华中农业大学讯（秦丽）近日，信息学院沙灜和秦丽的研究成果“Active Dynamic Load Adaptation for Quadruped Locomotion on Complex Terrain”被机器人领域国际会议 IEEE International Conference on Robotics and Automation（ICRA 2026）录用。
当四足机器人在复杂地形执行物资运输、搜索救援等任务时，维持其自身稳定性是一个巨大挑战。特别是当负载具有“主动性”和“动态性”时——例如在机器人背部加装一个正在作业的机械臂，其质量分布和质心会随着机械臂的运动实时发生变化。这种主动负载产生的动态扰动，与复杂地形引起的姿态扰动叠加，形成了复杂的“地形-负载”双重扰动。
当前，主流负载平衡方法通常将负载视为静态或被动的质量块，忽视了负载主动运动带来的实时力矩干扰，难以应对这种未知的动态载荷变化。针对这一关键挑战，研究团队提出一种主动动态负载建模方法及基于动力学模型的强化学习框架。
该研究首次对主动动态负载建模，利用逆动力学模型学习并捕获主动负载的动态特征（如关节位置、速度和力矩等），从而实现对未知负载扰动的隐式建模。同时，方法引入前向动力学模型以预测环境地形的变化，通过这种双动力学模型的协同作用，机器人能仅依靠本体感受信息，在运动中同步感知地形扰动并预测负载干扰，实现实时的平衡控制优化。
大量仿真与真机实验结果表明，该方法在应对 0-3kg 不同强度的动态负载干扰时，其基座平整度和横滚角偏差等稳定性指标均显著优于现有主流算法。在面对机械臂进行前/后、左/右、旋转及随机等多种运动模式时，四足机器人均表现出极强鲁棒性，证明该技术在实际复杂作业场景下拥有巨大应用潜力。
华中农业大学信息学院研究生肖易敏、本科生李佃中为论文共同第一作者，沙灜和秦丽为论文共同通讯作者。
ICRA 是国际机器人技术领域规模最大、最具影响力的国际学术会议，由 IEEE 机器人与自动化学会主办，也是中国人工智能学会推荐国际学术会议-智能机器人与系统的A类会议。
【摘要】
Quadruped robots show important potential for load-carrying tasks due to their terrain adaptability, and a unique challenge of these tasks is to maintain quadrupedal stability when the load has active and dynamic characteristics. The load’s mass and center of mass change dynamically, rather than being integrated as a whole-body component of the quadruped. Unlike traditional load-carrying tasks, where the load is typically passive and its influence on the robot’s movement is predictable and static, active dynamic loads can actively alter the robot’s balance control in real-time, imposing load disturbances on the robot’s locomotion. These load disturbances, when combined with the fundamental attitude changes induced by complex terrain, introduce dual dynamic disturbances to the robot. To address these dual disturbances, we propose an active dynamic load modeling approach that captures the active and dynamic characteristics of the load, enabling the robot to adapt to the real-time changes in load movement. This approach is integrated into a Reinforcement Learning (RL) framework that leverages dynamic models: an Inverse Dynamic Model (IDM) which learns the dynamic characteristics of the active load, and a Forward Dynamic Model (FDM) which predicts the effects of complex terrain on the robot’s motion, enabling synchronous adaptation to both types of dynamic disturbances. Extensive comparative simulations and physical experiments across diverse terrains, with active dynamic load of varying movements, demonstrate the effectiveness of our method in enhancing balance control and adaptability.
审核：沙灜
原创文章，作者：荆楚之窗，如若转载，请注明出处：https://www.jingchucn.com/85963/
