Adaptive Motion Planning with EfficientNet and A-Algorithm for Enhanced Robotics Automation in Industrial Applications

In industrial applications, robotics automation should respond instantly to all changes and be safe, while allowing the system to expand as needed. An adaptive way to plan robot motions is proposed by this paper, using the scaleable EfficientNet network for good sensing and the A-Algorithm for effective steering. Blending these techniques allows the system to easily compute paths for free from both stationary and moving obstacles. Some main challenges for industry addressed by this framework include using resources, reducing response times, being energy-savvy and increasing system capacity. Following extensive testing, we found that our algorithm achieved 98% accuracy in finding paths, detected 97% of obstacles, boosted energy efficiency to 92%, decreased latency to 20 milliseconds and improved its scaling by a factor of 3.2. This proposed system performs far better in real time and is more secure compared to current approaches, so it is highly suitable for demanding industrial uses that need quick, dependable robotic navigation and automation.