Path planning of lunar robot based on dynamic adaptive ant colony algorithm and obstacle avoidance

Abstract

Path planning of lunar robots is the guarantee that lunar robots can complete tasks safely and accurately. Aiming at the shortest path and the least energy consumption, an adaptive potential field ant colony algorithm suitable for path planning of lunar robot is proposed to solve the problems of slow convergence speed and easy to fall into local optimum of ant colony algorithm. This algorithm combines the artificial potential field method with ant colony algorithm, introduces the inducement heuristic factor, and adjusts the state transition rule of the ant colony algorithm dynamically, so that the algorithm has higher global search ability and faster convergence speed. After getting the planned path, a dynamic obstacle avoidance strategy is designed according to the predictable and unpredictable obstacles. Especially a geometric method based on moving route is used to detect the unpredictable obstacles and realize the avoidance of dynamic obstacles. The experimental results show that the improved adaptive potential field ant colony algorithm has higher global search ability and faster convergence speed. The designed obstacle avoidance strategy can effectively judge whether there will be collision and take obstacle avoidance measures.

Fast Path Planning for Long-Range Planetary Roving Based on a Hierarchical Framework and Deep Reinforcement Learning

“…Conventionally, given computable configuration spaces, such as the Voronoi diagrams or grid maps, the right path from the start point to the target can be solved by a wide range of searching algorithms, such as the A* [8,9], the SD*lite [1], the RRT [10,11], and the ant colony algorithm [12], etc. Various improvements have been made to these methods.…”

“…First, the original orbital images are interpreted into computable configuration spaces in which free space and obstacle space are identified. Then, the right path can be identified by some path searching algorithms, such as the A* [8,9], the rapidly exploring random trees (RRT) [10,11], the ant colony algorithm [12], and the genetic algorithm [13], etc. These methods are operable in most instances, but may suffer from the exponential explosion of computation time when it comes to wide-range or high-resolution maps.…”

An Improved Beetle Swarm Optimization Algorithm for the Intelligent Navigation Control of Autonomous Sailing Robots

“…Although the ability of a single ant is limited, after multiple ants mark the path, the entire ant colony will tend to the optimal path. In the application of path planning, Zhu [52] combined the artificial potential field method with the ant colony algorithm, introduced inducing heuristic factors, and dynamically adjusted the state transition rules of the ant colony algorithm, which has higher global search capabilities and a faster convergence speed. The ABC is a global optimization algorithm based on swarm intelligence.…”

The optimal path of robot end effector based on hierarchical clustering and Bézier curve with three shape parameters

“…An ant colony merged with the artificial potential field method-based path planning algorithm is proposed for lunar robots to determine the shortest path besides obtaining the reduced convergence speed in the environment with dynamic obstacles in ref. [16]. Xie et al [17] presented an algorithm for the multi-joint manipulator to obtain the optimal path to avoid obstacles in a workspace.…”