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Recently, mobile robots have been used in several applications, such as inspection, surveillance, exploration of unknown environments, among others. For example, the Vale's speleology group acquired a robotic platform, named EspeleoRobô, capable of moving on rough terrain, exploring and mapping natural caves. Although EspeloRobô achieved its goal in several field tests, we have faced repeated operational challenges regarding loss of communication between the control base and the robot. A possible solution is to implement autonomous navigation. Thus, an embedded instrumentation and control system would guide the robot to improve the exploration and prevent it from damage. Therefore, this paper evaluates path planning techniques to optimize the robot exploration on rough terrain. We represent the environment using triangle meshes modeled as a graph and generate optimal paths with the Dijkstra's algorithm considering the following metrics as cost function: traveled distance, terrain traversability, and robot power consumption. Lastly, this paper proposes a cost function that accounts for multiple goals during the path planning, and the user can set the trade-off between them through weights. Thus, the algorithm evaluates the relevance of each metric while finding paths connecting the start and goal positions.
Santos et al. (Thu,) studied this question.