Multirotors plan routes to save battery under localized disturbances
Battery-Aware Predictive Trajectory Planning and Control for Multirotors Under Disturbances
Robotics
Summary
Flying drones often face wind or other disturbances that make controlling them harder and use more battery. This paper shows how to plan drone paths smarter by predicting how disturbances and battery limits affect energy and control. The proposed method picks trajectories that balance smooth flying with energy saved, even when the battery is low. The results show better accuracy and less battery drain compared to simpler planning methods.
What this means in practice
- •For drone operators: Design drone missions that adjust flight paths to optimize battery life when facing localized winds or disturbances.
- •For robotics engineers: Develop controller software that integrates battery state and actuator limits into trajectory planning for improved drone endurance and control.
Authors
Krishna Bhavithavya Kidambi
Abstract
This paper presents a battery-aware predictive trajectory-planning and control framework for multirotors operating under spatially localized disturbances. Candidate trajectories are evaluated through closed-loop vehicle--motor--battery propagation, allowing disturbance-induced control demand, electrical energy, battery evolution, and terminal-voltage-dependent actuator capability to enter the planning process. % A reduced-order battery model is numerically benchmarked against an independently implemented Simscape equivalent-circuit reference, with a power NRMSE of $0.64\%$ and a cumulative-energy discrepancy below $0.7\%$. % In a $150$-s, $640$-m mission containing three disturbance regions, the selected trajectory reduces electrical energy consumption by $7.46\%$ and position-tracking RMSE by approximately $72\%$ relative to the disturbance-aware fixed-reference baseline. % Planner ablations show that battery-dependent terms are nonbinding at nominal SOC but alter the selected trajectory under a depleted-battery stress condition. % Execution with multiple feedback controllers further demonstrates that controller selection changes the tradeoff among tracking accuracy, energy consumption, and actuator utilization. % The results demonstrate the benefit of accounting for predicted closed-loop energetic and battery--actuator consequences during trajectory selection.