C. S. George Lee

Real-time moving object recognition and tracking using computation offloading

Mobile robots are widely used for computation-intensive tasks such as surveillance, moving object recognition and tracking. Existing studies perform the computation entirely on robot processors or on dedicated servers. The robot processors are limited by their computation capability; real-time performance may not be achieved. Even though servers can perform tasks faster, the communication time between robots and servers is affected by variations in wireless bandwidths. In this paper, we present a system for realtime moving object recognition and tracking using computation offloading. Offloading migrates computation to servers to reduce the computation time on the robots. However, the migration consumes additional time, referred as communication time in this paper. The communication time is dependent on data size exchanged and the available wireless bandwidth. We estimate the computation and communication needed for the tasks and choose to execute them on robot processors or servers to minimize the total execution time, in order to satisfy real-time constraints.

Closed-form inverse kinematic joint solution for humanoid robots

This paper focuses on developing a consistent methodology for deriving a closed-form inverse kinematic joint solution of a general humanoid robot. Most humanoid-robot researchers resort to iterative methods for inverse kinematics using the Jacobian matrix to avoid the difficulty of finding a closed-form joint solution. Since a closed-form joint solution, if available, has many advantages over iterative methods, we have developed a novel reverse decoupling mechanism method by viewing the kinematic chain of a limb of a humanoid robot in reverse order and then decoupling it into the positioning and orientation mechanisms, and finally utilizing the inverse transform technique in deriving a consistent joint solution for the humanoid robot. The proposed method presents a simple and efficient procedure for finding the joint solution for most of the existing humanoid robots. Extensive computer simulations of the proposed approach on a Hubo KHR-4 humanoid robot show that it can be applied easily to most humanoid robots with slight modifications.

CLOSED-FORM INVERSE KINEMATIC POSITION SOLUTION FOR HUMANOID ROBOTS

This paper focuses on developing a consistent methodology for deriving a closed-form inverse kinematic joint solution of a humanoid robot with decision equations to select a proper solution from multiple solutions. Most researchers resort to iterative methods for inverse kinematics using the Jacobian matrix to avoid the difficulty of finding a closed-form joint solution. Since a closed-form joint solution, if available, has many advantages over iterative methods, we have developed a novel reverse-decoupling method by viewing the kinematic chain of a limb of a humanoid robot in reverse order and then decoupling it into the positioning and orientation mechanisms, and finally utilizing the inverse-transform technique to derive a consistent joint solution for the humanoid robot. The proposed method presents a simple and efficient procedure for finding the joint solution for most of the existing humanoid robots. Extensive computer simulations of the proposed approach on a Hubo KHR-4 humanoid robot show that it can be applied easily to most humanoid robots such as HOAP-2, HRP-2 and ASIMO humanoid robots with slight modifications.

Mobility-aware ad hoc routing protocols for networking mobile robot teams

Mobile multi-robot teams are useful in many critical applications such as search and rescue. Explicit communication among robots in such mobile multi-robot teams is useful for the coordination of such teams as well as exchanging data. Since many applications for mobile robots involve scenarios in which communication infrastructure may be damaged or unavailable, mobile robot teams frequently need to communicate with each other via ad hoc networking. In such scenarios, low-overhead and energy-efficient routing protocols for delivering messages among robots are a key requirement. Two important primitives for communication are essential for enabling a wide variety of mobile robot applications. First, unicast communication (between two robots) needs to be provided to enable coordination and data exchange. Second, in many applications, group communication is required for flexible control, organization, and management of the mobile robots. Multicast provides a bandwidth-efficient communication method between a source and a group of robots. In this paper, we first propose and evaluate two unicast routing protocols tailored for use in ad hoc networks formed by mobile multi-robot teams: Mobile robot distance vector (MRDV) and mobile robot source routing (MRSR). Both protocols exploit the unique mobility characteristics of mobile robot networks to perform efficient routing. Our simulation study show that both MRDV and MRSR incur lower overhead while operating in mobile robot networks when compared to traditional mobile ad hoc network routing protocols such as DSR and AODV. We then propose and evaluate an efficient multicast protocol mobile robot mesh multicast (MRMM) for deployment in mobile robot networks. MRMM exploits the fact that mobile robots know what velocity they are instructed to move at and for what distance in building a long lifetime sparse mesh for group communication that is more efficient. Our results show that MRMM provides an efficient group communication mechanism that can potentially be used in many mobile robot application scenarios.

Robust ladder-climbing with a humanoid robot with application to the DARPA Robotics Challenge.

This paper presents an autonomous planning and control framework for humanoid robots to climb general ladder- and stair-like structures. The approach consists of two major components: 1) a multi-limbed locomotion planner that takes as input a ladder model and automatically generates a whole-body climbing trajectory that satisfies contact, collision, and torque limit constraints; 2) a compliance controller which allows the robot to tolerate errors from sensing, calibration, and execution. Simulations demonstrate that the robot is capable of climbing a wide range of ladders and tolerating disturbances and errors. Physical experiments demonstrate the DRC-Hubo humanoid robot successfully mounting, climbing, and dismounting an industrial ladder similar to the one intended to be used in the DARPA Robotics Challenge Trials.

