Continuous-Time Line-Of-Sight Constrained Trajectory Planning for 6-Degree of Freedom Systems
Christopher Hayner, John Carson, Behcet Acikmese, Karen Leung
AI summary
Problem
Existing trajectory planning methods enforce line-of-sight constraints only at discrete nodes, risking constraint violations between nodes. This limitation hinders reliable perception for safety-critical robotics tasks that require continuous target visibility.
Approach
The authors introduce CT-LOS, a continuous-time trajectory optimization method that reformulates line-of-sight constraints as integral conditions solved via successive convexification, ensuring strict visibility enforcement throughout the entire trajectory for six-degree-of-freedom systems.
Key results
- Sensor-footprint-agnostic line-of-sight constraint formulation
- Computationally tractable continuous-time guidance algorithm
- Significantly reduced line-of-sight violations compared to discrete-time baselines
- Lower computational runtime across challenging keypoint tracking scenarios
Why it matters
Enables reliable perception-driven autonomy for drones and aerial systems operating in safety-critical environments where continuous target visibility is mandatory.
Abstract
Perception algorithms are ubiquitous in modern autonomy stacks, providing necessary environmental information to operate in the real world. Many of these algorithms depend on the visibility of keypoints, which must remain within the robot’s line-of-sight (LoS) for reliable operation. This paper tackles the challenge of maintaining LoS on such keypoints during robot movement. We propose a novel method that addresses these issues by ensuring applicability to various sensor footprints, adaptability to arbitrary nonlinear system dynamics, and constant enforcement of LoS throughout the robot’s path. Our experiments show that the proposed approach achieves significantly reduced LoS violation and runtime compared to existing state-of-the-art methods in several representative and challenging scenarios.