To operate autonomously, unmanned ground vehicles (UGVs) must be able to identify the loadbearing surface of the terrain (i.e. the ground) and obstacles. Current sensing techniques work well for structured environments such as urban areas, where the roads and obstacles are usually highly predictable and well-defined. However, autonomous navigation on forested terrain presents many new challenge...

The effectiveness of a legged robot’s gait is highly dependent on the ground cover of the terrain the robot is traversing. It is therefore advantageous for a legged robot to adapt its behaviour to suit the environment. In order to achieve this, the robot must be able to detect and classify the type of ground cover it is traversing. We present a novel approach for ground cover classification tha...

Exploration Rovers 2 3 4 Christopher A. Brooks, Karl Iagnemma 5 Department of Mechanical Engineering 6 Massachusetts Institute of Technology 7 Cambridge, MA 02139 8 [email protected], [email protected] 9 10 Abstract 11 12 For future planetary exploration missions, improvements in autonomous rover 13 mobility have the potential to increase scientific data return by providing safe 14 access to geol...

In this thesis, we consider the problem of having a mobile robot autonomously learn to perceive differences between terrains. The targeted application is for terrain identification. Robust terrain identification can be used to enhance the capabilities of mobile systems, both in terms of locomotion and navigation. For example, a legged amphibious robot that has learned to differentiate sand from...

Autonomous navigation in unstructured environments is a complex task and an active area of research in mobile robotics. Unlike urban areas with lanes, road signs, and maps, the environment around our robot is unknown and unstructured. Such an environment requires careful examination as it is random, continuous, and the number of perceptions and possible actions are infinite. We describe a terra...

When an outdoor mobile robot traverses different types of ground surfaces, different types of vibrations are induced in the body of the robot. These vibrations can be used to learn a discrimination between different surfaces and to classify the current terrain. Recently, we presented a method that uses Support Vector Machines for classification, and we showed results on data collected with a ha...

Conclusion Jumping of legged mobile robots has been a highly motivated research area. When a running robot encounters obstacles comparable to its body height, jumping is one of the most effective ways to overcome them. Also, if the robots can jump over gaps or crevices, the mobility of robots in a wild field would be enhanced drastically.The jumping performance is dependent on the terrain prope...

This paper introduces a new classification scheme called “open-ended texture classification.” The standard approach for texture classification is to use a closed n-class classifier based on the Bayesian paradigm. These perform supervised classification, whereby all the texture classes have to be predefined. We propose a new texture classification scheme, one that does not require a complete set...

In order to improve autonomous mobility of planetary rover, many works have recently focused on non-geometric features of surrounding terrain such as color, texture, and wheel-soil interaction mechanics (Dima et al., 2004; Halatci et al., 2007; Helmick et al., 2009; Ishigami et al., 2007). To tackle with the issue, most of them propose to utilize on-board sensors such as multi-spectral imagers,...