Under the Urban Environment Exploration project, the Space and Naval Warfare Systems Center Pacic (SSC-
PAC) is maturing technologies and sensor payloads that enable man-portable robots to operate autonomously
within the challenging conditions of urban environments. Previously, SSC-PAC has demonstrated robotic capabilities to navigate and localize without GPS and map the ground
oors of various building sizes.1 SSC-PAC has
since extended those capabilities to localize and map multiple multi-story buildings within a specied area. To
facilitate these capabilities, SSC-PAC developed technologies that enable the robot to detect stairs/stairwells,
maintain localization across multiple environments (e.g. in a 3D world, on stairs, with/without GPS), visualize
data in 3D, plan paths between any two points within the specied area, and avoid 3D obstacles. These technologies have been developed as independent behaviors under the Autonomous Capabilities Suite, a behavior
architecture, and demonstrated at a MOUT site at Camp Pendleton. This paper describes the perceptions and
behaviors used to produce these capabilities, as well as an example demonstration scenario.
Under various collaborative efforts with other government labs, private industry, and academia, SPAWAR Systems
Center Pacific (SSC Pacific) is developing and testing advanced autonomous behaviors for navigation, mapping, and
exploration in various indoor and outdoor settings. As part of the Urban Environment Exploration project, SSC
Pacific is maturing those technologies and sensor payload configurations that enable man-portable robots to
effectively operate within the challenging conditions of urban environments. For example, additional means to
augment GPS is needed when operating in and around urban structures. A MOUT site at Camp Pendleton was
selected as the test bed because of its variety in building characteristics, paved/unpaved roads, and rough terrain.
Metrics are collected based on the overall system's ability to explore different coverage areas, as well as the
performance of the individual component behaviors such as localization and mapping. The behaviors have been
developed to be portable and independent of one another, and have been integrated under a generic behavior
architecture called the Autonomous Capability Suite. This paper describes the tested behaviors, sensors, and
behavior architecture, the variables of the test environment, and the performance results collected so far.
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.
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.
The Technology Transfer project employs a spiral development process to enhance the functionality and autonomy of mobile robot systems in the Joint Robotics Program (JRP) Robotic Systems Pool by converging existing component technologies onto a transition platform for optimization. An example of this approach is the implementation of advanced computer vision algorithms on small mobile robots. We demonstrate the implementation and testing of the following two algorithms useful on mobile robots: 1) object classification using a boosted Cascade of classifiers trained with the Adaboost training algorithm, and 2) human presence detection from a moving platform. Object classification is performed with an Adaboost training system developed at the University of California, San Diego (UCSD) Computer Vision Lab. This classification algorithm has been used to successfully detect the license plates of automobiles in motion in real-time. While working towards a solution to increase the robustness of this system to perform generic object recognition, this paper demonstrates an extension to this application by detecting soda cans in a cluttered indoor environment. The human presence detection from a moving platform system uses a data fusion algorithm which combines results from a scanning laser and a thermal imager. The system is able to detect the presence of humans while both the humans and the robot are moving simultaneously. In both systems, the two aforementioned algorithms were implemented on embedded hardware and optimized for use in real-time. Test results are shown for a variety of environments.
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.
Weapon payloads are becoming increasingly important components of unmanned ground vehicles (UGVs). However weapon payloads are extremely difficult to teleoperate. This paper explores the issues involved with automating several aspects of the operations of a weapon payload. These operations include target detection, acquisition, and tracking. Various approaches to these issues are discussed, and the development and results from two different working prototype systems developed at Space and Naval Warfare Systems Center, San Diego (SSC San Diego) are presented. One approach employs a motion-based scheme for target identification, while the second employs an appearance based scheme. Target selection, arming and firing remain teleoperated in both systems.
Unmanned vehicles perform critical mission functions. Today, fielded unmanned vehicles have restricted operations as a single asset controlled by a single operator. In the future, however, it is envisioned that multiple unmanned air, ground, surface and underwater vehicles will be deployed in an integrated unmanned (and "manned") team fashion in order to more effectively execute complex mission scenarios. To successfully facilitate this transition from single platforms to an integrated unmanned system concept, it is essential to first develop the required base technologies for multi-vehicle mission requirements, as well as test and evaluate such technologies in tightly controlled field experiments. Under such conditions, advances in unmanned technologies and associated system configurations can be empirically evaluated and quantitatively measured against relevant performance metrics. A series of field experiments will be conducted for unmanned force protection system applications. A basic teaming scenario is: Unmanned aerial vehicles (UAVs) detect a target of interest on the ground; the UAVs cue unmanned ground vehicles (UGVs) to the area; the UGVs provide on-ground evaluation and assessment; and the team of UAVs and UGVs execute the appropriate level of response. This paper details the scenarios and the technology enablers for experimentation using unmanned protection systems.
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.
In addition to the challenges of equipping a mobile robot with the appropriate sensors, actuators, and processing electronics necessary to perform some useful function, there coexists the equally important challenge of effectively controlling the system’s desired actions. This need is particularly critical if the intent is to operate in conjunction with human forces in a military application, as any low-level distractions can seriously reduce a warfighter’s chances of survival in hostile environments. Historically there can be seen a definitive trend towards making the robot smarter in order to reduce the control burden on the operator, and while much progress has been made in laboratory prototypes, all equipment deployed in theatre to date has been strictly teleoperated. There exists a definite tradeoff between the value added by the robot, in terms of how it contributes to the performance of the mission, and the loss of effectiveness associated with the operator control unit. From a command-and-control perspective, the ultimate goal would be to eliminate the need for a separate robot controller altogether, since it represents an unwanted burden and potential liability from the operator’s perspective. This paper introduces the long-term concept of a supervised autonomous Warfighter’s Associate, which employs a natural-language interface for communication with (and oversight by) its human counterpart. More realistic near-term solutions to achieve intermediate success are then presented, along with actual results to date. The primary application discussed is military, but the concept also applies to law enforcement, space exploration, and search-and-rescue scenarios.
Current man-portable robotic systems are too heavy for troops to pack during extended missions in rugged terrain and typically require more user support than can be justified by their limited return in force multiplication or improved effectiveness. As a consequence, today’s systems appear organically attractive only in life-threatening scenarios, such as detection of chemical/biological/radiation hazards, mines, or improvised explosive devices. For the long term, significant improvements in both functionality (i.e., perform more useful tasks) and autonomy (i.e., with less human intervention) are required to increase the level of general acceptance and, hence, the number of units deployed by the user. In the near term, however, the focus must remain on robust and reliable solutions that reduce risk and save lives. This paper describes ongoing efforts to address these needs through a spiral development process that capitalizes on technology transfer to harvest applicable results of prior and ongoing activities throughout the technical community.