About this Research Topic
Robot coverage path planning is the computation of paths that fully cover a given map. Various robot tasks require efficient coverage path planning. For example, agricultural robots prefer a path that fully covers the field, indoor cleaning robots should clean a room without missing any areas, milling or painting machines need to travel on a 3D model with full coverage, UAV and AUV are deployed for terrain coverage.
While coverage path planning has been studied for a long time, recent advances in computational power, wireless communication and sensory technology, as well as new algorithms in artificial intelligence and computational geometry, bring new breath to the coverage path planning problem. We look forward to seeing more techniques and applications of coverage path planning in this era of robotics and AI.
Robot coverage path planning is not a hard problem if we only need an approximate coverage or an inefficient way to cover the given region. The coverage path planning problem also gets harder if the space to be covered has an irregular boundary or a complex 3D geometry. If we have multiple robots to cover the map or there are many dynamic obstacles in the region, it is also challenging to control the interaction between the robots and the environment.
Recent advances allow heavier computation while smarter algorithms are found in computational geometry and artificial intelligence. In this Research Topic, we aim to explore novel ways of improving the flexibility and efficiency of coverage path planning. We are particularly interested in new applications of coverage path planning which utilise more advanced commodity robots and new algorithms and applications to tackle the existing problems in coverage path planning.
Topics of interest include, but are not limited to, the following:
• Algorithms for coverage path planning, approximate/exact coverage path planning
• Computational geometry (cellular decomposition, travel sales man problem, …) and Artificial intelligence (swarm intelligence, machine learning, …) for robot coverage path planning
• Multi-robot coverage path planning
• Coverage path planning for Autonomous Underwater Vehicle, Unmanned Aerial Vehicles and other Mobile/Field robots
• Coverage path planning for machine milling, 3D printing, 3D Surface reconstruction and other 3D object manipulation tasks
• Coverage path planning with collision avoidance, energy saving constraints or other boundary constraints
• Applications of coverage path planning, including robot inspection, rescue, terrain coverage or other new applications
Keywords: Coverage Path planning, Path planning, Motion Planning, Mobile Robots, Field Robots, machine learning, swarm intelligence
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