Tag Archives: Path Planning

A review of state-of-the-art path planning methods applied to autonomous driving

Mohamed Reda, Ahmed Onsy, Amira Y. Haikal, Ali Ghanbari, Path planning algorithms in the autonomous driving system: A comprehensive review, Robotics and Autonomous Systems, Volume 174, 2024 DOI: 10.1016/j.robot.2024.104630.

This comprehensive review focuses on the Autonomous Driving System (ADS), which aims to reduce human errors that are the reason for about 95% of car accidents. The ADS consists of six stages: sensors, perception, localization, assessment, path planning, and control. We explain the main state-of-the-art techniques used in each stage, analyzing 275 papers, with 162 specifically on path planning due to its complexity, NP-hard optimization nature, and pivotal role in ADS. This paper categorizes path planning techniques into three primary groups: traditional (graph-based, sampling-based, gradient-based, optimization-based, interpolation curve algorithms), machine and deep learning, and meta-heuristic optimization, detailing their advantages and drawbacks. Findings show that meta-heuristic optimization methods, representing 23% of our study, are preferred for being general problem solvers capable of handling complex problems. In addition, they have faster convergence and reduced risk of local minima. Machine and deep learning techniques, accounting for 25%, are favored for their learning capabilities and fast responses to known scenarios. The trend towards hybrid algorithms (27%) combines various methods, merging each algorithm’s benefits and overcoming the other’s drawbacks. Moreover, adaptive parameter tuning is crucial to enhance efficiency, applicability, and balancing the search capability. This review sheds light on the future of path planning in autonomous driving systems, helping to tackle current challenges and unlock the full capabilities of autonomous vehicles.

Using Deep RL (TRPO) for selecting best interest points in the environment for path planning

Jie Fan, Xudong Zhang, Yuan Zou, Hierarchical path planner for unknown space exploration using reinforcement learning-based intelligent frontier selection, Expert Systems with Applications, Volume 230, 2023 DOI: 10.1016/j.eswa.2023.120630.

Path planning in unknown environments is extremely useful for some specific tasks, such as exploration of outer space planets, search and rescue in disaster areas, home sweeping services, etc. However, existing frontier-based path planners suffer from insufficient exploration, while reinforcement learning (RL)-based ones are confronted with problems in efficient training and effective searching. To overcome the above problems, this paper proposes a novel hierarchical path planner for unknown space exploration using RL-based intelligent frontier selection. Firstly, by decomposing the path planner into three-layered architecture (including the perception layer, planning layer, and control layer) and using edge detection to find potential frontiers to track, the path search space is shrunk from the whole map to a handful of points of interest, which significantly saves the computational resources in both training and execution processes. Secondly, one of the advanced RL algorithms, trust region policy optimization (TRPO), is used as a judge to select the best frontier for the robot to track, which ensures the optimality of the path planner with a shorter path length. The proposed method is validated through simulation and compared with both classic and state-of-the-art methods. Results show that the training process could be greatly accelerated compared with the traditional deep-Q network (DQN). Moreover, the proposed method has 4.2%\u201314.3% improvement in exploration region rate and achieves the highest exploration completeness.

New algorithms for optimal path planning with certain optimality guarantees

Strub MP, Gammell JD. Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*): Asymmetric bidirectional sampling-based path planning, The International Journal of Robotics Research. 2022;41(4):390-417 DOI: 10.1177/02783649211069572.

Optimal path planning is the problem of finding a valid sequence of states between a start and goal that optimizes an objective. Informed path planning algorithms order their search with problem-specific knowledge expressed as heuristics and can be orders of magnitude more efficient than uninformed algorithms. Heuristics are most effective when they are both accurate and computationally inexpensive to evaluate, but these are often conflicting characteristics. This makes the selection of appropriate heuristics difficult for many problems. This paper presents two almost-surely asymptotically optimal sampling-based path planning algorithms to address this challenge, Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*). These algorithms use an asymmetric bidirectional search in which both searches continuously inform each other. This allows AIT* and EIT* to improve planning performance by simultaneously calculating and exploiting increasingly accurate, problem-specific heuristics. The benefits of AIT* and EIT* relative to other sampling-based algorithms are demonstrated on 12 problems in abstract, robotic, and biomedical domains optimizing path length and obstacle clearance. The experiments show that AIT* and EIT* outperform other algorithms on problems optimizing obstacle clearance, where a priori cost heuristics are often ineffective, and still perform well on problems minimizing path length, where such heuristics are often effective.

Modifications of Q-learning for better learning of robot navigation

Ee Soong Low, Pauline Ong, Cheng Yee Low, Rosli Omar, Modified Q-learning with distance metric and virtual target on path planning of mobile robot, Expert Systems with Applications, Volume 199, 2022, DOI: 10.1016/j.eswa.2022.117191.

Path planning is an essential element in mobile robot navigation. One of the popular path planners is Q-learning \u2013 a type of reinforcement learning that learns with little or no prior knowledge of the environment. Despite the successful implementation of Q-learning reported in numerous studies, its slow convergence associated with the curse of dimensionality may limit the performance in practice. To solve this problem, an Improved Q-learning (IQL) with three modifications is introduced in this study. First, a distance metric is added to Q-learning to guide the agent moves towards the target. Second, the Q function of Q-learning is modified to overcome dead-ends more effectively. Lastly, the virtual target concept is introduced in Q-learning to bypass dead-ends. Experimental results across twenty types of navigation maps show that the proposed strategies accelerate the learning speed of IQL in comparison with the Q-learning. Besides, performance comparison with seven well-known path planners indicates its efficiency in terms of the path smoothness, time taken, shortest distance and total distance used.

Analysis of the under-optimality of path lengths when path planning is carried out on a grid instead of the continuous world

James P. Bailey, Alex Nash, Craig A. Tovey, Sven Koenig, Path-length analysis for grid-based path planning, Artificial Intelligence, Volume 301, 2021, DOI: 10.1016/j.artint.2021.103560.

In video games and robotics, one often discretizes a continuous 2D environment into a regular grid with blocked and unblocked cells and then finds shortest paths for the agents on the resulting grid graph. Shortest grid paths, of course, are not necessarily true shortest paths in the continuous 2D environment. In this article, we therefore study how much longer a shortest grid path can be than a corresponding true shortest path on all regular grids with blocked and unblocked cells that tessellate continuous 2D environments. We study 5 different vertex connectivities that result from both different tessellations and different definitions of the neighbors of a vertex. Our path-length analysis yields either tight or asymptotically tight worst-case bounds in a unified framework. Our results show that the percentage by which a shortest grid path can be longer than a corresponding true shortest path decreases as the vertex connectivity increases. Our path-length analysis is topical because it determines the largest path-length reduction possible for any-angle path-planning algorithms (and thus their benefit), a class of path-planning algorithms in artificial intelligence and robotics that has become popular.

A unifying framework for path planning in real-time (mainly for UAVs) and a nice summary of the state-of-the-art in modern path planning

M. Murillo, G. SánchezL. GenzelisL. Giovanini, A Real-Time Path-Planning Algorithm based on Receding Horizon Techniques, Journal of Intelligent & Robotic Systems, September 2018, Volume 91, Issue 3–4, pp 445–457, DOI: 10.1007/s10846-017-0740-1.

In this article we present a real-time path-planning algorithm that can be used to generate optimal and feasible paths for any kind of unmanned vehicle (UV). The proposed algorithm is based on the use of a simplified particle vehicle (PV) model, which includes the basic dynamics and constraints of the UV, and an iterated non-linear model predictive control (NMPC) technique that computes the optimal velocity vector (magnitude and orientation angles) that allows the PV to move toward desired targets. The computed paths are guaranteed to be feasible for any UV because: i) the PV is configured with similar characteristics (dynamics and physical constraints) as the UV, and ii) the feasibility of the optimization problem is guaranteed by the use of the iterated NMPC algorithm. As demonstration of the capabilities of the proposed path-planning algorithm, we explore several simulation examples in different scenarios. We consider the existence of static and dynamic obstacles and a follower condition.

A new variant of A* that is more computationally efficient

Adam Niewola, Leszek Podsedkowski, L* Algorithm—A Linear Computational Complexity Graph Searching Algorithm for Path Planning, Journal of Intelligent & Robotic Systems, September 2018, Volume 91, Issue 3–4, pp 425–444, DOI: 10.1007/s10846-017-0748-6.

