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Assisting individuals in their daily activities through autonomous mobile robots is a significant concern, especially for users without specialized knowledge. Specifically, the capability of a robot to navigate to destinations based on human speech instructions is crucial. Although robots can take different paths toward the same objective, the shortest path is not always the most suitable. A preferred approach would be to accommodate waypoint specifications flexibly for planning an improved alternative path even with detours. Furthermore, robots require real-time inference capabilities. In this sense, spatial representations include semantic, topological, and metric-level representations, each capturing different aspects of the environment. This study aimed to realize a hierarchical spatial representation using a topometric semantic map and path planning with speech instructions by including waypoints. Thus, we present a hierarchical path planning method called spatial concept-based topometric semantic mapping for hierarchical path planning (SpCoTMHP), which integrates place connectivity. This approach provides a novel integrated probabilistic generative model and fast approximate inferences with interactions among the hierarchy levels. A formulation based on “control as probabilistic inference” theoretically supports the proposed path planning algorithm. We conducted experiments in a home environment using the Toyota human support robot on the SIGVerse simulator and in a lab–office environment with the real robot Albert. Here, the user issues speech commands that specify the waypoint and goal, such as “Go to the bedroom via the corridor.” Navigation experiments were performed using speech instructions with a waypoint to demonstrate the performance improvement of the SpCoTMHP over the baseline hierarchical path planning method with heuristic path costs (HPP-I) in terms of the weighted success rate at which the robot reaches the closest target (0.590) and passes the correct waypoints. The computation time was significantly improved by 7.14 s with the SpCoTMHP than the baseline HPP-I in advanced tasks. Thus, hierarchical spatial representations provide mutually understandable instruction forms for both humans and robots, thus enabling language-based navigation.

To enable autonomous vehicles to generate smooth and collision-free trajectories and improve their driving performance on structured roads, this paper proposes a hierarchical trajectory planning algorithm based on an improved artificial potential field method. To improve the applicability of the algorithm to complex scenarios, the Frenet coordinate system was established to address these limitations. First, the safety distance model is applied to the risk assessment of the improved artificial potential field method. Then, the hierarchical solution is carried out, and the road solvable convex space and the rough path solution are solved by combining the artificial potential field method. On this basis, the potential field term and the smoothing term cost function are established, and the sequential quadratic programming (SQP) algorithm is used to solve the exact path that meets the requirements of safety and smoothness. Hierarchical planning shortens the solution time by quickly determining the bounds of the convex space. Finally, in the speed planning, in order to take into account the comfort and safety of the occupants, the speed curve is solved by considering the dynamic constraints of the vehicle. The obstacle avoidance effects of the algorithm on static and dynamic obstacles are tested in different simulation scenarios. The results of the simulation experiment show that the proposed algorithm can successfully achieve obstacle avoidance on complex structured roads. [ABSTRACT FROM AUTHOR]

Path planning for wheeled mobile robots on partially known uneven terrain is an open challenge since robot motions can be strongly influenced by terrain with incomplete environmental information such as locally detected obstacles and impassable terrain areas. This paper proposes a hierarchical path planning approach for a wheeled robot to move in a partially known uneven terrain. We first model the partially known uneven terrain environment respecting the terrain features, including the slope, step, and unevenness. Second, facilitated by the terrain model, we use A ⋆ algorithm to plan a global path for the robot based on the partially known map. Finally, the Q-learning method is employed for local path planning to avoid locally detected obstacles in close range as well as impassable terrain areas when the robot tracks the global path. The simulation and experimental results show that the designed path planning approach provides satisfying paths that avoid locally detected obstacles and impassable areas in a partially known uneven terrain compared with the classical A ⋆ algorithm and the artificial potential field method. [ABSTRACT FROM AUTHOR]

Assisting individuals in their daily activities through autonomous mobile robots is a significant concern, especially for users without specialized knowledge. Specifically, the capability of a robot to navigate to destinations based on human speech instructions is crucial. Although robots can take different paths toward the same objective, the shortest path is not always the most suitable. A preferred approach would be to accommodate waypoint specifications flexibly for planning an improved alternative path even with detours. Furthermore, robots require real-time inference capabilities. In this sense, spatial representations include semantic, topological, and metric-level representations, each capturing different aspects of the environment. This study aimed to realize a hierarchical spatial representation using a topometric semantic map and path planning with speech instructions by including waypoints. Thus, we present a hierarchical path planning method called spatial concept-based topometric semantic mapping for hierarchical path planning (SpCoTMHP), which integrates place connectivity. This approach provides a novel integrated probabilistic generative model and fast approximate inferences with interactions among the hierarchy levels. A formulation based on "control as probabilistic inference" theoretically supports the proposed path planning algorithm. We conducted experiments in a home environment using the Toyota human support robot on the SIGVerse simulator and in a lab-office environment with the real robot Albert. Here, the user issues speech commands that specify the waypoint and goal, such as "Go to the bedroom via the corridor . " Navigation experiments were performed using speech instructions with a waypoint to demonstrate the performance improvement of the SpCoTMHP over the baseline hierarchical path planning method with heuristic path costs (HPP-I) in terms of the weighted success rate at which the robot reaches the closest target (0.590) and passes the correct waypoints. The computation time was significantly improved by 7.14 s with the SpCoTMHP than the baseline HPP-I in advanced tasks. Thus, hierarchical spatial representations provide mutually understandable instruction forms for both humans and robots, thus enabling language-based navigation., Competing Interests: The authors declare that the research was conducted without any commercial or financial relationships that may be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Taniguchi, Ito and Taniguchi.)

