Solving a Path Planning Problem in a Partially Known Environment using a Swarm Algorithm
This addresses navigation challenges for autonomous vehicles in uncertain terrains, but it is incremental as it builds on existing swarm and sensor-based methods.
The paper tackles path planning for an Autonomous Ground Vehicle in partially known environments by combining global planning using a biomimetic swarm algorithm with local online adjustments, achieving successful simulation on the Player-Stage-Gazebo platform.
This paper proposes a path planning strategy for an Autonomous Ground Vehicle (AGV) navigating in a partially known environment. Global path planning is performed by first using a spatial database of the region to be traversed containing selected attributes such as height data and soil information from a suitable spatial database. The database is processed using a biomimetic swarm algorithm that is inspired by the nest building strategies followed by termites. Local path planning is performed online utilizing information regarding contingencies that affect the safe navigation of the AGV from various sensors. The simulation discussed has been implemented on the open source Player-Stage-Gazebo platform.