Mobile Robot Navigation and Path Planning Techniques
Summary
Mobile robot navigation and path planning encompass the methods by which autonomous platforms perceive their surroundings, build internal representations and compute safe, efficient trajectories from origin to destination. Environments may be modelled as discrete grids, continuous configuration spaces or topological graphs, each affording distinct advantages in resolution and computational load. Classical graph-search techniques such as Dijkstra’s algorithm and A* employ cost metrics and heuristic functions to guarantee optimality in static settings, while sampling-based planners including Rapidly-Exploring Random Trees (RRT) and Probabilistic Roadmaps (PRM) offer scalable solutions in high-dimensional or complex terrain. Artificial potential field approaches impose virtual forces to drive motion but must address local minima through modifications or hybridisation. More recently, bio-inspired metaheuristics—such as ant colony optimisation and particle swarm optimisation—have been used to navigate cluttered environments by balancing exploration and exploitation. The advent of deep learning and reinforcement learning has enabled robots to learn navigation policies directly from sensor inputs, improving adaptability in dynamic or partially known scenes. Integration with simultaneous localisation and mapping (SLAM) allows continuous updating of the map and re-planning in real time, supporting multi-robot coordination and applications ranging from warehouse logistics and urban delivery to planetary exploration and search-and-rescue operations.
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Mobile Robot Navigation and Path Planning Techniques publication trend
The graph below shows the total number of articles in mobile robot navigation and path planning techniques across all publications each year (not limited to Nature Index journals).
Technical terms
Grid map: A discrete spatial representation dividing the environment into uniform cells for navigation and collision checking.
A* algorithm: A graph-search method that uses a heuristic function to find a least-cost path between two nodes.
Ant colony optimisation: A metaheuristic inspired by pheromone-based foraging to solve combinatorial path planning by stochastic exploration.
Probabilistic Roadmap (PRM): A sampling-based planner that constructs a graph of collision-free configurations connected by feasible paths.
Deep reinforcement learning: A technique combining deep neural networks and reward-driven policy optimisation for decision-making in complex environments.
References
- Intelligent path planning for cognitive mobile robot based on Dhouib-Matrix-SPP method. Cognitive Robotics (2024).
- Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment. IEEE Access (2021).
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