Abstract

Efficient and real time segmentation of color images has a variety of importance in many fields of computer vision such as image compression, medical imaging, mapping and autonomous navigation. Being one of the most computationally expensive operation, it is usually done through software implementation using high-performance processors. In robotic systems, however, with the constrained platform dimensions and the need for portability, low power consumption and simultaneously the need for real time image segmentation, we envision hardware parallelism as the way forward to achieve higher acceleration. Field-programmable gate arrays (FPGAs) are among the best suited for this task as they provide high computing power in a small physical area. They exceed the computing speed of software based implementations by breaking the paradigm of sequential execution and accomplishing more per clock cycle operations by enabling hardware level parallelization at an architectural level. In this paper, we propose three novel architectures of a well known Efficient Graph based Image Segmentation algorithm. These proposed implementations optimizes time and power consumption when compared to software implementations. The hybrid design proposed, has notable furtherance of acceleration capabilities delivering atleast 2X speed gain over other implementations, which henceforth allows real time image segmentation that can be deployed on Mobile Robotic systems.

Abstract

Real time calculation of inverse kinematics (IK) with dynamically stable configuration is of high necessity in humanoid robots as they are highly susceptible to lose balance. This paper proposes a methodology to generate joint-space trajectories of stable configurations for solving inverse kinematics using Deep Reinforcement Learning (RL). Our approach is based on the idea of exploring the entire configuration space of the robot and learning the best possible solutions using Deep Deterministic Policy Gradient (DDPG). The proposed strategy was evaluated on the highly articulated upper body of a humanoid model with 27 degree of freedom (DoF). The trained model was able to solve inverse kinematics for the end effectors with 90% accuracy while maintaining the balance in double support phase.

Abstract

The advent of indoor personal mobile robots has clearly demonstrated their utility in assisting humans at various places such as workshops, offices, homes, etc. One of the most important cases in such autonomous scenarios is where the robot has to search for certain objects in large rooms. Exploring the whole room would prove to be extremely expensive in terms of both computing power and time. To address this issue,we demonstrate a fast algorithm to reduce the search space by identifying possible object locations as two classes, namely - Support Structures and Clutter. Support Structures are plausible object containers in a scene such as tables, chairs, sofas, etc. Clutter refers to places where there seem to be several objects but cannot be clearly distinguished. It can also be identified as unorganized regions which can be of interest for tasks such as robot grasping, fetching and placing objects. The primary contribution of this paper is to quickly identify potential object locations using a Support Vector Machine(SVM) learnt over the features extracted from thedepth map and the RGB image of the scene, which further culminates into a densely connected Conditional Random Field(CRF) formulated over the image of the scene. The inference over the CRF leads to assignment of the labels - support structure, clutter, others to each pixel.There have been reliable outcomes even during challenging scenarios such as the support structures being far from the robot. The experiments demonstrate the efficacy and speed of the algorithm irrespective of alterations to camera angles, modifications to appearance change, lighting and distance from locations etc

Abstract

Small and medium sized obstacles such as rocks, small boulders, bricks left unattended on the road can pose hazards for autonomous as well as human driving situations. Many times these objects are too small on the road and go unnoticed on depth and point cloud maps obtained from state of the art range sensors such as 3D LIDAR. We propose a novel algorithm that fuses both appearance and 3D cues such as image gradients, curvature potentials and depth variance into a Markov Random Field (MRF) formulation that segments the scene into obstacle and non obstacle regions. Appearance and depth data obtained from a ZED stereo pair mounted on a Husky robot is used for this purpose. While identifying true positive obstacles such as rocks, large stones accurately our algorithm is simultaneously robust to false positive sources such as appearance changes on the road, papers and road markings. High accuracy detection in challenging scenes such as when the foreground obstacle blends with the background road scene vindicates the efficacy of the proposed formulation.

Abstract

The purpose of this paper is to present the model of a Humanoid robot inspired by Poppy, modified for heavier load capacity and the balancing of humanoid in multiple work environments. The design has been modified in order to use MX-64 servos with more torque capacity than MX-28 servos which were used in original design. We have also redesigned the ankle joint and feet to make it a more accurate human like model. We are using an integrated approach using zero moment point (ZMP) with force sensing resistor (FSR) and center of mass (CoM) to find balance margins of the robot. Balancing and forward bending experiments on robot are conducted by providing some basic motion to the robot and ensuring that the robot is balanced and moving within safe margins using this approach.

