Acceleration
Related papers: 20
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Acceleration, in the context of robotics and AI, refers to the rate of change of velocity over time — a fundamental physical quantity governing how robots and other dynamic systems move through space. In robotics, acceleration is central to dynamics modeling, trajectory planning, and motion control: engineers compute joint and end-effector accelerations to determine the torques required for precise, smooth, and safe movement of manipulators, mobile robots, and legged systems. Algorithms for trajectory planning often explicitly bound acceleration and jerk (rate of change of acceleration) to protect mechanical components, improve path accuracy, and ensure human safety during human-robot interaction. Acceleration measurements from inertial sensors also enable collision detection, load estimation, and activity monitoring in wearable systems. In reinforcement learning and model-based control, acceleration serves as a state variable or control output that helps agents learn physically realistic policies more efficiently. Understanding and controlling acceleration is essential for achieving high-speed, energy-efficient, and dynamically stable robot behavior across applications ranging from industrial pick-and-place systems to rehabilitation devices and autonomous vehicles.
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