About this Research Topic
Due to climate change and social unrest, risks arising from natural disasters and man-made incidents are ever-increasing. Therefore, it is essential to enhance disaster risk resilience in modern societies, not only to ensure the safety of citizens and reduce damages to critical infrastructure, but also to ensure the safety of first responders during rescue operations.
Since disaster-struck areas can become impervious to first responders, causing delays in the rescue operation or even danger to them, unmanned vehicles have become more attractive for preparation and response to disasters. However, to deploy these technologies at a larger scale, more effort must be invested in increasing the operational autonomy and capabilities of unmanned vehicles.
Increasing the operational autonomy of unmanned vehicles requires the ability to sense their surroundings for enhanced situation awareness and navigate autonomously in an unknown (or partially known) dynamic environment, which are essential capabilities when operating in a disaster-struck area. Novel sensor technologies and multi-sensor data fusion techniques can enable unmanned vehicles to operate in harsh, GNSS denied environments.
Enhancing the operational capabilities of unmanned vehicles calls for improved planning, sequential decision-making, and multi-agent coordination. AI methods can contribute more intelligent decision-making support, thereby allowing for more complex operations, where the deployment of multiple, or even a swarm of unmanned vehicles, is necessary.
During disaster response, First Responders are often operating in confined environments where sharing a space with unmanned vehicles can become extremely challenging. Collaborative capabilities for improved interaction of humans and unmanned vehicles to improve manipulation and enhance intervention capabilities are of great importance.
In this Research Topic, we intend to compile the latest research findings on the following themes, but not limited to:
- Novel sensor techniques and sensor fusion algorithms to be integrated on unmanned vehicles deployed for disaster response
- AI Algorithms, frameworks, and systems for automated planning, sequential decision-making, multi-agent coordination etc. of unmanned vehicles in disaster areas
- Algorithms and methods for motion control of robots to be deployed in disaster areas (to overcome the physically challenging environment at a disaster site, e.g. uneven grounds due to debris for ground vehicles, stormy weather for aerial vehicles, etc.)
- Collaborative capabilities for improved interaction of humans and unmanned vehicles in shared spaces
- Reporting on field validation tests for unmanned technologies in realistic environments that emulate a disaster-struck area
- Review articles on topics related to unmanned technologies for enhancing disaster resilience
Since disaster-struck areas can become impervious to first responders, causing delays in the rescue operation or even danger to them, unmanned vehicles have become more attractive for preparation and response to disasters. However, to deploy these technologies at a larger scale, more effort must be invested in increasing the operational autonomy and capabilities of unmanned vehicles.
Increasing the operational autonomy of unmanned vehicles requires the ability to sense their surroundings for enhanced situation awareness and navigate autonomously in an unknown (or partially known) dynamic environment, which are essential capabilities when operating in a disaster-struck area. Novel sensor technologies and multi-sensor data fusion techniques can enable unmanned vehicles to operate in harsh, GNSS denied environments.
Enhancing the operational capabilities of unmanned vehicles calls for improved planning, sequential decision-making, and multi-agent coordination. AI methods can contribute more intelligent decision-making support, thereby allowing for more complex operations, where the deployment of multiple, or even a swarm of unmanned vehicles, is necessary.
During disaster response, First Responders are often operating in confined environments where sharing a space with unmanned vehicles can become extremely challenging. Collaborative capabilities for improved interaction of humans and unmanned vehicles to improve manipulation and enhance intervention capabilities are of great importance.
In this Research Topic, we intend to compile the latest research findings on the following themes, but not limited to:
- Novel sensor techniques and sensor fusion algorithms to be integrated on unmanned vehicles deployed for disaster response
- AI Algorithms, frameworks, and systems for automated planning, sequential decision-making, multi-agent coordination etc. of unmanned vehicles in disaster areas
- Algorithms and methods for motion control of robots to be deployed in disaster areas (to overcome the physically challenging environment at a disaster site, e.g. uneven grounds due to debris for ground vehicles, stormy weather for aerial vehicles, etc.)
- Collaborative capabilities for improved interaction of humans and unmanned vehicles in shared spaces
- Reporting on field validation tests for unmanned technologies in realistic environments that emulate a disaster-struck area
- Review articles on topics related to unmanned technologies for enhancing disaster resilience
Keywords: AI, Emergency, Disaster Resilience, First Responders, Autonomous Robots, Rescue Operations
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
