Quadruped robots can traverse low obstacles, but many 2D planning pipelines still model obstacles as binary occupied regions and rely on sampling-based search that can be inefficient under a limited budget. We propose a perception-assisted height-adaptive planning framework based on CMP-IRRT*, a Channel Mamba PointNet-guided Informed RRT* planner. Given a calibrated top-view RGB observation, the perception module estimates obstacle regions and converts depth predictions into a ground-relative height map. The planner then performs height-conditioned collision checking, treating high obstacles as blocked while allowing low obstacles to be traversed, and uses the CMP guide to bias sampling toward promising regions while retaining standard free-space and informed sampling fallbacks. Experiments on 2D planning benchmarks show that CMP-IRRT* reduces explored nodes and iterations compared with classical and neural-guided baselines, and a controlled ablation supports the contribution of the Mamba-based guide. In constructed traversability-aware scenarios, the proposed planner reduces path length by up to 16.3% when low obstacles are traversable, and a Unitree Go2 demonstration further shows executable bypassing and traversal behaviors. Our code is publicly available at https://github.com/MingfanZhao/height-adaptive-planner.
Figures & tables
Figure 1: Overview of the proposed perception-assisted traversability-aware planning pipeline. The perception module estimates obstacle geometry from a calibrated top-view RGB observation. The planning module uses height-conditioned collision checking and CMP-guided informed sampling to find a low-cost path under a finite planning budget. The execution module decodes the path and traversal mode into robot commands.
Figure 2: Structure of the Channel Mamba PointNet guide used to predict promising search regions for CMP-IRRT*.
Figure 3: Comparison of planning performance on Center Block and 2D Random World problems. The compared methods include RRT* ( Karaman and Frazzoli, 2011 ) , IRRT* ( Gammell et al., 2014 ) , and NIRRT* ( Huang et al., 2024 ) . The plots report finite-budget planning behavior, including iterations to reach a cost threshold and path-cost improvement after an initial solution is found.
Method
RRT*
IRRT*
NIRRT*
CMP-IRRT*(Ours)
Initial Time(s)
0.17
0.19
1.81
1.37
Nodes
241.86
251.61
102.92
99.49
Iterations
299.04
312.75
151.69
146.55
Final Cost
233.01
232.63
206.51
203.41
Table 1: Average comparison results on the 2D Random World dataset. Lower values are better for all metrics.
Guide
Mamba
Channel Attn.
Nodes Ratio
Iterations Ratio
PointNet++
No
No
1.000
1.000
PointNet++ and Mamba
Yes
No
0.984
0.980
CMP
Yes
Yes
0.919
0.920
Table 2: Normalized controlled ablation of the guide architecture on 2D Random World. Nodes Ratio and Iterations Ratio denote the average number of nodes and iterations required to find the initial feasible path, normalized by the PointNet++ guide baseline. Lower values indicate better initial-solution search efficiency.
Evaluation setting and metric
NIRRT*
CMP-IRRT*
Table 1 protocol: success rate
100.0%
100.0%
Table 1 protocol: initial-solution time (s)
1.813±0.770
1.380±0.650
Table 1 protocol: final path cost after 18 s of post-initial optimization
207.27±26.79
204.11±25.73
2000-expansion budget: success rate
99.8%
100.0%
2000-expansion budget: iterations to first solution
151.71±106.24
146.61±99.87
2000-expansion budget: terminal path cost
207.06±26.62
203.87±25.29
Table 3: Paired repeated results on the 150-map Random World test set. Continuous metrics are reported as map-level mean and standard deviation, while success rates are computed over individual runs. Path cost is measured in map units. Lower is better except for success rate.
Figure 4: Traversability-aware planning results. (a) Visualization on four maps with low-height obstacles that can be traversed. Green obstacles denote obstacles below the threshold Hα . (b) Path shortening after applying height-conditioned traversal decisions.
Figure 5: Real-robot demonstration on a Unitree Go2 robot. The robot executes planned motions that include both obstacle bypassing and traversal of low obstacles.
Configuration
Task completion
Executed path, matched n=10
Intervention/safety
Binary-CMP
10/10
8.56±0.11 m
0 interventions
Oracle-height CMP
10/10
7.25±0.09 m
0 interventions
Full predicted- height CMP
18/20
7.71±0.13 m
2/20 trials required intervention; 0 falls; 0 body collisions
Table 4: Controlled Unitree Go2 trials. Task completion uses all trials for each configuration, whereas executed-path length is reported as mean and standard deviation on the common matched subset of ten trials.
Center for Autonomous Robotic Systems, Khalifa University (KU-CARS) · Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain