Passive sensor evaluation for unmanned ground vehicle mud detection

Rankin, Arturo L.; Matthies, Larry

doi:10.1002/rob.20341

Cited by 24 publications

(18 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Direct detection techniques have been studied using passive sensors, e.g. multispectral, short-wave infrared, mid-wave infrared, long-wave infrared, polarisation or stereo sensors [35]. Images taken from the sensors, through their visible spectral bands, are used to detect mud.…”

Section: State Of the Artmentioning

confidence: 99%

Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers

Jiménez

Muñoz

Márquez

2019

Reliability Engineering & System Safety

View full text Add to dashboard Cite

show abstract

Section: State Of the Artmentioning

confidence: 99%

Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers

Jiménez

Muñoz

Márquez

2019

Reliability Engineering & System Safety

View full text Add to dashboard Cite

show abstract

“…Commercial cleaning robots are also available and widely used [1, 15,16]. Many researchers have focused on outdoor mud and dirt detection on roads for unmanned ground robots and vehicles [17].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Algorithm for Cleaning Robots Using Vision Sensors

Ravankar

Watanabe

et al. 2019

The 6th International Electronic Conference on Sensors and Applications

View full text Add to dashboard Cite

In recent years, cleaning robots like Roomba have gained popularity. These cleaning robots have limited battery power, and therefore, efficient cleaning is important. Efforts are being undertaken to improve the efficiency of cleaning robots. Most of the previous works have used on-robot cameras, developed dirt detection sensors which are mounted on the cleaning robot, or built a map of the environment to clean periodically. However, a critical limitation of all the previous works is that robots cannot know if the floor is clean or not unless they actually visit that place. Hence, timely information is not available if the room needs to be cleaned. To overcome such limitations, we propose a novel approach that uses external cameras, which can communicate with the robots. The external cameras are fixed in the room and detect if the floor is untidy or not through image processing. The external camera detects if the floor is untidy, along with the exact areas, and coordinates of the portions of the floor that must be cleaned. This information is communicated to the cleaning robot through a wireless network. Thus, cleaning robots have access to a `bird’s-eye view’ of the environment for efficient cleaning. In this paper, we demonstrate the dirt detection using external camera and communication with robot in actual scenarios.

show abstract

“…Under RCTA, JPL performed an evaluation of several passive sensors for mud detection, including a Miricle 110KS LWIR camera [13]. Fig.…”

Section: Water and Mud Detectionmentioning

confidence: 99%

Unmanned ground vehicle perception using thermal infrared cameras

Rankin

Huertas

Matthies

et al. 2011

Unmanned Systems Technology XIII

View full text Add to dashboard Cite

The ability to perform off-road autonomous navigation at any time of day or night is a requirement for some unmanned ground vehicle (UGV) programs. Because there are times when it is desirable for military UGVs to operate without emitting strong, detectable electromagnetic signals, a passive only terrain perception mode of operation is also often a requirement. Thermal infrared (TIR) cameras can be used to provide day and night passive terrain perception. TIR cameras have a detector sensitive to either mid-wave infrared (MWIR) radiation (3-5µm) or long-wave infrared (LWIR) radiation (7-14µm). With the recent emergence of high-quality uncooled LWIR cameras, TIR cameras have become viable passive perception options for some UGV programs. The Jet Propulsion Laboratory (JPL) has used a stereo pair of TIR cameras under several UGV programs to perform stereo ranging, terrain mapping, tree-trunk detection, pedestrian detection, negative obstacle detection, and water detection based on object reflections. In addition, we have evaluated stereo range data at a variety of UGV speeds, evaluated dual-band TIR classification of soil, vegetation, and rock terrain types, analyzed 24 hour water and 12 hour mud TIR imagery, and analyzed TIR imagery for hazard detection through smoke. Since TIR cameras do not currently provide the resolution available from megapixel color cameras, a UGV's daytime safe speed is often reduced when using TIR instead of color cameras. In this paper, we summarize the UGV terrain perception work JPL has performed with TIR cameras over the last decade and describe a calibration target developed by General Dynamics Robotic Systems (GDRS) for TIR cameras and other sensors.

show abstract

Passive sensor evaluation for unmanned ground vehicle mud detection

Cited by 24 publications

References 25 publications

Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers

Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers

An Efficient Algorithm for Cleaning Robots Using Vision Sensors

Unmanned ground vehicle perception using thermal infrared cameras

Contact Info

Product

Resources

About