YOPOv2-Tracker: An End-to-End Agile Tracking and Navigation Framework from Perception to Action

Abstract: Traditional target tracking pipelines including detection, mapping, navigation, and control are comprehensive but introduce high latency, limitting the agility of quadrotors. On the contrary, we follow the design principle of "less is more", striving to simplify the process while maintaining effectiveness. In this work, we propose an end-to-end agile tracking and navigation framework for quadrotors that directly maps the sensory observations to control commands. Importantly, leveraging the multimodal nature of navigation and detection tasks, our network maintains interpretability by explicitly integrating the independent modules of the traditional pipeline, rather than a crude action regression. In detail, we adopt a set of motion primitives as anchors to cover the searching space regarding the feasible region and potential target. Then we reformulate the trajectory optimization as regression of primitive offsets and associated costs considering the safety, smoothness, and other metrics. For tracking task, the trajectories are expected to approach the target and additional objectness scores are predicted. Subsequently, the predictions, after compensation for the estimated lumped disturbance, are transformed into thrust and attitude as control commands for swift response. During training, we seamlessly integrate traditional motion planning with deep learning by directly back-propagating the gradients of trajectory costs to the network, eliminating the need for expert demonstration in imitation learning and providing more direct guidance than reinforcement learning. Finally, we deploy the algorithm on a compact quadrotor and conduct real-world validations in both forest and building environments to demonstrate the efficiency of the proposed method.

An Overview of YOPOv2-Tracker: An End-to-End Agile Tracking and Navigation Framework from Perception to Action

The paper "YOPOv2-Tracker: An End-to-End Agile Tracking and Navigation Framework from Perception to Action" presents an innovative approach to tracking and navigation for quadrotors, leveraging an integrated end-to-end framework. This framework stands out by mapping sensory observations directly to control commands, eschewing the traditional, latency-inducing, multi-step pipeline that decomposes tasks into separate modules like detection, mapping, planning, and control.

Framework Design and Methodology

The proposed YOPOv2-Tracker adopts a minimalist yet effective architecture that centers around a fully convolutional network, which directly maps visual and state inputs to control outputs. The network utilizes a series of motion primitives to cover the search space, thus addressing the complexities of both obstacle-rich navigation and agile tracking of moving targets. Key to the framework is the reformulation of trajectory optimization as a regression of primitive offsets, which are further refined based on safety, smoothness, and other critical metrics. The work incorporates multimodal strategies in both navigation and detection, maintaining interpretability while enhancing efficiency.

A notable aspect of the proposed methodology is the treatment of the problem as inherently multimodal by drawing parallels between object detection tasks and trajectory planning. The approach deploys motion primitives akin to anchor boxes used in object detectors, ensuring comprehensive spatial exploration. Offsets and associated trajectory costs are predicted, followed by conversion to control commands that consider both dynamics and environmental disturbances.

Control and Real-world Deployment

The control strategy in YOPOv2-Tracker is particularly noteworthy for its use of the quadrotor's differential flatness property. Unlike traditional methods that plan from a reference position, this framework plans directly from the current state, calculating desired thrust and attitude from the network's predictions while incorporating estimated disturbances. This approach eliminates a potential source of error accumulation and latency inherent in layered control architectures and allows for agile maneuvers in cluttered environments.

Deployment of the network on a compact quadrotor system highlights the practical viability of the framework. Real-world experiments demonstrate the framework's robust tracking capabilities in cluttered environments such as dense forests and complex architectural structures. These results underscore the effectiveness of the end-to-end design in ensuring high-speed, reliable navigation when relying only on limited computational resources and visual sensors.

Training Paradigm and Theoretical Contributions

The YOPOv2-Tracker introduces a unique training methodology that integrates traditional motion planning with deep learning through end-to-end gradient back-propagation, eliminating the need for expert demonstrations and the complexities associated with reinforcement learning. This paradigm allows the network to benefit directly from the privileged information during training, such as ground truths of the environment and target states, facilitating more accurate prediction and efficient learning.

The framework's design avoids the pitfalls of mode collapse, which can occur in multimodal problems, by leveraging a set of primitives for extensive exploration of the feasible space. By employing a detection network-like architecture, the system maintains a clear mapping between inputs and spatially distributed anchor primitives, ensuring numerical stability across predictions.

Implications and Future Prospects

The implications of this research extend beyond the immediate application in quadrotor navigation and tracking. By streamlining the perception-to-action process into a single, coherent network, the research sets a precedent for integrating multimodal tasks within unified frameworks. The end-to-end training strategy leverages the inherent strengths of deep learning, making it a strong candidate for further developments in autonomous robotic operations, especially in environments where obstacles are dense and the computational capabilities are constrained.

Future developments may explore expanding the proposed framework to incorporate additional sensory modalities or integrating it with other AI-driven decision-making processes. Moreover, extending the framework's application to address other multimodal tasks could reveal new design paradigms for autonomous systems operating under constraints similar to those encountered in this research.

In summary, YOPOv2-Tracker effectively addresses the challenges of agile tracking and high-speed navigation in cluttered environments, demonstrating performance and feasibility that surpass current methodologies. The combination of an elegant architectural design with a robust control strategy situates this work at the frontier of integrating AI into real-world robotic applications.