Active Sensing and Deferred-Decision Trajectory Optimization for Robust Target Identification

Researchers introduce Active-Sensing Deferred-Decision Trajectory Optimization (AS-DDTO), an advanced planning algorithm that optimizes mobile sensing system trajectories for target identification while maintaining reachability under resource constraints. The method enhances traditional DDTO by incorporating information-acquisition objectives, enabling earlier target identification through strategic path planning in uncertain sensing environments.

This research addresses a fundamental challenge in autonomous systems: how mobile sensors can efficiently identify a target from multiple candidates while managing resource constraints and sensing uncertainty. AS-DDTO extends prior work by integrating information theory directly into trajectory planning, allowing systems to gather decision-relevant data earlier in their missions.

The advancement builds on decades of control theory and Bayesian inference research. Traditional trajectory optimization assumes perfect information or treats sensing as a post-hoc step. AS-DDTO's innovation lies in treating target identification as a planning objective rather than a constraint, enabling the system to proactively move toward regions that maximize information gain. This reflects broader trends in robotics and autonomous systems toward more intelligent, adaptive planning algorithms.

For practitioners developing autonomous vehicles, drones, and surveillance systems, this work reduces operational costs by shortening missions and minimizing resource consumption. The mixed-integer conic reformulation provides a computationally tractable solution, making implementation feasible for real-world applications. Recursive feasibility guarantees ensure systems maintain safety properties even under planning failures, addressing practical deployment concerns.

The framework's support for conformal candidate-set updates enables handling of unexpected targets or model misspecification—critical for robust real-world operation. Future developments likely include scaling to higher-dimensional target sets, distributed multi-agent scenarios, and integration with modern learning-based perception systems. The work establishes a methodological foundation for systems requiring both adaptive exploration and safety-critical reachability guarantees.

• →AS-DDTO combines trajectory optimization with information-theoretic planning objectives to accelerate target identification under sensing uncertainty.
• →The algorithm maintains reachability guarantees to all candidate targets while biasing paths toward information-rich regions.
• →Mixed-integer conic reformulation provides computationally tractable solutions suitable for real-world autonomous systems.
• →Recursive feasibility and belief concentration guarantees ensure safety properties during deployment.
• →Simulations demonstrate earlier target identification compared to standard DDTO methods with distance-dependent sensing.