Efficient Environment Guided Approach for Exploration of Complex Environments Remote inspection of a complex environment is a difficult, time consuming task for human operators to perform. The need to manually avoid obstacles whilst considering other performance factors i.e. time taken, joint effort and information gained represents significant challenges to continuous operation. This paper proposes an autonomous robotic solution for exploration of an unknown, complex environment using a high DoF robot arm with an eye in hand depth sensor. The main contribution of this work is a new strategy to find the next best view by evaluating frontier regions of the map to maximise coverage, in contrast to many current approaches which densely sample joint or workspace configurations of the robot. Multiple utility functions were evaluated that showed different behaviours. Our results indicated that the presented algorithm can explore an arbitrary environment efficiently while optimising various performance criteria based on the utility function chosen, application constraints and the desires of the user.

An autonomous exploration algorithm using environmentrobot interacted traversability analysis Autoexploration is a task for selfdriving robots to explore unknown environments, which becomes much complicated when they move on irregular outdoor terrains. To improve the situation, a new frontierbased exploration algorithm is presented in this paper. It starts from original 3D cloud points of the environment to analyze the traversability of the scanned area, and further provides a reachability map to mark all map grid cells as reachable, dangerous or unknown. Frontier candidates are obtained from the reachable map, then clustered and reduced using an improved Kmeans. Finally, the target of next exploration step is selected from the frontiers left by evaluating their travel cost. The algorithm is validated on an irregular outdoor terrain and shows the capability for a field robot to explore on an irregular terrain.