Autonomous 3D exploration with dynamic obstacles : Towards Intelligent Navigation and Collision Avoidance for Autonomous 3D Exploration with dynamic obstacles

Abstract: The advancements within robotics in recent years has increased the demand for sophisticated algorithms that can tackle the challenges associated with building robust and safe autonomous systems. The objective of 3D exploration is to enable a robot to explore an unknown environment with a high degree of accuracy while minimizing time and path length. Planners such as Receding Horizon Next Best View Planner (RH-NBVP) and Autonomous Exploration Planner (AEP) have been widely studied in static environments. However, in dynamic environments where obstacles like pedestrians or vehicles can appear, existing solutions either use dynamic motion planners for obstacle avoidance or simply use reactive behavior to avoid collisions like Dynamic Exploration Planner (DEP). This thesis examines how dynamic obstacles can be included in the planning process while performing 3D exploration to make more advantageous decisions regarding both efficiency and overall safety. Considering the uncertain dynamics in the environment is crucial for a robot to act safely, and hence be deployable in a real-world scenario. The suggested solution, Dynamic Autonomous Exploration Planner (DAEP), has been evaluated with other 3D-exploration planners as benchmarks. Experiments demonstrate that static planners struggle in dynamic environments. However, most collisions can be avoided with a simple reactive motion planner. This thesis presents an extension to the static planner AEP that considers dynamic obstacles. The proposed solution samples safer routes to a goal and takes into account historical observations of dynamic obstacles. Finally, a novel potential volumetric information gain is implemented to predict explored volume in the future. The results demonstrate that the extensions to AEP enhance safety planning and improve coverage compared to regular AEP and DEP.