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A new approach to evolutionary robotics is presented. Neural networks are abstracted and supplanted by a system of ordinary differential equations that govern the changes in controller outputs. The equations are evolved as trees using an evolutionary algorithm based on symbolic regression in genetic programming. Initial proof-of-concept experiments are performed using a simulated two-wheeled robot...
Robots, in their most general embodiment, can be complex systems trying to negotiate and manipulate an unstructured environment. They ideally require an ‘intelligence’ that reflects our own. Artificial evolutionary algorithms are often used to generate a high-level controller for single and multi robot scenarios. But evolutionary algorithms, for all their advantages, can be very computationally intensive...
Autonomous robotic excavation has often been limited to a single robotic platform using a specified excavation vehicle. This paper presents a novel method for developing scalable controllers for use in multirobot scenarios and that do not require human defined operations scripts nor extensive modeling of the kinematics and dynamics of the excavation vehicles. Furthermore, the control system does not...
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