Matthew Powers

CHIMP, the CMU Highly Intelligent Mobile Platform

We have developed the CHIMP CMU Highly Intelligent Mobile Platform robot as a platform for executing complex tasks in dangerous, degraded, human-engineered environments. CHIMP has a near-human form factor, work-envelope, strength, and dexterity to work effectively in these environments. It avoids the need for complex control by maintaining static rather than dynamic stability. Utilizing various sensors embedded in the robots head, CHIMP generates full three-dimensional representations of its environment and transmits these models to a human operator to achieve latency-free situational awareness. This awareness is used to visualize the robot within its environment and preview candidate free-space motions. Operators using CHIMP are able to select between task, workspace, and joint space control modes to trade between speed and generality. Thus, they are able to perform remote tasks quickly, confidently, and reliably, due to the overall design of the robot and software. CHIMPs hardware was designed, built, and tested over 15i¾?months leading up to the DARPA Robotics Challenge. The software was developed in parallel using surrogate hardware and simulation tools. Over a six-week span prior to the DRC Trials, the software was ported to the robot, the system was debugged, and the tasks were practiced continuously. Given the aggressive schedule leading to the DRC Trials, development of CHIMP focused primarily on manipulation tasks. Nonetheless, our team finished 3rd out of 16. With an upcoming year to develop new software for CHIMP, we look forward to improving the robots capability and increasing its speed to compete in the DRC Finals.

Value-Based Communication Preservation for Mobile Robots

Value-Based Communication Preservation (VBCP) is a behavior-based, computationally efficient approach to maintaining line-of-sight RF communication between members of robot teams in the context of other tasks. The goal of VBCP is, at each time step, to reactively choose a direction in which to move that provides the best communication quality of service with the rest of the team. VBCP uses information about other robots, real-time quality of service measurements and an a priori map of the environment to approximate an optimal direction in an efficient manner. Here, VBCP maintains communication between members of a robotic team while traversing an urban environment in formation. Quantitative and qualitative results are demonstrated in simulation and physical robot teams.

Learning from Examples in Unstructured, Outdoor Environments

In this paper, we present a multi-pronged approach to the “Learning from Example” problem. In particular, we present a framework for integrating learning into a standard, hybrid navigation strategy, composed of both plan-based and reactive controllers. Based on the classification of colors and textures as either good or bad, a global map is populated with estimates of preferability in conjunction with the standard obstacle information. Moreover, individual feedback mappings from learned features to learned control actions are introduced as additional behaviors in the behavioral suite. A number of real-world experiments are discussed that illustrate the viability of the proposed method.

Executive decision support

One challenge facing coordination and deployment of unmanned aerial vehicles (UAVs) today is the amount of human involvement needed to carry out a successful mission. Currently, control and coordination of UAVs typically involves multiple operators to control a single agent. The aim of this article is to invert this relationship, enabling a single pilot to control and coordinate a group of UAVs. Furthermore, decision support is provided to the pilot to facilitate effective control of the UAV team. In the scenario envisioned in this article, the human operator (the pilot) is operating along-side a team of UAVs. The pilot communicates with the UAV team remotely and controls the UAV team to execute a surveillance mission.

A Modular, Hybrid System Architecture for Autonomous, Urban Driving

Autonomous navigation in urban environments inevitably leads to having to switch between various, sometimes conflicting control tasks. Sting Racing, a collaboration between Georgia Tech and SAIC, has developed a modular control architecture for this purpose and thispaperdescribestheoperationanddefinitionofthisarchitecturethroughso-callednested hybrid automata. We show how to map the requirements associated with the DARPA Urban GrandChallengeontothesenestedautomataandillustratetheiroperationthroughanumber of experimental results.

An optimal timing approach to controlling multiple UAVs

In this paper we address the problem of having a single operator control a team of unmanned aerial vehicles (UAVs). This is achieved by having the team execute a leader-follower coordinated behavior, where the leader is responsible for the execution of the high-level mission. The operator interacts with the system by selecting a leader and a decision support mechanism is provided whereby the system computes the best choice of leader in the current situation. This feedback is obtained through a novel, receding horizon optimal timing control that computes an on-line estimate as to the relative merits of selecting different vehicles as leaders. The method is implemented in a dynamic, 3D simulation environment, illustrating the soundness of the proposed approach.

Control-driven mapping and planning

Layered hybrid controllers typically include a planner at the top level with reactive control at the lower levels. The planner considers the state of the robot in a global context. The low-level controllers consider only the local environment of the robot and are able to operate at a high frequency to ensure the safety of the robot. Also, it is often the case that the low-level controllers consider more aspects of the robots state (e.g. kinematic constraints) than the planner. The consideration of such constraints at the planning level would prohibitively increase the state space the planner must consider and, accordingly, its running time and complexity. In this paper, we investigate how we can take advantage at the planning level of domain knowledge encapsulated in the lower level controllers, and we introduce a feedback mechanism that enables low-level controllers to influence the high-level planner.

Improving Multirobot Multitarget Tracking by Communicating Negative Information

In this paper, we consider the sensor fusion problem for a team of robots, each equipped with monocular color cameras, cooperatively tracking multiple ambiguous targets. In addition to coping with sensor noise, the robots are unable to cover the entire environment with their sensors and may be out numbered by the targets. We show that by explicitly communicating negative information (i.e. where robots don’t see targets), tracking error can be reduced significantly in most instances. We compare our system to a baseline system and report results.

A learning approach to integration of layers of a hybrid control architecture

Hybrid deliberative-reactive control architectures are a popular and effective approach to the control of robotic navigation applications. However, the design of said architectures is difficult, due to the fundamental differences in the design of the reactive and deliberative layers of the architecture. We propose a novel approach to improving system-level performance of said architectures, by improving the deliberative layers model of the reactive layers execution of its plans through the use of machine learning techniques. Quantitative and qualitative results from a physics-based simulator are presented.

Scene understanding for a high-mobility walking robot

High-mobility walking robots offer unique capabilities in complex off-road environments where wheeled vehicles are not able to travel. However, these environments can also pose significant autonomous navigation challenges. Key steps in planning a safe path for the robot autonomously include estimating the height of the support ground surface - which is often occluded by vegetation - and classifying the terrain and obstacles above the ground surface. This paper describes the development and experimental evaluation of a terrain classification and ground surface height estimation system to support autonomous navigation for a high-mobility walking robot. We provide experimental evaluation on an extensive, manually-labeled dataset collected from geographically diverse sites over a 28-month period.