control-aware trajectory optimization

This work addresses the limitations of classical trajectory planning methods, which prioritize kinematic smoothness while neglecting dynamics and actuator control effort, often resulting in large tracking errors and high energy consumption. To overcome these issues, the authors propose a control-aware optimal trajectory planning framework that explicitly integrates the nonlinear dynamics of robotic manipulators and actuator effort over a finite time horizon. A midpoint linearization strategy is introduced to enhance the accuracy of dynamic approximations during large-range motions. By establishing a unified nonlinear closed-loop simulation environment, the study enables, for the first time, an isolated evaluation of trajectory generation methods under identical conditions. Experimental results on a simplified UR5 model demonstrate that the proposed approach significantly reduces tracking error, corrective torque, and overall closed-loop execution cost, achieving substantially lower energy consumption and total operational expense compared to conventional planners such as cubic, quintic, and trapezoidal profiles.

Fixed controller parameters in quadcopter trajectory tracking hinder task adaptability and limit tracking accuracy and dynamic feasibility. To address this, we propose TACO (Trajectory-Aware Controller Optimization), a novel framework that jointly integrates a learned trajectory prediction model with a lightweight online optimization mechanism. TACO enables real-time, adaptive tuning of controller gains based on reference trajectory characteristics and the vehicle’s current state, while supporting online trajectory replanning under smoothness and dynamic feasibility constraints to enhance response robustness. Efficient training data generation and model learning are enabled via a parallelized high-fidelity simulator. Experiments demonstrate that TACO significantly reduces tracking error across diverse complex trajectories, outperforming conventional manual tuning approaches. Moreover, its optimization speed is orders of magnitude faster than black-box methods, exhibiting strong potential for real-time deployment.

Generating optimal trajectories for dynamic systems—such as UAVs and robotic manipulators—under strong nonlinear dynamics, nonconvex input constraints, and real-time obstacle avoidance remains challenging. Method: This paper proposes a bilevel temporal decomposition optimization framework: an outer loop fixes the planning horizon, while an inner loop solves subproblems over dynamically constructed restricted convex sets. It innovatively couples temporal decomposition with an incremental convex set search mechanism. Contributions/Results: The framework ensures local optimality and feasibility while enabling efficient real-time computation; theoretical analysis proves convergence and reduced task completion time under mild conditions. Key techniques—including short-horizon convex reformulation, a customized incremental algorithm, and embedded obstacle constraints—significantly accelerate computation. Extensive simulations validate the method’s real-time performance, trajectory quality, and robustness against modeling uncertainties and environmental changes.

To address trajectory optimization instability, high energy consumption, and poor real-time performance of resource-constrained UAVs under model mismatch (e.g., payload or structural changes), this paper proposes a synergistic framework integrating affine-parameterized multirotor dynamics modeling and online adaptive control. The nonlinear dynamics are innovatively relaxed into an affine parameter form, enabling formulation of a convexifiable moving-horizon parameter estimation (MHPE) problem, which is equivalently transformed into a linear-quadratic MHPE (LQ-MHPE) formulation. This allows tight closed-loop integration with model predictive control (MPC). Compared to nonlinear estimation methods, the proposed approach reduces average computation time by 98.2% and lowers trajectory optimization cost by 23.9%–56.2%. It is the first method to enable onboard real-time adaptive MPC for multirotors, achieving high accuracy, low computational complexity, and strong robustness against model uncertainties.

Time-optimal trajectory generation for quadrotors involves computationally expensive non-convex optimization, hindering real-time deployment. Method: This paper proposes an end-to-end trajectory generation framework based on sequential learning: given a collision-free geometric path, it directly predicts the time-optimal velocity profile satisfying dynamic constraints and safety boundaries. A novel backward reachable tube analysis framework guides the model to learn local analytical optimality; input-path random perturbation serves as data augmentation to improve robustness; and a length-agnostic sequential architecture (LSTM/Transformer) enables generalization to arbitrary path lengths. Results: Evaluated on a real quadrotor platform, the method accelerates trajectory generation by over one order of magnitude compared to conventional nonlinear programming solvers, achieves stable millisecond-level inference latency, and maintains high success rates and dynamical feasibility.

This paper addresses the controller synthesis problem for control-affine nonlinear systems subject to reach-avoid-stay specifications within a prescribed time horizon. We propose a novel adaptive smooth spatiotemporal tube (STT) framework. Unlike conventional STT methods that rely on barrier or avoidance functions—causing discontinuous tube boundaries and incurring high control effort—our approach constructs continuously differentiable STT boundaries, thereby eliminating the need for avoidance functions entirely. Furthermore, we derive an exact closed-form feedback control law that rigorously guarantees, within the specified time, simultaneous satisfaction of safety (avoidance), target reachability (reach), and persistent invariance (stay). Experimental results demonstrate that the proposed method significantly reduces control energy consumption while achieving superior task completion accuracy and temporal robustness compared to state-of-the-art approaches.

This work addresses the challenge of effectively managing glide energy for small fixed-wing UAVs under wind disturbances and obstacle constraints, where conventional approaches rely on reactive control and require meticulous parameter tuning. The authors elevate energy regulation to the planning level by proposing a nonlinear multi-objective trajectory planning method that, for the first time, achieves wind-aware energy-balanced gliding directly at the trajectory level. Integrating empirical sink polar curves with a net rate-of-climb model, the approach generates C³-continuous glide trajectories using Bernstein polynomials and maps them to control inputs via differential flatness. Cruise segments are initialized with Dubins paths, enabling online replanning and obstacle avoidance. Simulations and real-world flight tests demonstrate that the method effectively stabilizes sink rate, airspeed, and glide ratio in complex environments, confirming its reliability and practicality.

Time-optimal path tracking for robotic manipulators suffers from singularities and low computational efficiency when the path velocity reaches zero. Method: This paper proposes a path-parameterization-based optimization method that maximizes the path velocity—distinct from conventional total-time minimization frameworks—thereby inherently avoiding zero-velocity singularities while embedding trajectory smoothness and dynamic feasibility into the objective. The original nonlinear optimization problem is reformulated via discretization into a linearly parameterized form, enabling efficient and numerically stable solution via linear programming. Contribution/Results: Experiments demonstrate that the proposed method significantly reduces computational cost while generating time-optimal trajectories that are singularity-free and highly smooth, thereby enhancing real-time performance and robustness of path tracking.