The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights.

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A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

The application of vision-guided multiwarehouse automated guided vehicles (AGVs) is becoming more extensive. This article mainly studies the position measurement and corresponding path planning of AGV guided by visual sensors. An improved vehicle-mounted visual sensor model is proposed, which effectively reduces the cost of the sensor and achieves position prediction. The proposed manual coding landmark significantly improves the decoding speed and accuracy while ensuring the information coding capacity. An improved heuristic path planning algorithm combined with the visual sensor system is implemented, considering the planning effectiveness and directionality. Finally, simulations and experiments verify the reliability of the visual guidance method in position measurement and the advancement of the planning strategy. Compared with the traditional 2-D code and bottom visual model, the improved visual sensor position measurement effectively reduces motion blur and corresponding pose deviation. The decoding speed and accuracy are improved by more than 41.21% and 53.85%, respectively. Planning performance comparison is achieved between the same type of heuristic algorithms (A*, D*, and algorithm deployment). The significant improvement in core system indicators includes task efficiency (up to 70.02% improvement), parking time (up to 72% reduction), deadlock (up to 85.05% reduction), and planning time (up to 89.39% reduction).

Improved Coding Landmark-Based Visual Sensor Position Measurement and Planning Strategy for Multiwarehouse Automated Guided Vehicle

In the field of aerial photography, UAVs can be used as an ideal carrier of sensors. It is meaningful to plan its coverage path combined with the sensor model. Previous works mainly used decentralized coverage to realize UAV formation cooperative operation, but for the aerial photography scene of a single task, the decentralized coverage algorithm is not effective. In this paper, a centralized coverage algorithm of UAV formation for aerial photography is proposed, which is divided into two sub-problems: coverage path planning and formation control. In the coverage path planning, due to high repetition rate and excessive number of turns of the traditional PRM algorithm, the constraint of the number of turns is introduced, and the back-and-forth mode is used to improve it. In formation control, in order to more accurately evaluate the performance of the coverage path, it refers to the consensus theory and combines the artificial potential field method to guide the reverse-V UAV formation to follow the coverage path. Multi-group simulations show that the proposed algorithm is feasible to implement the concentrated coverage for aerial photography under the comprehensive consideration of path length, number of turns, calculation time, coverage rate and repeating rate.

Concentrated Coverage Path Planning Algorithm of UAV Formation for Aerial Photography

Mobile robots are extensively used to complete repetitive operations in industrial areas such as intelligent transportation, logistics, and manufacturing systems. This paper addresses the path planning problem of multi-type robot systems with time windows based on timed colored Petri nets. The tasks to be completed are divided into three different types: common, exclusive and collaborative. An analytical approach to plan a group of different types of mobile robots is developed to ensure that some specific robots will visit task regions within given time windows. First, a multi-type robot system and its environment are modeled by a timed colored Petri net. Then, some methods are developed to convert the task requirements that contain logic constraints and time windows into linear constraints. Based on integer linear programming techniques, a planning approach is proposed to minimize the total cost (i.e., total travel distance) of the system. Finally, simulation studies are investigated to show the effectiveness of the developed approach.

https://www.mdpi.com/2076-3417/12/14/6878/pdf?version=1657198998

Path Planning of Multi-Type Robot Systems with Time Windows Based on Timed Colored Petri Nets

In multi-agent systems, the decision of an agent will be affected by the behaviors of others. Therefore, from the perspective of an agent, the situation is uncertain and random. Inspired by the social behaviors in the biological world, a novel multi-agent coverage path planning algorithm is proposed. Based on the positions of agents, the problem is decoupled, which can effectively reduce the dimension of the decision space. The behavior-guide-point is introduced to guide agents in making decisions, and a new motion mode is presented. To avoid falling into the local optimum, a cooperation mechanism is designed, which can improve the adaptability of the system. Through proximity interaction, the prediction results obtained via the model predictive control (MPC) technology are fused, evaluated, and sorted within the neighborhood, based on which decisions are gained. The proposed algorithm can handle emergencies in unknown environments such as body damage and moving obstacles, and can also be applied to heterogeneous systems. Simulation shows that compared with other algorithms, it has advantages in terms of the makespan and the coverage repetition rate.

Multi-Agent Coverage Path Planning via Proximity Interaction and Cooperation

In this paper, a new path planning algorithm is developed to address the problem of persistent and cooperative coverage using a group of robots in a complex environment. Previous works have mainly used geometric division to achieve collaborative coverage search, but ignored the environmental complexity and the path length difference of each robot. In contrast, this paper presents a novel strategy for multi-robot persistent coverage, which aims to obtain more equal coverage route for each robot while guaranteeing both the obstacle avoidance and the minimization of the coverage period. The strategy first deploys the sensors in the target region to satisfy coverage requirements. Then it classifies the sensor points by improved k-means clustering which introducing the feedback mechanism to balance the length of each route. Finally, the approach solves the travelling salesman problem to obtain the closed route for each robot. The numerical simulations show that the proposed approach is feasible to implement the cooperative and persistent coverage in consideration of complicated obstacles, equilibrium of the path length, and the minimization of total cost.

Yuan Tang

Papers

A Novel Cooperative Path Planning for Multirobot Persistent Coverage in Complex Environments