In this article, we propose GPU-Accelerated TRajectory Optimization (GATRO), a framework that casts trajectory optimization as a simplified Semidefinite Programming (SDP) problem and employs a custom parallelized differentiable solver for GPU acceleration. We primarily demonstrate GATRO on tractor-trailer trajectory planning, as it represents one of the most challenging scenarios in autonomous vehicle motion planning due to its non-holonomic kinematics and complex constraints. The trajectory optimization is reformulated as a simplified SDP by leveraging differential flatness for non-holonomic kinematics elimination, approximating constraints (e.g., physical constraints and collision avoidance constraints) as convex polytopes, and coupling piecewise polynomial parameterization with the Markov-Luk´acs theorem. Exploiting the fact that the SDP problem consists of many Positive Semidefinite (PSD) matrices, we develop a parallelized Primal-Dual Hybrid Gradient (PDHG) solver to expedite the solving process. Moreover, by differentiating the Lagrangian of the SDP problem, our solver derives the analytical gradient of the optimal objective with respect to temporal parameters, which is used to further optimize the spatiotemporal trajectory. Numerical simulations show that GATRO outperforms existing trajectory optimization methods in computational time while preserving non-holonomic kinematics and satisfying all constraints. Our PDHG solver demonstrates superior performance compared to state-of-the-art commercial and open-source SDP solvers, particularly for large-scale problems. The approach is validated on a full-scale operational autonomous tractor-trailer.

Wheeled-legged robots combine the efficiency of wheels with the versatility of legs, but face significant energy optimization challenges when navigating diverse environments. In this work, we present a hierarchical control framework that integrates predictive power modeling with residual reinforcement learning to optimize omnidirectional locomotion efficiency for wheeled quadrupedal robots. Our approach employs a novel power prediction network that forecasts energy consumption across different gait patterns over a 1-second horizon, enabling intelligent selection of the most energy-efficient nominal gait. A reinforcement learning policy then generates residual adjustments to this nominal gait, fine-tuning the robot’s actions to balance energy efficiency with performance objectives. Comparative analysis shows our method reduces energy consumption by up to 35% compared to fixed-gait approaches while maintaining comparable velocity tracking performance. We validate our framework through extensive simulations and real-world experiments on a modified Unitree Go1 platform, demonstrating robust performance even under external disturbances. Videos and implementation details are available at https://sites.google.com/view/switching-wpg.

In this paper, we propose a differential-flatness-based controller (DFBC) for precise trajectory tracking of tractor-trailers, particularly during reversing maneuvers, which are challenging due to unstable equilibrium points. The proposed controller leverages the differential flatness property of tractor-trailers, equivalently transforming the nonlinear kinematics into a brunovsky canonical form, allowing the application of linear control theory for control design. Compared to traditional linear quadratic regulator (LQR) controllers, the proposed DFBC method achieves higher precision and robustness in reversing maneuvers. We also showcase the performance of the proposed DFBC method through physical experiments conducted on our self-developed 1/10 scale autonomous tractor-trailer.

Inertia drift is an aggressive transitional driving maneuver, which is challenging due to the high nonlinearity of the system and the stringent requirement on control and planning performance. This paper presents a solution for the consecutive inertia drift of an autonomous RC car based on primitive-based planning and data-driven control. The planner generates complex paths via the concatenation of path segments called primitives, and the controller eases the burden on feedback by interpolating between multiple real trajectories with different initial conditions into one near-feasible reference trajectory. The proposed strategy is capable of drifting through various paths containing consecutive turns, which is validated in both simulation and reality.

Maneuvering an autonomous vehicle under drift condition is critical to the safety of autonomous vehicles when there is a sudden loss of traction due to external conditions such as rain or snow, which is a challenging control problem due to the presence of significant sideslip and nearly full saturation of the tires. In this paper, we focus on the control of drift maneuvers of autonomous vehicle to track circular paths with either fixed or moving centers, subject to change in the tire-ground interaction. In order to achieve the above tasks, we propose a hierarchical control architecture which decouples the curvature and center control of the trajectory. In particular, an outer control loop is proposed to stabilize the center by tuning the target curvature, and an inner control loop tracks the curvature using a feedforward/feedback controller enhanced by an$\mathcal{L}_{1}$adaptive component. The hierarchical architecture is flexible because the inner loop is task-agnostic and adaptive to changes in tire-ground interaction, which allows the outer loop to be designed independent of low-level dynamics, opening up the possibility of incorporating sophisticated planning algorithms. We implement our control strategy on a simulation platform as well as on a 1/10 scale RC car, and both the simulation and experiment results illustrate the effectiveness of our strategy in achieving the above described set of drift maneuvering tasks.