이 예제는 RRT(Rapidly-exploring Random Tree) 알고리즘을 사용하여 알려진 맵에서 움직이는 이동체의 경로를 계획하는 방법을 보여줍니다. 특수한 이동체 제약 조건은 사용자 지정 상태공간에도 적용됩니다. 모든 내비게이션 응용 분야에서 사용자 지정 state space 객체와 path validation 객체를 이용해 사용자 자신의 플래너를 조정할 수 있습니다.

이 예제는 거리 측정기와 A* 경로 플래너를 이용해 창고 맵에 동적 재계획을 수행하는 방법을 보여줍니다.

Perceive surround-view information and use it to design an automated lane change maneuver system for highway driving scenarios.

Demonstrates how to plan a local trajectory in a highway driving scenario. This example uses a reference path and dynamic list of obstacles to generate alternative trajectories for an ego vehicle. The ego vehicle navigates through traffic defined in a provided driving scenario from a drivingScenario object. The vehicle alternates between adaptive cruise control, lane changing, and vehicle following maneuvers based on cost, feasibility, and collision-free motion.

Find global path-planning solutions for systems with complex kinematics using the kinematics-based planner, plannerControlRRT. The example is organized into three primary sections:

Dynamically replan the motion of an autonomous vehicle based on the estimate of the surrounding environment. You use a Frenet reference path and a joint probabilistic data association (JPDA) tracker to estimate and predict the motion of other vehicles on the highway. Compared to the Highway Trajectory Planning Using Frenet Reference Path example, you use these estimated trajectories from the multi-object tracker in this example instead of ground truth for motion planning.

Optimize the path for a car-like robot by maintaining a smooth curvature and a safe distance from the obstacles in a parking lot.

Choose the best 2-D path planner for a differential drive robot in a warehouse environment from the available path planners. Use the plannerBenchmark object to benchmark the path planners plannerRRT, plannerRRTStar, plannerBiRRT, plannerPRM, and plannerHybridAstar on the warehouse environment with the randomly chosen start and goal poses. Compare the path planners based on their ability to find a valid path, clearance from the obstacles, time taken to initialize a planner, time taken to find a path, length of the path, and smoothness of the path. A suitable planner is chosen based on the performance of each path planner on the above mentioned metrics.

Process and store 2.5-D information, and presents various techniques for using it for an offroad path planner.

Use a Hybrid A* planner to plan a path to a narrow parking space, while accounting for the shape of a car-like robot.

Plan a path for a mobile robot to a goal region using a rapidly exploring random tree (RRT) path planner. In this example, you can define a custom goal region as a 2-D polygon, and then plan a path to it.

Demonstrates how to train a deep learning-based sampler to speed up path planning using sampling-based planners like RRT (rapidly-exploring random tree) and RRT*.

The example demonstrates how to augment sampling-based planners such as RRT (rapidly-exploring random tree) and RRT* with a deep-learning-based sampler to find optimal paths efficiently.