The Technology Transfer project employs a spiral development process to enhance the functionality and autonomy of mobile systems in the Joint Robotics Program (JRP) Robotic Systems Pool (RSP). The approach is to harvest prior and on-going developments that address the technology needs identified by emergent in-theatre requirements and users of the RSP. The component technologies are evaluated on a transition platform to identify the best features of the different approaches, which are then integrated and optimized to work in harmony in a complete solution. The result is an enabling mechanism that continuously capitalizes on state-of-the-art results from the research environment to create a standardized solution that can be easily transitioned to ongoing development programs. This paper focuses on particular research areas, specifically collision avoidance, simultaneous localization and mapping (SLAM), and target-following, and describes the results of their combined integration and optimization over the past 12 months.Keywords: robotics, autonomy, collision avoidance, vision-tracking, SLAM, augmented virtuality, technology transfer. BACKGROUNDThe objective is to enhance the functionality and autonomy of mobile robotic systems in the Joint Robotics Program (JRP) Robotic Systems Pool through a spiral-development process that harvests existing component technologies for optimization. The Tactical Mobile Robot (TMR) program, sponsored by the Defense Advanced Research Projects Agency (DARPA), was transferred to Space and Naval Warfare Systems Center, San Diego (SSC San Diego) at the end of FY-02 to facilitate the transition of TMR-funded technology into ongoing JRP development efforts. SSC San Diego worked with a variety of DARPA contractors to extract relevant aspects of their research and port it to ongoing projects and systems associated with the JRP Robotic Systems Pool. The continuing search for supporting technologies has naturally expanded to other government research activities, academia, and industry to further foster emergent technology-transfer opportunities (Figure 1). Accordingly, the JRP Technology Transfer Program has teamed with a number of organizations with similar ambitions, such as the Idaho National Laboratory (INL), to assist in the coordinated development, evaluation, and sharing of robotics technology. INL has a direct interest in autonomous robots for use in a variety of DOE missions, including homeland defense and critical infrastructure protection. This synergistic teaming between SSS San Diego and INL has two obvious advantages: 1) The INL Robotics Group, with similar objectives and experience, substantially augments the available manpower resources, allowing more technology options to be evaluated; and, 2) active DOE involvement opens up another major conduit for exporting the results into relevant government applications.An equally important objective of the program is to also transition relevant technology enhancements into the private sector, in order to enhance the supporting ...

Sensors commonly mounted on small unmanned ground vehicles (UGVs) include visible light and thermal cameras, scanning LIDAR, and ranging sonar. Sensor data from these sensors is vital to emerging autonomous robotic behaviors. However, sensor data from any given sensor can become noisy or erroneous under a range of conditions, reducing the reliability of autonomous operations. We seek to increase this reliability through data fusion. Data fusion includes characterizing the strengths and weaknesses of each sensor modality and combining their data in a way such that the result of the data fusion provides more accurate data than any single sensor. We describe data fusion efforts applied to two autonomous behaviors: leader-follower and human presence detection. The behaviors are implemented and tested in a variety of realistic conditions. KEYWORDS: robotics, unmanned systems, data fusion, intelligent behaviors, computer vision 1. BACKGROUND Technology Transfer ProjectThe JGRE Technology Transfer Project (TechTXFR) managed by Space and Naval Warfare Systems Center, San Diego (SSC San Diego) seeks to enhance the functionality (ability to perform more tasks) and autonomy (with less human intervention) of teleoperated systems 1 . The objective is to expedite advancement of the technologies needed to produce an autonomous robot that can robustly perform in battlefield situations. Instead of developing new capabilities from scratch, the approach is to assess the technology readiness levels (TRLs) of component technologies (i.e., mapping, object recognition, motion-detection-on-the-move) developed under a variety of past and ongoing R&D efforts (such as the DARPA Tactical Mobile Robot program). The most mature algorithms are integrated and optimized into cohesive behavior architectures and then ported to various platforms used by the warfighter for further evaluation in operational environments. Contributing sources of component technologies include the Idaho National Laboratory (INL), NASA's Jet PropulsionLaboratory, Carnegie-Mellon University (CMU), Stanford Research Institute International (SRI), University of Michigan, Brigham Young University, University of California San Diego, and University of Texas Austin, as well as other SSC San Diego projects (e.g., Man Portable Robotic System 2 and the ROBART series 3 ). Starting in FY-03, the approach was to harvest existing indoor navigation technologies developed by various players and assess their different approaches to dead reckoning, obstacle detection/avoidance, mapping, localization, and path planning. The details of these focus areas will not be discussed in this paper but can be found in previous project publications 4 . The best features of the more promising solutions have now been integrated into an optimal system, giving an operator the ability to send an autonomous platform into an unknown indoor area and accurately map the surroundings. An augmented virtuality representation of the environment is derived, fusing real-time sensor information with the evo...

