In order to fully exploit robot motion capabilities in complex environments, robots need to reason about obstacles in a non-binary fashion. In this paper, we focus on the modeling and characterization of pliable materials such as tall vegetation. These materials are of interest because they are pervasive in the real world, requiring the robotic vehicle to determine when to traverse or avoid them. This paper develops and experimentally verifies a template model for vegetation stems. In addition, it presents a methodology to generate predictions of the associated energetic cost incurred by a tracked mobile robot when traversing a vegetation patch of variable density.
Real world motion planning often suffers from the need to replan during execution of the trajectory. This replanning can be triggered as the robot fails to properly track the trajectory or new sensory information is provided that invalidates the planned trajectory. Particularly in the case of many occluded obstacles or in unstructured terrain, replanning is a frequent occurrence. Developing methods to allow the robots to replan efficiently allows for greater operation time and can ensure robot mission success. This paper presents a novel approach that updates heuristic weights of a sampling based A* planning algorithm. This approach utilizes parallel instances of this planner to quickly search through multiple heuristic weights within its allotted replanning time. These weights are employed upon triggered replanning to speed up computation time. The concept is tested on a simulated quadrupedal robot LLAMA with realistic constraints on computation time imposed.
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.