In multi-agent scenarios, there can be a disparity in the quality of position estimation amongst the various agents. Here,
we consider the case of two agents - a leader and a follower - following the same path, in which the follower has a significantly
better estimate of position and heading. This may be applicable to many situations, such as a robotic "mule"
following a soldier. Another example is that of a convoy, in which only one vehicle (not necessarily the leading one) is
instrumented with precision navigation instruments while all other vehicles use lower-precision instruments. We present
an algorithm, called Follower-derived Heading Correction (FDHC), which substantially improves estimates of the
leader's heading and, subsequently, position. Specifically, FHDC produces a very accurate estimate of heading errors
caused by slow-changing errors (e.g., those caused by drift in gyros) of the leader's navigation system and corrects those
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.
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.
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.