Autonomous robots are becoming a promising solution to perform tasks without supervision in complex environments. In recent years, remarkable advancements have been achieved in independent operation of autonomous robots without human control or intervention. However, the challenge of achieving robust locomotion, sensing, and manipulation in humanoid robots persists. Overcoming this challenge requires three essential components: 1. Integration of smart materials-based actuators and sensors to enhance the level of autonomy and operational efficiency 2. Robust locomotion combined with spatial reasoning to navigate through constrained spaces. 3. Sensor fusion to perceive and respond to moving obstacles and engage in human interaction. By integrating novel findings from these key aspects, the full potential of autonomous robots can be unlocked, enabling them to transition from laboratory experiments to practical contributors in human society.
To become a general-purpose platform to provide services in actual applications, autonomous robots must possess both physical/computational intelligence and advanced control autonomy to interact independently with complex, often unstructured environments.
First, development of smart materials and mechanisms that enable actuation and sensor integration is crucial to enhance multifunctionatilities, payload capacity and battery life of autonomous robots. Likewise, the utilization of direct drive motors with high torque density and energy recovery capabilities plays a significant role in designing more powerful robots.
In addition, model-based numerical optimization has emerged as a prominent approach for real-time control of autonomous robots. The integration of advanced, onsite model predictive control (MPC) into the autonomous robots, such as the Altas robot developed by Boston Dynamics, effectively showcases the remarkable adaptability and resilience of this method in facilitating locomotion and manipulation tasks.
Further, the future prospects of autonomous robots lie in their autonomous decision-making in navigation, sensing and interaction with complex environments. This asks for integrated sensors equipped with sensor fusion techniques to obtain multimodal feedback signals. Hence, the development of efficient sensor fusion techniques like vision-motion control is critical towards the successful deployment of autonomous robots in real-world scenarios.
This Research Topic calls for original research works in the general area related to the development of advanced intelligent autonomous robots. As an interdisciplinary field of study, contributions addressing different aspects of the problem for practical implementation of legged robots are greatly valued. Theoretical studies, practical applications and state-of-the-art reviews in the relevant fields are all encouraged to submit. We welcome papers on a range of subjects, including, but not limited to:
Design and Instrumentation
Integrated actuation and sensing
Smart materials and mechanisms
Power-efficient actuators
Tactile/visual/motion sensors
Advanced Motion Control
Model predictive control (MPC) of legged robots
Humanoid/Quadrupedal whole-body control
Trajectory optimization
Reinforcement learning
Intelligent Perception and Planning
Multi-sensor fusion and state estimation
Dynamic motion planning with moving obstacles
Visual servoing
Terrain-aware locomotion
Keywords:
Autonomous robots, legged robots, smart materials, integrated actuation and sensing, numerical optimization, model predictive control, motion planning, convex optimization, perception, high power density actuator, sensor fusion
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.
Autonomous robots are becoming a promising solution to perform tasks without supervision in complex environments. In recent years, remarkable advancements have been achieved in independent operation of autonomous robots without human control or intervention. However, the challenge of achieving robust locomotion, sensing, and manipulation in humanoid robots persists. Overcoming this challenge requires three essential components: 1. Integration of smart materials-based actuators and sensors to enhance the level of autonomy and operational efficiency 2. Robust locomotion combined with spatial reasoning to navigate through constrained spaces. 3. Sensor fusion to perceive and respond to moving obstacles and engage in human interaction. By integrating novel findings from these key aspects, the full potential of autonomous robots can be unlocked, enabling them to transition from laboratory experiments to practical contributors in human society.
To become a general-purpose platform to provide services in actual applications, autonomous robots must possess both physical/computational intelligence and advanced control autonomy to interact independently with complex, often unstructured environments.
First, development of smart materials and mechanisms that enable actuation and sensor integration is crucial to enhance multifunctionatilities, payload capacity and battery life of autonomous robots. Likewise, the utilization of direct drive motors with high torque density and energy recovery capabilities plays a significant role in designing more powerful robots.
In addition, model-based numerical optimization has emerged as a prominent approach for real-time control of autonomous robots. The integration of advanced, onsite model predictive control (MPC) into the autonomous robots, such as the Altas robot developed by Boston Dynamics, effectively showcases the remarkable adaptability and resilience of this method in facilitating locomotion and manipulation tasks.
Further, the future prospects of autonomous robots lie in their autonomous decision-making in navigation, sensing and interaction with complex environments. This asks for integrated sensors equipped with sensor fusion techniques to obtain multimodal feedback signals. Hence, the development of efficient sensor fusion techniques like vision-motion control is critical towards the successful deployment of autonomous robots in real-world scenarios.
This Research Topic calls for original research works in the general area related to the development of advanced intelligent autonomous robots. As an interdisciplinary field of study, contributions addressing different aspects of the problem for practical implementation of legged robots are greatly valued. Theoretical studies, practical applications and state-of-the-art reviews in the relevant fields are all encouraged to submit. We welcome papers on a range of subjects, including, but not limited to:
Design and Instrumentation
Integrated actuation and sensing
Smart materials and mechanisms
Power-efficient actuators
Tactile/visual/motion sensors
Advanced Motion Control
Model predictive control (MPC) of legged robots
Humanoid/Quadrupedal whole-body control
Trajectory optimization
Reinforcement learning
Intelligent Perception and Planning
Multi-sensor fusion and state estimation
Dynamic motion planning with moving obstacles
Visual servoing
Terrain-aware locomotion
Keywords:
Autonomous robots, legged robots, smart materials, integrated actuation and sensing, numerical optimization, model predictive control, motion planning, convex optimization, perception, high power density actuator, sensor fusion
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.