PAMPC: Perception-Aware Model Predictive Control for Quadrotors (1804.04811v2)

Abstract: We present the first perception-aware model predictive control framework for quadrotors that unifies control and planning with respect to action and perception objectives. Our framework leverages numerical optimization to compute trajectories that satisfy the system dynamics and require control inputs within the limits of the platform. Simultaneously, it optimizes perception objectives for robust and reliable sens- ing by maximizing the visibility of a point of interest and minimizing its velocity in the image plane. Considering both perception and action objectives for motion planning and control is challenging due to the possible conflicts arising from their respective requirements. For example, for a quadrotor to track a reference trajectory, it needs to rotate to align its thrust with the direction of the desired acceleration. However, the perception objective might require to minimize such rotation to maximize the visibility of a point of interest. A model-based optimization framework, able to consider both perception and action objectives and couple them through the system dynamics, is therefore necessary. Our perception-aware model predictive control framework works in a receding-horizon fashion by iteratively solving a non-linear optimization problem. It is capable of running in real-time, fully onboard our lightweight, small-scale quadrotor using a low-power ARM computer, to- gether with a visual-inertial odometry pipeline. We validate our approach in experiments demonstrating (I) the contradiction between perception and action objectives, and (II) improved behavior in extremely challenging lighting conditions.

• The paper introduces PAMPC, a framework that unifies quadrotor control and planning by jointly optimizing action and perception objectives.
• PAMPC integrates perception objectives like maximizing visibility into a real-time non-linear optimization problem computed onboard low-power ARM computers.
• Experimental validation shows PAMPC improves quadrotor performance in challenging lighting conditions, enabling more reliable navigation in difficult environments.

Essay: PAMPC: Perception-Aware Model Predictive Control for Quadrotors

The paper introduces the Perception-Aware Model Predictive Control (PAMPC) framework, which represents a significant advancement in the field of quadrotor control. The primary objective of PAMPC is to unify control and planning by jointly optimizing action and perception objectives. This framework leverages numerical optimization to compute trajectories that satisfy both the dynamical constraints of the quadrotor platform and the perception objectives necessary for robust and reliable sensing.

Overview of Methodology

The core innovation of PAMPC is its ability to integrate perception objectives into the control process, specifically by maximizing the visibility of a point of interest and minimizing its velocity in the image plane. This dual focus is essential because the requirements for perception can often contradict those for motion control, necessitating a careful balancing act. For instance, optimizing the thrust alignment with an intended trajectory can lead to orientations that reduce the visibility of critical visual landmarks. The PAMPC recasting of these objectives into a non-linear optimization problem allows for a more harmonious consideration of these aspects, catering for real-time adjustments in a rapidly changing environment.

Numerical and Experimental Validation

A crucial aspect of PAMPC’s development is its real-time operational capacity, achieved through effective onboard computation. The platform runs a non-linear optimization problem in a receding-horizon fashion, making it computationally feasible with the help of low-power ARM computers. This capability is particularly relevant for small-scale quadrotors, which have stringent computational and power constraints.

Experimentally, the paper validates the PAMPC framework through a series of tests, demonstrating its efficacy in scenarios where traditional control methods may fail. Notably, the framework shows improved performance under highly challenging lighting conditions and in maintaining visual markers within the camera’s field of view, underscoring the reliability and robustness of the perception-aware approach.

Implications

The implications of this research are broad for both theoretical and practical advancements in autonomous aerial vehicles. Theoretically, the integration of perception objectives into the quadrotor control framework enhances the understanding of coupled dynamics and sensing. Practically, it opens new possibilities in environments previously deemed too challenging for reliable autonomous navigation, such as those with dynamic lighting changes or sparse visual features.

Future work could extend these principles to other robotic platforms and complex environments, potentially incorporating machine learning algorithms to dynamically adjust perception objectives based on real-time learning and adaptation. Additionally, expanding the current perception objectives could further enhance the framework’s applicability to diverse operational scenarios, such as obstacle avoidance and cooperative multi-robot systems.

In conclusion, the PAMPC framework represents a valuable advancement in robotic perception and control, providing a robust methodology for integrating sensory data with dynamic motion planning in quadrotor systems. The framework not only supports more reliable and agile navigation but also exemplifies a powerful paradigm for interdisciplinary research and application in autonomous systems.