Hierarchical Model Predictive Control for Multi-Robot Navigation
Authors: Chao Huang, Xin Chen, Yifan Zhang, Shengchao Qin, Yifeng Zeng, Xuandong Li
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed control scheme guarantees the stability and feasibility, and is more efficient and viable than other Model Predictive Control schemes, as evidenced by our simulation results. |
| Researcher Affiliation | Academia | 1State Key Laboratory for Novel Software Technology, Nanjing University, China 2School of Computing, Teesside University, UK 3Shenzhen University, China |
| Pseudocode | No | The HMPC scheme steps are described in text, but no formally labeled "Pseudocode" or "Algorithm" block is present. |
| Open Source Code | No | The paper mentions using "CVX a Matlab-based package" and "Multi-Parametric Toolbox 3.0" but does not provide specific access to its own implementation code for HMPC. |
| Open Datasets | No | The paper describes simulation scenarios ("stationary formation" and "moving aggregation") with robots and their dynamics, but it does not use a publicly available or open dataset, nor does it provide access information for the data generated in its simulations. |
| Dataset Splits | No | The paper describes simulation scenarios and parameters but does not involve machine learning datasets with explicit training, validation, or test splits. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for running the simulations. |
| Software Dependencies | Yes | We use the CVX a Matlab-based package [CVX Research, 2012; Grant and Boyd, 2008] for solving optimization problems in multiple MPC schemes. ... computed by the multi-parametric toolbox [Herceg et al., 2013], which is the Ki-step reachable set from the origin, i.e. Ri(0, Ki). |
| Experiment Setup | Yes | The parameter setting in the simulation is as follows. First, the sampling interval of i-th robot is defined:... The input constraint of the i-th robot is: Ui = {u : |u| ≤ 0.2}. The safety distance is dsafe = 0.6. In practice, most works [Rawlings and Muske, 1993; Sznaier and Damborg, 1987] advocate choosing the horizon H online or big enough so that the optimal state q(H|t) obtained by solving problem (5) without the terminal constraint actually satisfies q(H|t) ∈ Qf. Also, a large enough weight of l H can ensure Assumption A4 to be met. Accordingly we give the configuration of parameters to make CMPC stable (hence, so is our HMPC): l H(q) = 10lq(q); Qf = R4; H = 9; p = 2 in the cost function, i.e. l is quadratic. Other parameters will be chosen differently for each scenario. ... Weighting factors in the cost function are chosen to be Cabs = Crel = I4, Cu = 0.01I2. ... setting the weighting factor of absolute position to 0: Cabs = 0, Crel = I4, Cu = 0.01I2. |