Figure 1.
Line graph of number of papers vs. year published
Table 1.
Summary of hybrid approaches methods for mobile robot path planning
| Paper | Global Planner Algorithm | Local Planner Algorithm | Strength |
|---|---|---|---|
| [6] | IADA* | Reinforcement Learning (RL) | - IADA* algorithm can re-plan paths efficiently in dynamic environments without recalculating the entire path when an obstacle is encountered. |
| [15] | — | Deep Reinforcement Learning (DRL) | - The human-in-the-loop (HL) training speeds up the convergence of the DRL algorithm, reducing the time required to learn complex navigation policies. |
| [7] | A* | Reinforcement Learning (RL) | - The RL is allowing the robot to adapt its policy through trial-and-error interactions with its surroundings. |
| [13] | PRM | Reinforcement Learning (RL) | - PRM is triggered using an updated probabilistic roadmap, if RL fails to find a valid path due to obstacles detected. |
| [10] | RRT | Reinforcement Learning (RL) | - RL learns to select optimal actions that lead to collision-free paths, while RRT generates collision-free states. |
| [8] | A* | Reinforcement Learning (RL) | - The approach can be scaled to multi-robot systems without centralized control. |
| [9] | RRT | Reinforcement Learning (RL) | - RL helps the RRT tree grow toward target point, avoiding computationally expensive steering functions. |
| [11] | PRM | Reinforcement Learning (RL) | - PRM-RL combines the strengths of PRMs for long-range planning with RL agents that handle short-range. |
| [12] | PRM | Reinforcement Learning (RL) | - PRM-RL is designed to be robust against sensor noise and unmodeled dynamic environments. |
| [14] | A*, Dijkstra | Deep Reinforcement Learning (DRL) | - The DRL is specifically trained to navigate around humans, predicting their movements and adjusting robot trajectories accordingly to avoid close encounters. |
| [17] | RRT* | Back Propagation (BP) Neural Networks (NNs) | - BP-RRT* method uses neural networks to predict the optimal number of samples required in each phase of the search, making it faster and more efficient. - reducing the computational process by optimizing the node selection process |
| [18] | A*, RRT* | Neural Networks (NNs) | - RNN continuously learns from the environment, making it adaptable and faster in generating paths. - RNN allows it to operate in a relatively constant time regardless of environmental complexity. |
| [19] | A*, RRT* | Neural Networks (NNs) | - Limitation learning from pre-calculated optimal paths making this method unique on real-time calculations. - The use of R-CNN allows for fast computation by leveraging offline-trained models, reducing the need for heavy real-time computations |
| [24] | RRT | Fuzzy Logic | - fuzzy logic is computationally light and well-suited for real-time operations. - An extended Kalman filter (EKF) is employed to minimize cross-track errors during path following, ensuring smooth and accurate navigation along the planned trajectory |
| [22] | PRM | Deep Reinforcement Learning (DRL) | - PMR-Dueling DQN utilizes prioritized replay and dueling networks, improving the learning process by focusing on more critical learning events and better approximating state-action values. |
| [20] | — | Deep Neural Network (DNN) | - DNN is used to optimize the heuristic function, allowing it to maintain the strengths of traditional search algorithms while improving efficiency |
| [27] | A* | Neural Networks (NNs) | - Learning Heuristic A* (LHA*) algorithm uses a neural network to model the heuristic function. - The neural network reduces the number of unnecessary vertex expansions in a graph, speeding up the search process. |
| [21] | A* | Neural Networks (NNs) | - Neural A* combines learning and search into a unified framework, which allows for both task optimization and improved performance. |
| [23] | — | Deep Neural Network (DNN) | - OMAP does not require large datasets or neural network training, making it a simple yet powerful alternative for solving complex path-planning problems. |
| [28] | DWA | Fuzzy logic | - Important points on the global path are selected as key sub-target sites for the local motion planning phase. |
| [25] | A* | Fuzzy logic | - Significantly reduces computation and memory usage in large, complex environments while maintaining near-optimal paths. |
| [26] | Dijkstra | Fuzzy logic | - Ensures globally optimal offline planning with adaptive real-time obstacle avoidance in partially known environments |
Figure 2.
Comparison of the optimal solutions between the integration of RRT with Neural Network, RRT, and Improved RRT* [16]
Table 2.
Comparison of the success rates between the integration of iADA with AI techniques and standalone iADA [6]
| Static environment | Algorithms | ||
|---|---|---|---|
| IADA* | IADA*+DQN | IADA*+DDPG | |
| Success | 47 | 50 | 50 |
| Failure | 3 | - | - |
| Success rate | 94% | 100% | 100% |
| Dynamic environment | Algorithms | ||
| IADA* | IADA*+DQN | IADA*+DDPG | |
| Success | 37 | 47 | 45 |
| Failure | 13 | 3 | 5 |
| Success rate | 74% | 94% | 90% |
Table 3.
Comparison of convergence time between PRM+DQN, DQN, DDQN, and Q-learning [22]
| Method | Algorithm comparison on environment E-2 | |
|---|---|---|
| Success rate | Convergence Time/min | |
| Q-learning | 26.7 | 268 |
| DQN | 49.3 | 139 |
| DDQN | 56.4 | 116 |
| PMR-Dueling DQN | 84.6 | 107 |
| Method | Algorithm comparison on environment E-3 | |
| Success rate | Convergence Time/min | |
| Q-learning | 21.4 | 276 |
| DQN | 32.6 | 161 |
| DDQN | 41.5 | 143 |
| PMR-Dueling DQN | 79.6 | 122 |
Table 4.
Comparison of convergence speed between BP-RRT, RRT*, RRT, and IRRT* [17]
| Algorithm name | Avarage search time/s | Avarage number of nodes samples |
|---|---|---|
| RRT | 21.04 | 5293.6 |
| RRT* | 23.31 | 1366.7 |
| Improved P-RRT* | 16.70 | 1482.5 |
| BP-RRT* | 12.76 | 1044.7 |