Improving Continuous-time Conflict Based Search
Authors: Anton Andreychuk,Konstantin Yakovlev,Eli Boyarski,Roni Stern11220-11227
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the effect of the suggested enhancements by running experiments both on general graphs and 2k-neighborhood grids. |
| Researcher Affiliation | Collaboration | 1 Peoples Friendship University of Russia (RUDN University) 2 Federal Research Center for Computer Science and Control of Russian Academy of Sciences 3 HSE University 4 Ben-Gurion University of the Negev 5 Palo Alto Research Center |
| Pseudocode | Yes | Algorithm 1: Low-level search for CCBS with DS |
| Open Source Code | Yes | Our implementation and all the raw results are available at: github.com/PathPlanning/Continuous-CBS. |
| Open Datasets | Yes | This map is publicly available in the Moving AI MAPF benchmark (Stern et al. 2019). |
| Dataset Splits | No | The paper describes generating 'scenarios' and 'MAPFR instances' for evaluation but does not specify a distinct validation set or explicit dataset split percentages (e.g., train/validation/test splits). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for the experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper mentions tools and algorithms like 'OMPL' and 'Dijkstra s algorithm' but does not specify version numbers for any software dependencies, which are required for reproducibility. |
| Experiment Setup | Yes | In the conducted experiments all agents were assumed to be disk-shaped with radius equal to 2/4. In each run of the evaluated algorithm, we recorded the runtime, the number of expanded CT nodes, and whether the algorithm was able to find a solution under a time limit of 30 seconds. ... For each roadmap, 25 different scenarios were generated. Each scenario is a list of start-goal vertices, chosen randomly from the graph. Then, we pick the first n = 2 startgoal pairs and create a MAPFR instance for n agents. If the evaluated algorithm solves this instance within the 30 seconds time limit, we proceed by increasing n by 1 and creating a new MAPFR instance. This is repeated until the evaluated algorithm is not able to solve the instance in 30 seconds. |