Papers for

autonomous vehicle fleet managers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

ItA-LaCAM solves target assignment and path planning for many robots

ITA-LaCAM: A Complete and Scalable TAPF Solver via Assignment-Aware Configuration-Space Search

Abstract: Combined Target Assignment and Path Finding (TAPF) requires assigning targets for agents while simultaneously planning collision-free paths. We present ITA-LaCAM, a complete and scalable TAPF solver inspired by LaCAM and ITA-CBS. In ITA-LaCAM, each joint-configuration node carries an agent-to-target matching. When a successor is generated, ITA-LaCAM incrementally repairs the matching for the agents that moved and uses the targets to guide PIBT successor generation. This design enables adaptive reassignment without explicitly enumerating the combinatorial assignment space, while preserving LaCAM's completeness and scalability. Across 9,760 benchmark instances on eight maps with 5--200 agents, ITA-LaCAM solved 100% of the instances, compared with 95.6% for IR-TAPF configured with DBS-Hungarian. ITA-LaCAM found an initial solution faster in 84.0% of the comparisons and achieved a lower sum of costs in 65.0% of the instances solved by both methods.

Sun 13 SeptRobotics

The gist

Some robots need to be assigned targets and find paths to reach them without bumping into each other. The authors present ITA-LaCAM, a method that plans these assignments and collision-free routes together more efficiently than earlier methods. ITA-LaCAM adapts assignments on the fly and avoids checking every possible target combination, making it faster and more reliable. It solved all tested problems with up to 200 robots and often found better solutions quicker than comparable methods.

Open → 2609.14208v1