Shengjie Jiao

Road maintenance is fundamental to ensuring safe and comfortable transportation. As critical safety elements, traffic cone robots (TCRs) autonomously secure work zones and guide vehicle flow during road construction and maintenance. This study addresses the formation placement and recovery control of TCRs under environmental and system uncertainties. The TCRs are capable of coordinated formation maneuvers while simultaneously tracking placement and recovery targets within constrained environments. To address these constraints, a diffeomorphism is introduced to systematically mitigate their effects. The uncertainties of the TCRs are bounded, including both matching and mismatching components, with known boundary values belonging to a fuzzy set. Based on this, we propose an adaptive robust control methodology that ensures both uniform boundedness and uniform ultimate boundedness of the TCR system. Additionally, to enhance system performance, we introduce a multi-parameter optimization method based on Nash game theory. The Nash equilibrium, representing the optimal control parameters, is achieved by minimizing the associated cost functions. Comprehensive comparative and ablation analyses are conducted to explicitly distinguish the respective contributions of uncertainty decomposition and Nash-equilibrium-based parameter optimization to robustness and dynamic performance. The performance of the TCR system is illustrated through a series of numerical simulations. These findings provide essential theoretical and technical support for the placement and recovery operations of TCRs in construction road maintenance.

The challenge of effectively managing the formation and recovery of traffic cone robots (TCRs) is addressed by proposing a linear time-varying model predictive control (MPC) strategy. This problem involves coordinating multiple TCR formations within a work area to reach a target location, which is a huge challenge due to the complexity of dynamic coordination. Unlike conventional approaches, our method decomposes the formation control problem into two main components: leader TCR motion planning and follower formation tracking control. The motion planning component involves path and velocity planning to achieve leader trajectory control, which serves as a reference trajectory for the follower. The formation tracking task extends to formation control among multiple robots to achieve the traffic cone robot formation placement and recovery task. To address the TCR input limitation problem, input constraints are considered during the design process of the MPC controllers. The effectiveness and practicality of the proposed control strategy are validated through a series of numerical simulations and physical experiments with TCRs.

A mine dump truck is exposed to heavy load and harsh working environment. When the truck passes over the road bumps, it will cause the body to tilt and the tires to "jump off the ground" (JOTG), which will affect the stability and safety...