Energy-time-efficient adaptive dispatching algorithms for ant-like robot systems

In this paper, we investigate energy-time-efficient dispatching methods for ant-like robots to cover an unmapped region effectively. These ant-like robots have very limited energy and sensor ability, making them practical and inexpensive to build. Our dispatching model was based on bio-inspired algorithms from an ant colony system. We assumed that all the ant-like robots start from their home starting point, the nest, and the region is composed of floor tiles that can be modelled as vertices in a graph. In this dispatching system, the ant-like robots leave pheromone on the tiles and use this information to cover the region. We developed and analyzed two different adaptive dispatching algorithms with different communication methods to the nest. We further compared these two adaptive dispatching algorithms with two non-adaptive methods. Extensive computer simulations validated the proposed adaptive algorithms, showing that they can dispatch ant-like mobile robots to cover an unmapped region with energy-time efficiency.

Anatomical-plane-based representation for human-human interactions analysis

In this paper, we present a novel view-invariant, motion-pose geometric descriptor (MPGD) as a human-human interaction representation to capture the semantic meaning of body-parts between two interacting humans. The proposed MPGD representation is based on utilizing the concept of anatomical planes to construct a motion profile and a pose profile for each human. Those two profiles are then concatenated to form a descriptor for the two interacting humans. Using the proposed MPGD representation, we study two problems related to human-human interaction analysis, namely human-human interaction classification and prediction. For the human-human interaction classification problem, we propose a hierarchical classification framework consisting of a representation layer and three classification layers. The classification framework aims to realize what is the performed interaction in an input video by understanding how and when each individual performed sub-activities to each other over time. The human-human interaction prediction problem aims to predict the class of ongoing human-human interaction at its early stages. To do so, we propose a prediction framework that utilizes the proposed MPGD to construct an accumulated histograms-based representation for an ongoing interaction. The accumulated histograms of MPGDs are then used to train a set of support-vector-machine classifiers with a probabilistic output to predict the class of an ongoing interaction. In order to evaluate our proposed MPGD representation and both the classification and the prediction frameworks, we utilize a Microsoft Kinect sensor to capture human-human interactions in a video dataset that consists of 12 interactions performed by 12 individuals. We evaluate the performance of our proposed classification framework and compare the results with an appearance-based representation and a representation that combines both the MPGD representation and the appearance-based representation. On the one hand, our proposed MPGD representation performance has shown promising results compared to the appearance-based representation with an average accuracy of 94.86% in classifying human-human interactions. On the other hand, human-human interaction prediction framework has achieved an average prediction accuracy of 82.46% with only 50% of the interaction video being observed. HighlightsWe propose a view-invariant motion-pose geometric descriptor (MPGD) as a representation of human-human interaction (HHI).The MPGD utilizes the concept of anatomical planes to model the interacting humans.The MPGD can capture the semantic meaning of body-parts for two interacting humans.Using the MPGD, we developed frameworks to analyze HHI from two perspectives: classification and prediction from an input video.

Motion planning and control of ladder climbing on DRC-Hubo for DARPA Robotics Challenge.

This video presents our preliminary work towards addressing the ladder climbing event in DARPA Robotics Challenge (DRC) using DRC-Hubo robot. A ladder-climbing motion planner is developed which generates a collision-free, stable quasi-static trajectory for execution. Compliance control is enabled on arm joints to compensate for the calibration error, modeling error and control error. We have demonstrated that DRC-Hubo can robustly climb a variety of ladders in simulation and successfully climb a ship ladder on the hardware.

A 3D-point-cloud feature for human-pose estimation

Estimating human poses is an important step towards developing robots that can understand human motions and improving their cognitive capabilities. This paper presents a geometric feature for estimating human poses from a 3D point cloud input. The proposed feature can be considered as an extension of the idea of visual features, such as color/edge, of color/grayscale images, and it contains the geometric structure of the point cloud. It is derived by arranging the 3D points into a tree structure, which preserves the global and local properties of the 3D points. Shown experimentally, the tree structure (spatial ordering) is particularly important for estimating human poses (i.e., articulated objects). The 3D orientation (pan, tilt and yaw angles) and shape features are then extracted from each node in the tree to describe the geometric distribution of the 3D points. The proposed feature has been evaluated on a benchmark dataset and compared with two existing geometric features. Experimental results show that the proposed feature has the lowest overall error in human-pose estimation.

Lifetime maximization in mobile sensor networks with energy harvesting

This paper investigates mobility strategies of mobile robots to improve the lifetime of a mobile sensor network with energy harvesting capability. The network lifetime problem is formulated as a nonlinear non-convex optimization problem, which is solved distributively by a series of convex approximations and a novel saddle-point computation algorithm. The convergence of the proposed method is guaranteed. Computer simulations showed quick convergence to the optimal solution in most cases, and verified the use of mobility for energy efficiency by showing its significant improvement to the network lifetime and relatively low cost in mobility.