Abstract

The primary objective of a robot is to substitute or complement humans in life-endangering situations. The paper deals with the design and analysis of a robot suitable for such Urban Search and Rescue operation (USAR) scenarios. A survey of the existing robots in this field is done and new design considerations have been developed. Further, quasi-static analysis of the robot along a circular stair is performed to confirm whether the design considerations will satisfy versatility as well as mobility, the key requirements of a true uneven terrain robot. Keywords— Compliant Design; Multi-module robot; Nonlinear optimization; 3D Quasi Static Model

Abstract

The purpose of this paper is to present the modelof a Humanoid robot inspired by Poppy, modified for heavier load capacity and the balancing of humanoid in multiple work environments. The design has been modified in order to useMX-64 servos with more torque capacity than MX-28 servos which were used in original design. We have also redesigned the ankle joint and feet to make it a more accurate human like model. We are using an integrated approach using zero moment point (ZMP) with force sensing resistor (FSR) and center of mass (CoM) to find balance margins of the robot. Balancing and forward bending experiments on robot are conducted by providing some basic motion to the robot and ensuring that the robot is balanced and moving within safe margins using this approach.

Abstract

It is a well known result in the vision literature that the motion of independently moving objects viewed by an affine camera lie on affine subspaces of dimension four or less. As a result a large number of the recently proposed motion segmentation algorithms model the problem as one of clustering the trajectory data to its corresponding affine subspace. While these algorithms are elegant in formulation and achieve near perfect results on benchmark datasets, they fail to address certain very key real-world challenges, including perspective effects and motion degeneracies. Within a robotics and autonomous vehicle setting, the relative configuration of the robot and moving object will frequently be degenerate leading to a failure of subspace clustering algorithms. On the other hand, while gestalt-inspired motion similarity algorithms have been used for motion segmentation, in the moving camera case, they tend to over-segment or under-segment the scene based on their parameter values. In this paper we present a principled approach that incorporates the strengths of both approaches into a cohesive motion segmentation algorithm capable of dealing with the degenerate cases, where camera motion follows that of the moving object. We first generate a set of prospective motion models for the various moving and stationary objects in the video sequence by a RANSAC-like procedure. Then, we incorporate affine and long-term gestalt-inspired motion similarity constraints, into a multi-label Markov Random Field (MRF). Its inference leads to an over-segmentation, where each label belongs to a particular moving object or the background. This is followed by a model selection step where we merge clusters based on a novel motion coherence constraint, we call in-frame shear, that tracks the in-frame change in orientation and distance between the clusters, leading to the final segmentation. This oversegmentation is deliberate and necessary, allowing us to assess the relative motion between the motion models which we believe to be essential in dealing with degenerate motion scenarios.We present results on the Hopkins-155 benchmark motion segmentation dataset [27], as well as several on-road scenes where camera and object motion are near identical. We show that our algorithm is competitive with the state-of-the-art algorithms on [27] and exceeds them substantially on the more realistic on-road sequences.

Abstract

Application of wheeled mobile robots has gradually progressed from the confines of structured indoor environments to rough outdoor terrains. Material transport and exploration are some of the few areas where wheeled robots are required to navigate over uneven terrains. Stable and efficient navigation of wheeled robots over uneven terrains requires a framework which can correctly ascertain the stability and maneuverability for a given robot’s state. Most existing works on uneven terrain navigation assume a one-to-one correspondence between postural stability and maneuverability. In this paper, we show that such characterization is incomplete as states having high postural stability may have restricted maneuverability depending on underlying terrain topology. We thus, present a novel metric called Feasible Acceleration Count (FAC), introduced in our earlier works as a unified measure of robot stability and maneuverability. The metric gives the measure of the space of feasible accelerations available to the robot at a given state. The feasibility is decided by a set of inequalities which depends not only on robot’s state but also on surface normals at the wheel ground contact point. This unique feature of the FAC metric makes it a more appropriate choice for motion planning on uneven terrains than metrics like Tip-Over. We further show that since space of feasible accelerations is a direct characterization of the space of possible motions at a given state, the metric FAC, also quantifies the quality of state space exploration achieved at each step of incremental sampling based planners. We build on top of this aspect of FAC and present an incremental trajectory planner with a novel node selection criteria for navigation of generic four wheeled robots and articulated systems like mobile manipulators on uneven terrain.

Abstract

Reasoning about objects in images and videos using 3D representations is re-emerging as a popular paradigm in computer vision. Specifically, in the context of scene understanding for roads, 3D vehicle detection and tracking from monocular videos still needs a lot of attention to enable practical applications. Current approaches leverage two kinds of information to deal with the vehicle detection and tracking problem: (1) 3D representations (eg. wireframe models or voxel based or CAD models) for diverse vehicle skeletal structures learnt from data, and (2) classifiers trained to detect vehicles or vehicle parts in single images built on top of a basic feature extraction step. In this paper, we propose to extend current approaches in two ways. First, we extend detection to a multiple view setting. We show that leveraging information given by feature or part detectors in multiple images can lead to more accurate detection results than single image detection. Secondly, we show that given multiple images of a vehicle, we can also leverage 3D information from the scene generated using a unique structure from motion algorithm. This helps us localize the vehicle in 3D, and constrain the parameters of optimization for fitting the 3D model to image data. We show results on the KITTI dataset, and demonstrate superior results compared with recent state-of-the-art methods, with upto 14.64 % improvement in localization error.