Results / Data
Comparative performance metrics across all five algorithms
Performance Comparison
Results collected from runs on a physical Turtlebot4 across simple and complex maze environments with static, added, and removed obstacles.
| Environment | Obstacle Type | Algorithm | Time (s) | Distance (m) |
|---|---|---|---|---|
| Simple Environment | ||||
| Simple | Static | A* | 40.43 | 4.04 |
| Simple | Static | D* Lite | 40.1 | 4.19 |
| Simple | Static | Jump A* | 39.64 | 4.10 |
| Simple | Static | DWA (w costmap) | 14.98 | 3.88 |
| Simple | Static | Hybrid | 15.34 | 3.93 |
| Simple | Add | A* | FAIL | FAIL |
| Simple | Add | D* Lite | 49.24 | 4.23 |
| Simple | Add | Jump A* | FAIL | FAIL |
| Simple | Add | DWA (w costmap) | 15.79 | 3.83 |
| Simple | Add | Hybrid | 15.34 | 3.86 |
| Simple | Remove | A* | 40.91 | 4.14 |
| Simple | Remove | D* Lite | 50.22 | 4.11 |
| Simple | Remove | Jump A* | 39.01 | 4.19 |
| Simple | Remove | DWA (w costmap) | 14.81 | 3.73 |
| Simple | Remove | Hybrid | 15.12 | 3.88 |
| Complex Environment | ||||
| Complex | Static | A* | 44.89 | 4.75 |
| Complex | Static | D* Lite | 64.8 | 4.88 |
| Complex | Static | Jump A* | 47.82 | 5.17 |
| Complex | Static | DWA (w costmap) | FAIL | FAIL |
| Complex | Static | Hybrid | 18.9 | 4.59 |
| Complex | Add | A* | 44.631 | 4.261 |
| Complex | Add | D* Lite | 73.2 | 5.51 |
| Complex | Add | Jump A* | FAIL | FAIL |
| Complex | Add | DWA (w costmap) | FAIL | FAIL |
| Complex | Add | Hybrid | FAIL | FAIL |
| Complex | Remove | A* | 41.38 | 4.67 |
| Complex | Remove | D* Lite | 59.96 | 5.02 |
| Complex | Remove | Jump A* | 51.15 | 5.19 |
| Complex | Remove | DWA (w costmap) | 14.54 | 3.42 |
| Complex | Remove | Hybrid | 18.56 | 4.54 |
1 A* Complex/Add partially succeeded on one run. It made it to the goal through brute force of pushing walls.
Pros & Cons
Qualitative trade-offs observed across all five algorithms.
Global Planner
A* (A-Star)
Pros
- Guaranteed optimal path
- Predictable, deterministic behavior
- Well-understood and well-documented
- Can stack with local planners (DWA)
Cons
- No dynamic replanning — must restart on new obstacles
- Can be slow on very large maps
- Requires fully-known map before planning
Dynamic Replanner
D* Lite
Pros
- Efficient incremental replanning
- Handles dynamic and unknown environments
- Only recomputes affected path segments
- Strong performance in partially-known maps
Cons
- More complex to implement and tune
- Higher overhead than A* in static environments
- Can produce non-smooth paths
Global Planner
Jump Point Search (JPS)
Pros
- Much faster than A* on open uniform grids
- Same path optimality as A*
- Dramatic reduction in node expansions
Cons
- Only works correctly on uniform-cost grids
- No dynamic replanning support
- Benefits diminish on densely-occupied maps
Reactive Controller
DWA (Dynamic Window Approach)
Pros
- Reactive real-time obstacle avoidance
- Respects robot kinematic constraints
- Configurable cost function for tuning behavior
- No prior map required (reactive only)
Cons
- Can get stuck in local minima
- No global path optimality guarantees
- Sensitive to weight tuning
- May struggle in narrow corridors
Hybrid Stack
Hybrid A* + DWA
Pros
- Global optimality + local reactivity
- Handles dynamic obstacles along planned path
- Mirrors production Nav2 architecture
- More robust than either alone
Cons
- Higher system complexity and tuning overhead
- Two nodes must coordinate correctly
- DWA may deviate from A* plan in cluttered spaces