Tumbling robots are simple platforms that are able to traverse large obstacles relative to their size, at the cost of being difficult to control. Existing control methods apply only a subset of possible robot motions and make the assumption of flat terrain. Reinforcement learning (RL) allows for the development of sophisticated control schemes that can adapt to diverse … [Read more...] about Tumbling Robot Control Using Reinforcement Learning: An Adaptive Control Policy That Transfers Well to the Real World
Collision avoidance
A Cable-Driven Hyperredundant Manipulator: Obstacle-Avoidance Path Planning and Tension Optimization
Manipulators with a hyperredundant and large aspect ratio are becoming more commonly used to detect complex and narrow spaces. The hyperredundant feature confers advantages for such manipulator types in comparison to traditional manipulators. However, they also introduce path-planning challenges. Due to the characteristics of hyperredundancy, there are countless inverse … [Read more...] about A Cable-Driven Hyperredundant Manipulator: Obstacle-Avoidance Path Planning and Tension Optimization
Safety and Efficiency in Robotics: The Control Barrier Functions Approach
This article aims at presenting an introductory overview of the theoretical framework of control barrier functions (CBFs) and of their application to the design of safety-related controllers for robotic systems. The article starts by describing the basic concepts of CBFs and how they can be used to build optimization problems embedding CBF-based constraints, whose solutions … [Read more...] about Safety and Efficiency in Robotics: The Control Barrier Functions Approach
