Advances in robotics and autonomous systems have opened new horizons for the scientists by creating new opportunities to explore extreme environments that would previously not have been possible. For example, robots that are deployed to study environmental processes such remote volcanos, monitor the climate variables under the adverse weather conditions, understand underground mines, and explore deep oceans which are all inaccessible or hazardous for the human. Industrial applications can also often be situated in extreme environments such as offshore oil and gas and nuclear industries. In such applications the autonomous robot is expected to complete tasks such as repair and maintenance, exploration, reconnaissance, inspection, and transportation which is either done in isolation or as a team of cooperative robots. Due to the harsh and severe conditions of such environments, designing an advanced robotic system that can endure them is a challenging task. The robot needs to cope with the time-varying, restricted, uncertain, and unstructured nature of the environment to achieve the planning and execution of the tasks. This in turn demands development of advanced, robust and adaptive motion control and navigation algorithms along with machine learning and deep learning algorithms with high cognitive capability for the robot to perceive the surrounding environment effectively. The use of both single and multi-robot platforms can be advantageous depending on the specific application and environment.
This Research Topic aims to address the most recent advances within the development of motion control and navigation systems for the autonomous operation of robots in extreme environments. The main research challenges for the robots operating in such harsh environments includes, but is not limited to the uncertain and time-varying nature of the robot dynamic, lack of a prior knowledge or map within the environment to aid navigation, the interaction of each robot with the environment and other robots in the presence of the environmental constraints and disturbances, kinematic and dynamic constraints of the robot, hardware and software insufficiencies such as limited payload and limited power for an extended period of robot operation in the environment.
The main topics of interest include but are not limited to:
• Design and development of both hardware and software platforms for ground, submersible and aerial robotic systems for exploration, inspection, repair and maintenance in extreme environments. This includes for example, redundant or radiation hardened processors and fault tolerant algorithms.
• Design of Human–Robot Interaction (HRI) algorithms and systems for supervised and safe operation of the robots in the hazardous environment. For example, using advances in virtual reality and development of digital twins will reduce the cognition load or the operator and enhance the level of autonomy.
• Trajectory tracking of robots using nonlinear robust and adaptive control, and model predictive control (MPC) in the presence of constraints and sever uncertainties such as collision with obstacles and degradation of sensors, actuators, and dynamic of the system.
• Formation control of multi robot systems in the presence of uncertainty, including, leader-follower, behavioural based methods, virtual structure methods, and consensus-based methods.
• Navigation algorithms for autonomous operation of a single or multiple robotic system in cluttered hazardous environments. For example, development of navigation algorithms in GPS denied environments for autonomous exploration and mapping.
• Reducing the dependency of the robots in the extreme environment to the remote human operator by development of motion planning and trajectory generation algorithms, including sampling-based techniques (RRT, PRM, ... ), Voronoi diagram (VD), and kino-dynamic approaches.
• Development of sensory subsystem for hazardous environment by including features such as sensor self-calibration and sensor fault detection to address bias, drift, scaling factor and other noises arising from the application and their compensations in the robotic system
Advances in robotics and autonomous systems have opened new horizons for the scientists by creating new opportunities to explore extreme environments that would previously not have been possible. For example, robots that are deployed to study environmental processes such remote volcanos, monitor the climate variables under the adverse weather conditions, understand underground mines, and explore deep oceans which are all inaccessible or hazardous for the human. Industrial applications can also often be situated in extreme environments such as offshore oil and gas and nuclear industries. In such applications the autonomous robot is expected to complete tasks such as repair and maintenance, exploration, reconnaissance, inspection, and transportation which is either done in isolation or as a team of cooperative robots. Due to the harsh and severe conditions of such environments, designing an advanced robotic system that can endure them is a challenging task. The robot needs to cope with the time-varying, restricted, uncertain, and unstructured nature of the environment to achieve the planning and execution of the tasks. This in turn demands development of advanced, robust and adaptive motion control and navigation algorithms along with machine learning and deep learning algorithms with high cognitive capability for the robot to perceive the surrounding environment effectively. The use of both single and multi-robot platforms can be advantageous depending on the specific application and environment.
This Research Topic aims to address the most recent advances within the development of motion control and navigation systems for the autonomous operation of robots in extreme environments. The main research challenges for the robots operating in such harsh environments includes, but is not limited to the uncertain and time-varying nature of the robot dynamic, lack of a prior knowledge or map within the environment to aid navigation, the interaction of each robot with the environment and other robots in the presence of the environmental constraints and disturbances, kinematic and dynamic constraints of the robot, hardware and software insufficiencies such as limited payload and limited power for an extended period of robot operation in the environment.
The main topics of interest include but are not limited to:
• Design and development of both hardware and software platforms for ground, submersible and aerial robotic systems for exploration, inspection, repair and maintenance in extreme environments. This includes for example, redundant or radiation hardened processors and fault tolerant algorithms.
• Design of Human–Robot Interaction (HRI) algorithms and systems for supervised and safe operation of the robots in the hazardous environment. For example, using advances in virtual reality and development of digital twins will reduce the cognition load or the operator and enhance the level of autonomy.
• Trajectory tracking of robots using nonlinear robust and adaptive control, and model predictive control (MPC) in the presence of constraints and sever uncertainties such as collision with obstacles and degradation of sensors, actuators, and dynamic of the system.
• Formation control of multi robot systems in the presence of uncertainty, including, leader-follower, behavioural based methods, virtual structure methods, and consensus-based methods.
• Navigation algorithms for autonomous operation of a single or multiple robotic system in cluttered hazardous environments. For example, development of navigation algorithms in GPS denied environments for autonomous exploration and mapping.
• Reducing the dependency of the robots in the extreme environment to the remote human operator by development of motion planning and trajectory generation algorithms, including sampling-based techniques (RRT, PRM, ... ), Voronoi diagram (VD), and kino-dynamic approaches.
• Development of sensory subsystem for hazardous environment by including features such as sensor self-calibration and sensor fault detection to address bias, drift, scaling factor and other noises arising from the application and their compensations in the robotic system
