Motion Planning for a Chain of Mobile Robots Using A* and Potential Field

Abstract

Traditionally, motion planning involved navigating one robot from source to goal for accomplishing a task. Now, tasks mostly require movement of a team of robots to the goal site, requiring a chain of robots to reach the desired goal. While numerous efforts are made in the literature for solving the problems of motion planning of a single robot and collective robot navigation in isolation, this paper fuses the two paradigms to let a chain of robot navigate. Further, this paper uses SLAM to first make a static map using a high-end robot, over which the physical low-sensing robots run. Deliberative Planning uses A* algorithm to plan the path. Reactive planning uses the Potential Field Approach to avoid obstacles and stay as close to the initial path planned as possible. These two algorithms are then merged to provide an algorithm that allows the robot to reach its goal via the shortest path possible while avoiding obstacles. The algorithm is further extended to multiple robots so that one robot is followed by the next robot and so on, thus forming a chain. In order to maintain the robots in a chain form, the Elastic Strip model is used. The algorithm proposed successfully executes the above stated when tested on Amigobot robots in an office environment using a map made by the Pioneer LX robot. The proposed algorithm works well for moving a group of robots in a chain in a mapped environment.

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment with Multi-Criteria Decision Making Using Full Consistency Method

“…This method generates kinematically feasible motions for multi-robot system. In [15], the A* algorithm in combination with potential field approach is used for path planning of a given set of mobile robots, while moving and avoiding obstacles in a chained fashion. The [16] focuses on consideration of path planning and controlling a group of autonomous agents to accomplish multiple tasks in dynamic environments.…”

“…The principle of D* Lite that is presented in Figure 3 can be summarized as follows: D* Lite performs searches by assigning the current cells of the robot and target to the start and goal cells of each search, respectively. The initialization process sets both the initial g and rhs values of all cells except the goal cell to infinity (lines [15][16]. The goal cell is inserted into the priority queue (OPEN) because it is initially inconsistent (line 17).…”

Robot Coverage Path Planning under Uncertainty Using Knowledge Inference and Hedge Algebras

“…Extending the proposed rule-based linguistic approach using semantics with kansei engineering in combination with hedge algebras forms an interesting research direction; • A further potentially profitable direction for research (in computing terms) lies in the use of semiotics [20] and SC [60] to recognise the type and nature of obstacles or other robots operating in the environment. Semiotics employs both linguistics and images to create a representative model, their combined use in context-aware intelligent robotic systems is a potentially profitable direction for robotics research; • There are potential use-cases where multiple mobile robots may operate collaboratively using for example "forward chaining" [62][63][64]; in such a use-case awareness of their environment and other robots operating in the same environment is required. For example, in a large search area multiple robots may be deployed to investigate an environment where efficient search requires both CPP for each robot while avoiding duplication in the search activity.…”

Optimizing an Autonomous Robot’s Path to Increase Movement Speed

“…Another alternative approach to trajectory planning involves two distinct modes of planning: deliberative and reactive. Deliberative methods require more computational resources and time, as they need to consider all possible options and their combinations [31][32][33][34]. These methods are suitable for static environments.…”