3.4.1 Comparative Evaluation of Path Planning Strategies
Real-time autonomous navigation experiments were carried out in the four
scenarios in Fig. 3, where the Improved LeGO-LOAM facilitated
localization and mapping. Our primary focus was on evaluating the impact
of the local planner on the resultant trajectory. The relevant
parameters are also displayed in Table 2. To visually compare the
effects of algorithmic improvements, the comparative results are
presented on a two-dimensional grid map. As shown in Fig. 6, the
original OpenPlanner is represented by the blue line, whereas our
Enhanced OpenPlanner is indicated by the red line. These trajectories
are derived based on the odometer information of the robot.
Fig. 6 (a) and (e) display the results of navigation and obstacle
avoidance experiments in the crop row scenario. The lower left of Fig. 6
(a) provides the location and details of the obstacle. The plant edges
comprise leaves close to the ground, posing a challenge for successful
robot avoidance. The comparison results in Fig. 6 (e) show that both
algorithms avoid the obstacles, and their trajectories initially appear
almost identical. However, a difference emerges when encountering
obstacles: our algorithm executes a larger-angle evasion due to the
consideration of plant density, whereas the robot relying on the
original OpenPlanner algorithm encounters the edge of the plants.
Therefore, in this scenario, our algorithm proves safer.
Fig. 6 (b) and (f) illustrate navigation and obstacle avoidance results
within a landscape greenhouse. We configured a TurtleBot2 to shift
laterally on the road as a dynamic obstacle. Fig. 6 (b) provides details
of our algorithm’s avoidance of the dynamic obstacle, while Fig. 6 (f)
contrasts both algorithms. The results reveal that both methods are
capable of effectively avoiding dynamic obstacles and reaching the
endpoint. However, a close examination reveals that the original
OpenPlanner algorithm plans a more inward route to take a shorter path.
Given the presence of branches stretching onto the road, this may cause
the robot to collide with some branches. In contrast, the enhanced
OpenPlanner algorithm prioritizes plant density over distance
expenditure. This experiment demonstrates that the enhanced OpenPlanner
algorithm is not only capable of avoiding dynamic obstacles but also
ensures greater safety.
Fig. 6 (c) and (g) present navigation results within a challenging
unstructured greenhouse. This is the most challenging scenario of this
study, where both the rugged road surface and roadside vegetation pose
significant challenges to navigation. As shown in the figures, the
original OpenPlanner algorithm fails in this environment. Fig. 6 (c)
presents the failed scenario and its cause. In local planning, the
original OpenPlanner algorithm prioritizes distance cost, leading to
traversal over many complex terrains. Eventually, the algorithm fails
due to severe drift in pose estimation. In contrast, our algorithm,
considering ground structure, safely reaches the target point. Even
though it takes a longer route, this approach is meaningful from a
practical perspective. This scenario underscores the crucial role of
ground factors in successful navigation in complex environments.
Fig. 6 (d) and (h) aim to test navigation performance in low-lying plant
environments. We set the target point at the edge of the low-lying
plants. The LiDAR trajectory in Fig. 6 (d) reveals that the original
OpenPlanner algorithm travels very close to the plants. This implies
potential harm to low-lying plants if the robot is too large, as the
algorithm does not consider plant density during local path planning. On
the other hand, as seen in comparison Fig. 6 (h), our algorithm
effectively keeps a safe distance from the low-lying plants, planning a
more feasible and safer trajectory.
From the tests conducted across the four scenarios, it is evident that
our enhanced OpenPlanner can effectively evade both static and dynamic
obstacles during local path planning. Compared to the original
OpenPlanner, the improved algorithm maintains a safer distance from the
plants, which aids in protecting them from possible vehicle damage. This
includes avoidance of plant branches protruding from the road edges and
low-lying plants. Moreover, our algorithm proficiently avoids rugged
terrain, contributing to a higher success rate in complex environment
navigation. This robustness makes our algorithm more practical for
real-world applications.