The fusion of multiple behavior commands and sensor data into intelligent and cohesive robotic movement has been the focus of robot research for many years. Sequencing low level behaviors to create high level intelligence has also been researched extensively. Cohesive robotic movement is also dependent on other factors, such as environment, user intent, and perception of the environment. In this paper, a method for managing the complexity derived from the increase in sensors and perceptions is described. Our system uses fuzzy logic and a state machine to fuse multiple behaviors into an optimal response based on the robot's current task. The resulting fused behavior is filtered through fuzzy logic based obstacle avoidance to create safe movement. The system also provides easy integration with any communications protocol, plug-and-play devices, perceptions, and behaviors. Most behaviors and the obstacle avoidance parameters are easily changed through configuration files. Combined with previous work in the area of navigation and localization a very robust autonomy suite is created.Keywords: robotics, autonomy, obstacle avoidance, arbiter, fuzzy logic, state machine BACKGROUNDThe simplest and most basic form of robot control is tele-operation. During tele-operation, the rules for movement and arbitration among system inputs are relatively simple: user input is mapped directly to motor controller output. As robot autonomy increases, the requirements of the system architecture become increasingly complex. For example, in reflexive tele-operation, data from multiple sensors must be arbitrated with user input and combined into an appropriate output 1,2,3 . When moving from tele-operation to reflexive tele-operation, the space of possible inputs increases linearly and is easily handled by conventional programming techniques.However, the addition of new behaviors and technologies under the Robotics Technology Transfer project 4 at SPAWAR Systems Center, San Diego (SSC San Diego) results in a nonlinear explosion of possible variables to arbitrate. Examples of such behaviors include leader-follower, building exploration, and searching for radioactive sources. These high-level behaviors depend on underlying component behaviors, such as obstacle avoidance, and operate concurrently with the autonomy levels described above. Complexity increases because the high-level behaviors also often need to influence the underlying component behaviors. For example, the building exploration behavior requires the robot to avoid obstacles encountered in its path. However, the leader-follower behavior requires the robot to maintain a close proximity to the leader. Clearly the obstacle avoidance in these two cases must operate differently. A robot with multiple levels of autonomy and various high level and low level behaviors must handle a potentially huge number of internal states in order to behave appropriately in any combination of autonomy level and behavior. If not well designed, the addition of just one new behavior can result in...

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Many envisioned applications of mobile robotic systems require the robot to navigate in complex urban environments. This need is particularly critical if the robot is to perform as part of a synergistic team with human forces in military operations. Historically, the development of autonomous navigation for mobile robots has targeted either outdoor or indoor scenarios, but not both, which is not how humans operate. This paper describes efforts to fuse component technologies into a complete navigation system, allowing a robot to seamlessly transition between outdoor and indoor environments. Under the Joint Robotics Program's Technology Transfer project, empirical evaluations of various localization approaches were conducted to assess their maturity levels and performance metrics in different exterior/interior settings. The methodologies compared include Markov localization, global positioning system, Kalman filtering, and fuzzy-logic. Characterization of these technologies highlighted their best features, which were then fused into an adaptive solution. A description of the final integrated system is discussed, including a presentation of the design, experimental results, and a formal demonstration to attendees of the Unmanned Systems Capabilities Conference II in San Diego in December 2005. BACKGROUNDThe first significant military use of robotic systems on the battlefield occurred in FY-04, when the Space and Naval Warfare Systems Center, San Diego (SSC San Diego) assisted the OSD Joint Robotics Program (JRP) in procuring and fielding 163 robots to address the ongoing threat of improvised explosive devices (IEDs) in Iraq and Afghanistan. The number of robotic systems grew to approximately 2000 in FY-05 and is expected to reach 4000 in FY-06. To date, however, all of these robots are strictly teleoperated devices (i.e., remote-controlled) with no onboard intelligence, and thus require intense operator involvement and high-bandwidth communications links.To address these shortcomings, the JRP Technology Transfer Project managed by SSC San Diego seeks to enhance the functionality (ability to perform more tasks) and autonomy (with less human intervention) of these teleoperated systems. 1 The objective is to expedite advancement of the technologies needed to produce an autonomous robot that can robustly perform in battlefield situations. Instead of developing new capabilities from scratch, the approach is to assess the technology readiness levels (TRLs) of component technologies (i.e., mapping, object recognition, motion-detection-onthe-move) developed under a variety of past and ongoing R&D efforts (such as the DARPA Tactical Mobile Robot program). The most mature algorithms are integrated and optimized into a cohesive behavior architecture and then ported to various platforms used by the warfighter for further evaluation in operational environments. Contributing sources of component technologies include the Idaho