Mine detection is a dangerous and physically demanding task that is very well-suited for robotic applications. In the
experiment described in this paper, we try to determine whether a remotely-operated robotic mine detection system
equipped with a hand-held mine detector can match the performance of a human equipped with a hand-held mine
detector. To achieve this objective, we developed the Robotic Mine Sweeper (RMS). The RMS platform is capable of
accurately sweeping and mapping mine lanes using common detectors, such as the Minelab F3 Mine Detector or the
AN/PSS-14. The RMS is fully remote controlled from a safe distance by a laptop via a redundant wireless connection
link. Data collected from the mine detector and various sensors mounted on the robot are transmitted and logged in real-time
to the remote user interface and simultaneously graphically displayed. In addition, a stereo color camera mounted
on top of the robot sends a live picture of the terrain. The system plays audio feedback from the detector to further
enhance the user's situational awareness. The user is trained to drag and drop various icons onto the user interface map
to locate mines and non-mine clutter objects. We ran experiments with the RMS to compare its detection and false alarm
rates with those obtained when the user physically sweeps the detectors in the field. The results of two trials: one with
the Minelab F3, the other with the Cyterra AN/PSS-14 are presented here.
Payloads for small robotic platforms have historically been designed and implemented as platform and task specific
solutions. A consequence of this approach is that payloads cannot be deployed on different robotic platforms without
substantial re-engineering efforts. To address this issue, we developed a modular countermine payload that is designed
from the ground-up to be platform agnostic. The payload consists of the multi-mission payload controller unit (PCU)
coupled with the configurable mission specific threat detection, navigation and marking payloads. The multi-mission
PCU has all the common electronics to control and interface to all the payloads. It also contains the embedded processor
that can be used to run the navigational and control software. The PCU has a very flexible robot interface which can be
configured to interface to various robot platforms. The threat detection payload consists of a two axis sweeping arm and
the detector. The navigation payload consists of several perception sensors that are used for terrain mapping, obstacle
detection and navigation. Finally, the marking payload consists of a dual-color paint marking system. Through the multimission
PCU, all these payloads are packaged in a platform agnostic way to allow deployment on multiple robotic
platforms, including Talon and Packbot.
The Black Knight is a 12-ton, C-130 deployable Unmanned Ground Combat Vehicle (UGCV). It was developed to demonstrate how unmanned vehicles can be integrated into a mechanized military force to increase combat capability while protecting Soldiers in a full spectrum of battlefield scenarios. The Black Knight is used in military operational tests that allow Soldiers to develop the necessary techniques, tactics, and procedures to operate a large unmanned vehicle within a mechanized military force. It can be safely controlled by Soldiers from inside a manned fighting vehicle, such as the Bradley Fighting Vehicle. Black Knight control modes include path tracking, guarded teleoperation, and fully autonomous movement. Its state-of-the-art Autonomous Navigation Module (ANM) includes terrain-mapping sensors for route planning, terrain classification, and obstacle avoidance. In guarded teleoperation mode, the ANM data, together with automotive dials and gages, are used to generate video overlays that assist the operator for both day and night driving performance. Remote operation of various sensors also allows Soldiers to perform effective target location and tracking. This document covers Black Knight's system architecture and includes implementation overviews of the various operation modes. We conclude with lessons learned and development goals for the Black Knight UGCV.
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