The state-of-the-art graph searching algorithm applied to the optimal global path planning problem for mobile robots is the A* algorithm with the heap structured open list. In this paper, we present a novel algorithm, called the L* algorithm, which can be applied to global path planning and is faster than the A* algorithm. The structure of the open list with the use of bidirectional sublists (buckets) ensures the linear computational complexity of the L* algorithm because the nodes in the current bucket can be processed in any sequence and it is not necessary to sort the bucket. Our approach can maintain the optimality and linear computational complexity with the use of the cost expressed by floating-point numbers. The paper presents the requirements of the L* algorithm use and the proof of the admissibility of this algorithm. The experiments confirmed that the L* algorithm is faster than the A* algorithm in various path planning scenarios. We also introduced a method of estimating the execution time of the A* and the L* algorithm. The method was compared with the experimental results.

A new spatial transformation for planning mobile robot trajectories that guarantees a solution and also time requirement satisfaction

S. G. Loizou, The Navigation Transformation, IEEE Transactions on Robotics, vol. 33, no. 6, pp. 1516-1523, DOI: 10.1109/TRO.2017.2725323.

This work introduces a novel approach to the solution of the navigation problem by mapping an obstacle-cluttered environment to a trivial domain called the point world, where the navigation task is reduced to connecting the images of the initial and destination configurations by a straight line. Due to this effect, the underlying transformation is termed the “navigation transformation.” The properties of the navigation transformation are studied in this work as well as its capability to provide-through the proposed feedback controller designs-solutions to the motion- and path-planning problems. Notably, the proposed approach enables the construction of temporal stabilization controllers as detailed herein, which provide a time abstraction to the navigation problem. The proposed solutions are correct by construction and, given a diffeomorphism from the workspace to a sphere world, tuning free. A candidate construction for the navigation transformation on sphere worlds is proposed. The provided theoretical results are backed by analytical proofs. The efficiency, robustness, and applicability of the proposed solutions are supported by a series of experimental case studies.

State of the art of symbolic planning, particularly the one that optimizes some cost, and a novel approach

Álvaro Torralba, Vidal Alcázar, Peter Kissmann, Stefan Edelkamp, Efficient symbolic search for cost-optimal planning, Artificial Intelligence, Volume 242, January 2017, Pages 52-79, ISSN 0004-3702, DOI: 10.1016/j.artint.2016.10.001.

In cost-optimal planning we aim to find a sequence of operators that achieve a set of goals with minimum cost. Symbolic search with Binary Decision Diagrams (BDDs) performs efficient state space exploration in terms of time and memory. This is crucial in optimal settings, in which large parts of the state space must be explored in order to prove optimality. However, the development of accurate heuristics for explicit-state search in recent years have left symbolic search techniques in a secondary place. In this article we propose two orthogonal improvements for symbolic search planning. On the one hand, we analyze and compare different methods for image computation in order to efficiently perform the successor generation on symbolic search. Image computation is the main bottleneck of symbolic search algorithms so an efficient computation is paramount for efficient symbolic search planning. On the other hand, we study how to use state-invariant constraints to prune states in symbolic search. This is essential in regression search but it is yet to be exploited in symbolic search planners. Experiments with symbolic bidirectional uniform-cost search and symbolic A ⁎ search with PDBs show remarkable performance improvements on most IPC benchmark domains. Overall, with the help of our improvements, symbolic bidirectional search outperforms explicit-state search with state-of-the-art heuristics such as LM-cut across many different domains.

Survey and taxonomy of path planning algorithms

Thi Thoa Mac, Cosmin Copot, Duc Trung Tran, Robin De Keyser, Heuristic approaches in robot path planning: A survey, Robotics and Autonomous Systems, Volume 86, 2016, Pages 13-28, ISSN 0921-8890, DOI: 10.1016/j.robot.2016.08.001.

Autonomous navigation of a robot is a promising research domain due to its extensive applications. The navigation consists of four essential requirements known as perception, localization, cognition and path planning, and motion control in which path planning is the most important and interesting part. The proposed path planning techniques are classified into two main categories: classical methods and heuristic methods. The classical methods consist of cell decomposition, potential field method, subgoal network and road map. The approaches are simple; however, they commonly consume expensive computation and may possibly fail when the robot confronts with uncertainty. This survey concentrates on heuristic-based algorithms in robot path planning which are comprised of neural network, fuzzy logic, nature-inspired algorithms and hybrid algorithms. In addition, potential field method is also considered due to the good results. The strengths and drawbacks of each algorithm are discussed and future outline is provided.