Previous research has presented work on sensor requirements, specifications, and testing, to evaluate the feasibility of increasing autonomous vehicle system speeds. Discussions included the theoretical background for determining sensor requirements, and the basic test setup and evaluation criteria for comparing existing and prototype sensor designs. This paper will present and discuss the continuation of this work. In particular, this paper will focus on analyzing the problem via a real-world comparison of various sensor technology testing results, as opposed to previous work that utilized more of a theoretical approach. LADAR/LIDAR, radar, visual, and infrared sensors are considered in this research. Results are evaluated against the theoretical, desired perception specifications. Conclusions for utilizing a suite of perception sensors, to achieve the goal of doubling ground vehicle speeds, is also discussed.
As robotic ground systems advance in capabilities and begin to fulfill new roles in both civilian and military life, the limitation of slow operational speed has become a hindrance to the wide-spread adoption of these systems. For example, military convoys are reluctant to employ autonomous vehicles when these systems slow their movement from 60 miles per hour down to 40. However, these autonomous systems must operate at these lower speeds due to the limitations of the sensors they employ. Robotic Research, with its extensive experience in ground autonomy and associated problems therein, in conjunction with CERDEC/Night Vision and Electronic Sensors Directorate (NVESD), has performed a study to specify system and detection requirements; determined how current autonomy sensors perform in various scenarios; and analyzed how sensors should be employed to increase operational speeds of ground vehicles. The sensors evaluated in this study include the state of the art in LADAR/LIDAR, Radar, Electro-Optical, and Infrared sensors, and have been analyzed at high speeds to study their effectiveness in detecting and accounting for obstacles and other perception challenges. By creating a common set of testing benchmarks, and by testing in a wide range of real-world conditions, Robotic Research has evaluated where sensors can be successfully employed today; where sensors fall short; and which technologies should be examined and developed further. This study is the first step to achieve the overarching goal of doubling ground vehicle speeds on any given terrain.
The U.S. Army Tank Automotive Research, Development and Engineering Center (TARDEC) held
an autonomous robot competition called CANINE in June 2012. The goal of the competition was to
develop innovative and natural control methods for robots. This paper describes the winning
technology, including the vision system, the operator interaction, and the autonomous mobility. The
rules stated only gestures or voice commands could be used for control. The robots would learn a
new object at the start of each phase, find the object after it was thrown into a field, and return the
object to the operator. Each of the six phases became more difficult, including clutter of the same
color or shape as the object, moving and stationary obstacles, and finding the operator who moved
from the starting location to a new location. The Robotic Research Team integrated techniques in
computer vision, speech recognition, object manipulation, and autonomous navigation. A multi-filter
computer vision solution reliably detected the objects while rejecting objects of similar color or
shape, even while the robot was in motion. A speech-based interface with short commands provided
close to natural communication of complicated commands from the operator to the robot. An
innovative gripper design allowed for efficient object pickup. A robust autonomous mobility and
navigation solution for ground robotic platforms provided fast and reliable obstacle avoidance and
course navigation. The research approach focused on winning the competition while remaining
cognizant and relevant to real world applications.