Comprehensive motion control law for an articulated robotic platform

BACKGROUND: Controlling robotic platform (RP) with multiple servo drives in difficult conditions, such as areas of man-made or natural disasters, requires significant effort from the operator. Therefore, automating the motion control of RP can improve task efficiency and reduce the operator’s workload, especially when overcoming large obstacles. AIM: Enhancement of the mobility of a robotic platform by developing a comprehensive motion control law focused on automating obstacle navigation in low-visibility conditions. METHODS: To create the motion control law, a computer simulation model, enabling the study of RP dynamics on a surface with a complex profile in a close to “real-time” mode, was developed. The proposed control law is based on a finite-state machine approach and includes four modes: manual control, automated obstacle overcoming, transport mode, and stepping mode. A key feature of this control law is the use of the environmental data obtained by analyzing force factors at the wheel axles during motion. This approach allows the developed control law to be applied in low-visibility conditions, unlike the algorithms relying on computer vision, which can be crucial for operation in disaster zones. The effectiveness of the developed control law was evaluated by overcoming typical obstacles (ledges, cliffs, ditches) through a comparison of automated and manual control results in virtual tests. RESULTS: The proposed control law demonstrates significant advantages when using the automated obstacle-overcoming mode compared to manual RP control, both in terms of motion energy efficiency and maneuver execution time (average velocity). Additionally, the use of a force-sensing wheel system and automated drive control ensures better safety, even in low-visibility conditions. CONCLUSION: The proposed control law significantly improves obstacle navigation efficiency, reducing the operator’s workload and enhancing RP safety in challenging environments. Further research involves conducting field tests to adapt the system to real-world robotic systems.