KEYWORDS: Mobile robots, Motion models, Kinematics, Optimization (mathematics), Systems modeling, Statistical modeling, LIDAR, Fusion energy, Data modeling, Space robots
Mobile robots operating in the field need to be aware of the terrain and use this information to select the proper motion planning modality. For example, in very benign terrain it might be desirable to navigate to a goal following the shortest path. However, on challenging terrains, such maneuvers may be prohibitively expensive in terms of energy consumption. This paper summarizes field experiment results corresponding to a comparison between distance optimal and energy optimal motion planning for a skid-steered robot on different terrains. The results show that on average there is substantive energy savings associated with energy optimal motion planning.
Motion planning for legged machines such as RHex-type robots is far less developed than motion planning for wheeled vehicles. One of the main reasons for this is the lack of kinematic and dynamic models for such platforms. Physics based models are difficult to develop for legged robots due to the difficulty of modeling the robot-terrain interaction and their overall complexity. This paper presents a data driven approach in developing a kinematic model for the X-RHex Lite (XRL) platform. The methodology utilizes a feed-forward neural network to relate gait parameters to vehicle velocities.
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