Author: Ravi Shukla
Abstract: Adaptive motion planning has emerged as a critical aspect of robotics, particularly for humanoid and industrial robots operating in dynamic and uncertain environments. Unlike conventional motion planning algorithms, adaptive methods enable robots to modify their paths and trajectories in realtime, responding to changing obstacles, human interactions, or task requirements. This paper reviews recent developments in adaptive motion planning, highlighting algorithms, sensors, control strategies, and practical applications. The discussion includes humanoid robots’ gait and balance adaptation, industrial manipulators’ path optimization, and learning-based methods. Challenges such as computational complexity, real-time constraints, and robustness in unstructured environments are also explored. Finally, future research directions emphasize hybrid approaches combining model-based planning, machine learning, and human-in-the-loop systems.
Keywords: Adaptive motion planning, humanoid robots, industrial robots, realtime trajectory optimization, machine learning, dynamic environments.
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