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A framework for autonomous waypoint planning, trajectory generation through waypoints, and trajectory tracking for multi-rotor unmanned aerial vehicles (UAVs) is proposed in this work. Safe and effective operations of these UAVs is a problem that demands obstacle avoidance strategies and advanced trajectory planning and control schemes for stability and energy efficiency. To address this problem, a two-level optimization strategy is used for trajectory generation, then the trajectory is tracked in a stable manner. The framework given here consists of the following components: (a) a deep reinforcement learning (DRL)-based algorithm for optimal waypoint planning while minimizing control energy and avoiding obstacles in a given environment; (b) an optimal, smooth trajectory generation algorithm through waypoints, that minimizes a combinaton of velocity, acceleration, jerk and snap; and (c) a stable tracking control law that determines a control thrust force for an UAV to track the generated trajectory.
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Mutual Information-Based Trajectory Planning for Cislunar Space Object Tracking using Successive Convexification.
We consider the problem of trajectory planning for optimal relative orbit determination in the cislunar environment. The recent interest in cislunar space has created a need to develop autonomous tracking technologies that can maintain situational awareness of this dynamically complex regime. Optical sensors provide an ideal observation platform because of their low cost and versatility in tracking both cooperative and non-cooperative space objects. The estimation performance of an optical observer can be significantly enhanced through manuevering. This work develops a trajectory planning tool, compatible with low-thrust propulsion, for tracking one or multiple targets operating in proximity to the observer. We formulate an objective function that is a convex combination of the mutual information between target states and measurements, and the low-thrust control effort. The subsequent optimal control problem is solved via direct collocation using the successive convexification algorithm, which, we argue, is well suited for a potential onboard trajectory planning application. We demonstrate the tool for several relevant scenarios with one and multiple targets operating around periodic orbits in the circular restricted three-body problem. A sequential estimator's performance is evaluated using the Cramer-Rao lower bound and, compared to a purely passive observer, we show that optimizing the observer's trajectory can decrease this bound by up to several orders of magnitude within a planning window. This investigation provides an initial proof-of-concept to future onboard planning technologies for relative tracking in the cislunar domain.
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- Award ID(s):
- 2211548
- PAR ID:
- 10511434
- Publisher / Repository:
- American Institute of Aeronautics and Astronautics
- Date Published:
- Journal Name:
- AIAA SciTech Forum
- ISBN:
- 978-1-62410-711-5
- Format(s):
- Medium: X
- Location:
- Orlando, FL
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.2514/6.2024-0626
