of the environment in which there is a high probability of the robot existing. High-level localization is used to track the mode of the AMs using discrete Bayes filter. Low-level localization uses a bag-of …

Abstract

The Simultaneous Localization and Mapping problem (SLAM) in robotics is typically modeled as a dyadic graph of relative pose measurements taken by the robot. The graph nodes store the values representing the absolute pose of the robot at a given point of time. An edge connecting two nodes represents robot movement and it stores the measurements taken by the robot sensor while moving between two nodes. The objective of the SLAM problem is to find the optimal global measurements best satisfying the noisy relative measurements [12]. This problem of optimal estimation on a graph given relative measurements is a well-studied problem within the control community, for which several results and algorithms are known [3, 4]. SLAM is generally solved as a least squares problem. Robust kernels which are less sensitive to outliers are used to deal with noise and outlier measurements. However, robust kernels tend to be dependent on initialization and can fail as the number of outliers increase. Therefore, it's important to identify and prune the outlier (noisy) measurements represented by incorrect loop closure edges for an accurate pose estimate. In this paper we propose a multi-scale Heat-Kernel analysis based loop closure edge pruning algorithm for the SLAM graph. We show that compared to other pruning algorithms, our algorithm has a substantially higher precision and recall when compared and is able to handle a large amount of outlier measurements.We have corroborated results on several publicly available datasets and several types of noise. Our algorithm is not restricted to SLAM graphs only, but has a much wider applicability to other types of geometric graphs.

Abstract

Complex tasks are often handled through software implementation in combination with high performance processors. Taking advantage of hardware parallelism, FPGA is breaking the paradigm by accomplishing more per clock cycle with closely matched application requirements. With the aim to minimise computation delay with increase in map's size and geometric constraints, we present the FPGA based combinatorial architecture that allows multiple RRTs to work together to achieve accelerated, uniform exploration of the map. We also analyse our architecture against hardware implementation of other scalable RRT methods for motion planning. We observe notable furtherance of acceleration capabilities with the proposed architecture delivering a minimum 3X gain over the other implementations while maintaining uniformity in exploration

Abstract

In an object search scenario with several small objects spread over a large indoor environment, the robot cannot infer about all of them at once. Pruning the search space is highly desirable in such a case. It has to actively select a course of actions to closely examine a selected set of objects. Here, we model the inferences about far away objects and their viewpoint priors into a decision analytic abstraction to prioritize the waypoints. By selecting objects of interest, a potential field is built over the environment by using Composite Viewpoint Object Potential (CVOP) maps. A CVOP is built using VOP, a framework to identify discriminative viewpoints to recognize small objects having distinctive features only in specific views. Also, a CVOP helps to clearly disambiguate objects which look similar from far away. We formulate a Decision Analysis Graph (DAG) over the above information, to assist the robot in actively navigating and maximize the reward earned. This optimal strategy increases search reliability, even in the presence of similar looking small objects which induce confusion into the agent and simultaneously reduces both time taken and distance travelled. To the best of our knowledge, there is no current unified formulation which addresses indoor object search scenarios in this manner. We evaluate our system over ROS using a TurtleBot mounted with a Kinect.

Abstract

This paper presents a novel approach for algorithmic singularity avoidance for reactionless visual servoing of a satellite mounted space robot. Task priority approach is used to perform visual servoing while reactionless manipulation of the space robot. Algorithmic singularity is prominent in such cases of prioritizing two tasks. The algorithmic singularity is different from the kinematic and dynamic singularities as the former is an artefact of the tasks at hand, and difficult to predict. In this paper, we present a geometric interpretation of its occurrence, and propose a method to avoid it. The method involves path planning in image space, and generates a sequence of images that guides the robot towards goal avoiding algorithmic singularity. The method is illustrated through numerical studies on a 6-DOF planar dual-arm robot mounted on a service satellite.

Abstract

This paper presents a new hierarchical architecture for parallelizing the computation intensive rapidly exploring random tree problem. The architecture resembles a tree like structure that agglutinates minimal inter-module communication of a shared memory with data integrity of a distributed memory. Another novelty of this research has been in quantitatively analysing the performance metrics of the RRT algorithm across numerous embedded hardware solutions and ultimately implementing this algorithm on an FPGA to achieve hardware level optimization that offers real time performance and economical power consumption levels. We then analyse our implementation against hardware implementation of other scalable parallel RRT methods for motion planning.

Abstract

This work focuses on enhancing step descending ability of the modular robot proposed in [16]. The proposed robot consists of three modules connected with each other through passive joints. It is propelled using an active pair of wheels per module. Since there are no actuators at the joints, the joints are not susceptible to losing operability while traversing on rugged terrain. However with the absence of actuators, we face the issue of the robot toppling over when an abnormally large obstacle is encountered. This shortcoming is overcome with the use of compliant joints. The compliant joints are designed by employing springs of optimal stiffness, which is calculated through an optimization formulation aided with the constraints presented by the static analysis of the robot. The novelty lies in the systematic design of compliant joint for step descent. The robot is successful in climbing and descending obstacles of dimension 17 cm. Simulations of the mathematically modelled robot are carried out. The results from the same are validated on a working prototype and presented.

Abstract

In this paper, we propose a better planning technique of the standard walking gaits for a quadruped robot than the conventional successive gait transition method to realize omnidirectional static walking. The technique involved planning the sequence as well as motion of the swinging and supporting legs. The relationship between the stability margin, the stride length and the duty factor are also formulated mathematically. The proposed modified crawl gait is compared to the conventional method with respect to the above parameters geometrically as well as mathematically and is shown to have positive stability margin at all times. The successive gait transition is demonstrated on the modified crawl and rotation gaits. Computer simulations of a model quadruped robot were performed to validate the theory proposed. Experiments were performed on an actual quadruped robot to realize the omnidirectional static walking with increased stability margin.

Abstract

Overtaking of a vehicle moving on structured roads is one of the most frequent driving behavior. In this work, we have described a Real Time Control System based framework for overtaking maneuver of autonomous vehicles. Proposed framework incorporates Intelligent Planning and Modular control modules. Intelligent Planning module of the framework enables the vehicle to intelligently select the most appropriate behavioral characteristics given the perceived operating environment. Subsequently, Modular control module reduces the search space of overtaking trajectories through an SVM based learning approach. These trajectories are then examined for possible future time collision using Velocity Obstacle. It employs non linear time scaling that provides for continuous trajectories in the space of linear and angular velocities to achieve continuous curvature overtaking maneuvers respecting velocity and acceleration bounds. Further time scaling also can scale velocities to avoid collisions and can compute a time optimal trajectory for the learned behavior. The preliminary results show the appropriateness of our proposed framework in virtual urban environment.

Abstract

Autonomous navigation of generic monocular quadcopter in the natural environment requires sophisticated mechanism for perception, planning and control. In this work, we have described a framework which performs perception using monocular camera and generates minimum time collision free trajectory and control for any commercial quadcopter flying through cluttered unknown environment. The proposed framework first utilizes supervised learning approach to estimate the dense depth map for video stream obtained from frontal monocular camera. This depth map is initially transformed into Ego Dynamic Space and subsequently, is used for computing locally traversable way-points utilizing binary integer programming methodology. Finally, trajectory planning and control module employs a convex programming technique to generate collision-free trajectory which follows these way-points and produces appropriate control inputs for the quadcopter. These control inputs are computed from the generated trajectory in each update. Hence, they are applicable to achieve closed-loop control similar to model predictive controller. We have demonstrated the applicability of our system in controlled indoors and in unstructured natural outdoors environment.