Key Takeaways & Executive Findings
- •• Reviews pivotal technologies for robot motion planning in contact tasks, including environmental recognition, trajectory generation, and sim-to-real transfer. • Highlights the role of AI and embodied intelligence in advancing robot motion planning towards greater intelligence and automation. • Emphasizes the need for adaptability and precision in robots to handle dynamic industrial environments. • Identifies challenges and future directions for intelligent motion planning in industrial automation.
Abstract
With the swift advancement of industrial automation, robots have emerged as an essential component in emerging industries and high-end equipment, thereby propelling industrial production towards greater intelligence and efficiency. This paper reviews the pivotal technologies for motion planning of robots engaged in contact tasks within industrial automation contexts, encompassing environmental recognition, trajectory generation strategies, and sim-to-real transfer. Environmental recognition technology empowers robots to accurately discern objects and obstacles in their operational environment. Trajectory generation strategies formulate optimal motion paths based on environmental data and task specifications. Sim-to-real transfer is committed to effectively translating strategies from simulated environments to actual production, thereby diminishing the discrepancies between simulation and reality. The article also delves into the application of artificial intelligence in robot motion planning and how embodied intelligence models catalyze the evolution of robotics technology towards enhanced intelligence and automation. The paper concludes with a synthesis of the methodologies addressing this challenge and a perspective on the myriad challenges that warrant attention.
1. Introduction
In the contemporary manufacturing landscape, automated production systems integrated with robotic arms have emerged as pivotal to enhancing manufacturing efficiency and product excellence. Tracing the evolution from the onset of the third industrial revolution [1] to the current era characterized by Industry 4.0 [2], robotic technology has consistently been at the heart of industrial automation advancements (as depicted in Figure 1). Within the spectrum of tasks performed by robotic arms, contact-intensive operations such as manipulation, assembly, grinding, and welding (illustrated in Figure 2) are crucial for the realization of automated production processes [3–5]. These operations necessitate that the robot or its end effector engage in sustained and frequent contact with the objects or environments within its interactive domain. This requirement underscores the need for the robot to exhibit a high level of agility and precision, and also to possess the adaptability to navigate the dynamic and ever-evolving production settings and demands [6].
The successful execution of these tasks depends not only on the hardware performance of the robot, but also on the intelligence of its software system. With the complexity of production tasks and the diversification of working environments, traditional industrial robots face great challenges in performing these tasks. For instance, during robotic welding operations, accurate control over the welding path is crucial; however, it is equally essential that the system can dynamically adjust welding parameters in real-time to accommodate varying materials and conditions. In such scenarios, the ability to adapt to complex environments, fully comprehend the task at hand, and implement more flexible robot motion planning becomes increasingly valuable and competitive [7]. Conventional motion planning techniques frequently rely on predefined trajectories and parameters, which are inadequate for navigating the intricacies of dynamic production settings. As a result, substantial time investments are required for programming and validation, hindering efficiency and flexibility in robotic manufacturing applications [8]. With the development of artificial intelligence technology, the integration of artificial intelligence into the robotic manufacturing process has been internationally recognized as the main driving force for the transformation of traditional factories and the realization of higher-level automated production [9]. Therefore, making robot motion planning more intelligent while ensuring operational
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Shibo Jin, Kaichen Ke, Boyang Gao, Li Fu, Xingrong Huang (2025). A Comprehensive Review of Key Technologies for Robot Motion Planning in Contact Tasks in Industrial Automation Scenarios. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01356-x
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Frequently Asked Questions
What are the key technologies for robot motion planning in contact tasks?
The key technologies include environmental recognition, trajectory generation strategies, and sim-to-real transfer, which enable robots to perceive their environment, plan optimal paths, and transfer learned strategies from simulation to real-world production.
How does artificial intelligence enhance robot motion planning?
AI enhances robot motion planning by enabling adaptive and intelligent decision-making, allowing robots to handle complex and dynamic environments, and facilitating the integration of embodied intelligence models for more autonomous and efficient operations.
What are the challenges in sim-to-real transfer for robot motion planning?
Challenges include bridging the reality gap between simulation and real-world physics, ensuring robustness to variations, and achieving reliable performance in unstructured industrial settings.
Why is environmental recognition important in industrial automation?
Environmental recognition allows robots to accurately identify objects and obstacles, which is crucial for safe and precise motion planning in dynamic and cluttered industrial environments.
What is the significance of this review for industrial automation?
This review synthesizes current methodologies and highlights future directions, providing a comprehensive reference for researchers and engineers to advance intelligent robot motion planning, thereby improving efficiency and flexibility in automated production.
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