Path planner based on time potential function in dynamic environment

The temporal potential function method is used to simulate the path planning in dynamic environment

Dynamic environment refers to the environment in which obstacles or targets or both move at the same time . In most current studies , Robots that try to navigate in dynamic environments use reaction systems . Although reactive systems have the advantages of fast execution and low overhead , But in terms of path optimization, there are still performance tradeoffs . Usually , The robot will eventually track the target , To follow the path of the goal , And deviating from this strategy is just to avoid collision with obstacles it may encounter . In the path planner , The path from the starting point to the target is calculated before the robot starts . If the target or obstacle changes position , The path must be recalculated . In a dynamic environment , This happens all the time . One compensation method is to consider the target speed and the speed of obstacles when planning the path . therefore , Robots don't have to follow their goals , It's about estimating the best place to reach your goal , And plan the path to the location . In this package , We simulate a method of path planning in a dynamic environment . This method uses the potential function method , In this method, time is regarded as a variable in the calculation of potential value . The potential value of a specific position and time represents the probability of collision between the robot and the obstacle , Suppose the robot starts to walk randomly from this position . The robot uses the current velocity to extrapolate the object's motion and calculates the potential value up to the predicted limit to plan the path , The potential value is determined by calculating the minimum path length using connectivity assessment and then determining the utility of expanding the foresight limit . This method is very fast , So if the initial conditions change during execution , You can re plan your path with little overhead .

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