Aug 2026· Theoretical and Natural Science· 0 citations
TL;DR
It is argued that the future of robot path planning will be dominated by hybrid systems that combine global planning, local replanning, optimization, and learning-based prediction, enabling robots to operate more safely, intelligently, and adaptively in complex real-world environments.
Abstract
Robot path planning is a fundamental problem in robotics enabling autonomous robots to navigate safely, efficiently and naturally from a start position to a target position. In real robotic systems, path planning is not only about finding a collision-free path, but also about generating motions that satisfy the robot's physical, sensory, and task constraints. In this essay, the main robot path planning methods, including graph search, artificial potential field methods, sampling-based planning, local obstacle avoidance, and trajectory optimization are reviewed. Their respective strengths, limitations, and applicable scenarios are examined, with particular attention to how these approaches address issues such as computational complexity, environmental structure, and real-time responsiveness. It also addresses present day issues such as uncertainty, dynamic environments, computational efficiency and physical feasibility, emphasizing that no single method is sufficient for all robotic applications. Practical systems increasingly rely on hierarchical integration to balance global navigation with local reactivity. Finally, the essay argues that the future of robot path planning will be dominated by hybrid systems that combine global planning, local replanning, optimization, and learning-based prediction, enabling robots to operate more safely, intelligently, and adaptively in complex real-world environments.
A node detection strategy grounded in the safe workspace effectively prevents collisions between the generated path and surrounding obstacles, and a two-stage heuristic search strategy is designed, incorporating an intermediate node mechanism to substantially enhance search efficiency.
Xin-Guang Li, Shilong Zhao, Xiao-Qi Guo· Proceedings of the Instituti...· 0 citations
The problem of path planning is one of the most crucial and challenging issues in the fields of intelligent systems and autonomous robotics. A robot’s ability to move quickly and easily from a starting position to a goal position without hitting anything is directly related to how useful the robot is in real life. This paper compares five advanced path-planning algorithms: A* (A-Star), D* Lite (Dynamic A-Star Lite), RRT* (Rapidly exploring Random Tree Star), PRM* (Probabilistic Roadmap Star), and APF-D (Adaptive Potential Field with Dynamic Awareness). The paper addresses the difficult problem in dynamic environments where objects enter, exit, and move around continuously within the robot environment, as it moves through the environment, which is becoming more prevalent in the real world, such as in warehouses, hospitals, and urban and outdoor environments. Six performance measures, namely path length, computation time, memory, optimality ratio, success rate, and replanning latency, are used to test our five algorithms on a standard simulator in Matlab. Experiments are conducted in four different conditions, from very quiet to very dynamic, with a high number of obstacles. Results indicate that A* fails to perform well in dynamic environments and performs nearly optimally in static environments, while APF-D and D* Lite adapt to changes in the environment much better. An experimental study was carried out by 50 independent simulations in static and dynamic environments, where in each simulation, the hybrid solution was evaluated. The APF-D algorithm showed a success rate of 92.8% in highly dynamic environments, which is better than that of A* (58.8%), RRT* (76.8%), and PRM* (70.5%), whereas the success rate of D* Lite was found to be 89.3%. Additionally, APF-D decreased the average time taken for replanning by around 25% in comparison to other graph-based algorithms.
Prathyusha Vinukonda, V. Manikandan· Robotics· 0 citations
: This paper provides a thorough survey and integrative presentation of cooperative path planning for multi-robot systems operating in dynamic, cluttered, and partially observable environments. People synthesise algorithmic foundations ranging from heuristic graph search to sampling-based motion planners, including A*, D* Lite, and Safe Interval Path Planning for discrete/time-augmented spaces, as well as RRT, RRT*, and Informed RRT* for continuous configuration spaces. Multi-agent coordination techniques are reviewed, covering reciprocal collision avoidance (ORCA) and centralised Multi-Agent Path Finding (MAPF) solvers such as Conflict-Based Search (CBS) and bounded-suboptimal variants (ECBS). The paper also examine control and safety layers like Model Predictive Control and Control Barrier Functions that translate plans into dynamically feasible commands with safety guarantees. Recent progress in cooperative multi-agent reinforcement learning (MAPPO, QMIX, MADDPG) is evaluated for adaptability under partial observability and nonstationary environments. Applications in warehousing, intelligent transportation, and disaster response are used to illustrate practical trade-offs and integration patterns, referencing real-world systems such as Kiva-style warehouse fleets and autonomous driving pipelines. The paper concludes with a focused discussion on open challenges — scalability with guarantees, safety under uncertainty, sim-to-real transfer, and planning – control interface fragility — and proposes research directions including learning-augmented heuristics, unified safety-aware planning, adaptive MPC – CBF filters, and more informative benchmarks to drive reproducible progress.
Yun Pan· Proceedings of the 3rd Inter...· 0 citations
This work presents a prioritized Safe Interval Path Planning algorithm (SIPP-PP) with a novel limited goal reservation strategy to prevent goal-blocking conflicts while allowing shared goal regions, and demonstrates a multi-robot planner capable of real-time operation in dense scenarios, satisfying the stringent requirements of industrial applications such as drive units in fulfillment centers.
Rajat Kumar, Kristin Predeck, Ken Meszaros et al.· 0 citations
A softmin-based adaptive blending mechanism that automatically selects the most suitable path from the champion solutions on the Pareto front according to regional environmental conditions, thereby eliminating the need for manual user intervention is introduced.
Osman Emre Turan, Oğuz Mısır, Mustafa Özden· Measurement science and tech...· 0 citations
This follow-up work tests the feasibility of the neuro-inspired self-supervised learning framework for trajectory planning that leverages forward and inverse models as the internal supervisory mechanism in an environment that contains an obstacle, and demonstrates the tendency of the planner to exploit the learning signal provided by the forward and inverse models.
M. Krupa, Miroslav Cibula, Kristína Malinovská· arXiv.org· 0 citations
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