Multi-Agent Motion Planning for Differential Drive Robots Through Stationary State Search
Authors: Jingtian Yan, Jiaoyang Li
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we tested our methods on the single-shot grid map domain and the lifelong warehouse domain. Our method shows up to 400% improvements in terms of throughput compared to existing methods. |
| Researcher Affiliation | Academia | Jingtian Yan, Jiaoyang Li Carnegie Mellon University EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Stationary SIPP (SSIPP) Algorithm 2: Partial Stationary Expansion Algorithm 3: Pseudocode for Windowed-SSIPP |
| Open Source Code | Yes | The source code for our method is publicly accessible at https://github.com/JingtianYan/MASS-AAAI. |
| Open Datasets | Yes | We evaluated all methods on four-neighbor grid maps, including empty (empty-32-32, size: 32 32), random (random-32-32-10, size: 32 32), room (room-64-64-8, size: 64 64), den520d (den520d, size: 256 257), Boston (Boston 0 256, size: 256 256), warehouse-small (warehouse-10-20-10-2-1, size: 161 63), warehouse-large (warehouse-20-40-10-22, size: 340 164) from the Moving AI benchmark (Stern et al. 2019), and the sortation-center map (size: 500 140) from the LMAPF Competition (Chan et al. 2024). |
| Dataset Splits | No | For each map, we conducted experiments with a progressive increment in the number of agents, using the 25 random scenarios from the benchmark set. For lifelong MAMPD, the paper states: "We run each method for 1,000 simulation time seconds and average the results over 7 runs." The paper does not provide specific train/test/validation dataset splits but rather uses predefined scenarios/instances or simulation time and runs. |
| Hardware Specification | Yes | We conducted all experiments on an Ubuntu 20.04 machine equipped with an AMD 3990x processor and 188 GB of memory. Our code was executed using a single core for all computations. |
| Software Dependencies | No | We implemented both our and baseline methods in C++. We conducted all experiments on an Ubuntu 20.04 machine. The paper mentions the programming language (C++) and operating system (Ubuntu 20.04) but does not provide specific version numbers for any key software libraries or dependencies used. |
| Experiment Setup | Yes | All agents adhered to the same kinodynamic constraints, where the speed is bounded by the range of [0, 2] cell/s, while the acceleration is confined to [ 0.5, 0.5] cell/s2. Each agent has a speed limit from [0, 2] m/s and an acceleration limit from [ 0.5, 0.5] m/s. Table 2 also lists 'tw' values (20 s, 25 s, 30 s, 40 s) for the lifelong MAMPD evaluation. |