In this paper, a novel path planning algorithm with Reinforcement Learning is proposed based on the topological map. The proposed algorithm has a two-level structure. At the first level, the proposed method generates the topological area using the region dynamic growth algorithm based on the grid map. In the next level, the Multi-SARSA method divided into two layers is applied to find a near-optimal global planning path, in which the artificial potential field method, first of all, is used to initialize the first Q table for faster learning speed, and then the second Q table is initialized with the connected domain obtained by topological map, which provides the prior information. A combination of the two algorithms makes the algorithm easier to converge. Simulation experiments for path planning have been executed. The results indicate that the method proposed in this paper can find the optimal path with a shorter path length, which demonstrates the effectiveness of the presented method. [ABSTRACT FROM AUTHOR]

Under unforeseen circumstances, a hierarchical path planning (HPP) scheme combining global and local tasks of an unmanned surface vehicle (USV) is proposed by devising fuzzy artificial potential field (FAPF). Within the HPP scheme, the elite reservation, diversity increment, adaptive mutation probability and adaptive genetic algorithm are incorporated to generate optimally sparse waypoints together with smooth path. To exclusively accommodate unpredictable environments, the innovative FAPF method with fuzzy decision-making is developed to remove singular dilemmas, pertaining to which the obstacles on the path make the USV hesitate. By inserting virtual returning points onto the global path, a hybrid compound combining the global and local paths is established. Finally, the simulations and comparisons in various geographical areas demonstrate the effectiveness and superiority of the proposed HPP scheme. [ABSTRACT FROM AUTHOR]

Path planning for wheeled mobile robots on partially known uneven terrain is an open challenge since robot motions can be strongly influenced by terrain with incomplete environmental information such as locally detected obstacles and impassable terrain areas. This paper proposes a hierarchical path planning approach for a wheeled robot to move in a partially known uneven terrain. We first model the partially known uneven terrain environment respecting the terrain features, including the slope, step, and unevenness. Second, facilitated by the terrain model, we use A⋆ algorithm to plan a global path for the robot based on the partially known map. Finally, the Q-learning method is employed for local path planning to avoid locally detected obstacles in close range as well as impassable terrain areas when the robot tracks the global path. The simulation and experimental results show that the designed path planning approach provides satisfying paths that avoid locally detected obstacles and impassable areas in a partially known uneven terrain compared with the classical A⋆ algorithm and the artificial potential field method.

In this paper, a novel path planning algorithm with Reinforcement Learning is proposed based on the topological map. The proposed algorithm has a two-level structure. At the first level, the proposed method generates the topological area using the region dynamic growth algorithm based on the grid map. In the next level, the Multi-SARSA method divided into two layers is applied to find a near-optimal global planning path, in which the artificial potential field method, first of all, is used to initialize the first Q table for faster learning speed, and then the second Q table is initialized with the connected domain obtained by topological map, which provides the prior information. A combination of the two algorithms makes the algorithm easier to converge. Simulation experiments for path planning have been executed. The results indicate that the method proposed in this paper can find the optimal path with a shorter path length, which demonstrates the effectiveness of the presented method.

In this paper, we present a hierarchical path planning framework called SG–RL (subgoal graphs–reinforcement learning), to plan rational paths for agents maneuvering in continuous and uncertain environments. By "rational", we mean (1) efficient path planning to eliminate first-move lags; (2) collision-free and smooth for agents with kinematic constraints satisfied. SG–RL works in a two-level manner. At the first level, SG–RL uses a geometric path-planning method, i.e., simple subgoal graphs (SSGs), to efficiently find optimal abstract paths, also called subgoal sequences. At the second level, SG–RL uses an RL method, i.e., least-squares policy iteration (LSPI), to learn near-optimal motion-planning policies which can generate kinematically feasible and collision-free trajectories between adjacent subgoals. The first advantage of the proposed method is that SSG can solve the limitations of sparse reward and local minima trap for RL agents; thus, LSPI can be used to generate paths in complex environments. The second advantage is that, when the environment changes slightly (i.e., unexpected obstacles appearing), SG–RL does not need to reconstruct subgoal graphs and replan subgoal sequences using SSGs, since LSPI can deal with uncertainties by exploiting its generalization ability to handle changes in environments. Simulation experiments in representative scenarios demonstrate that, compared with existing methods, SG–RL can work well on large-scale maps with relatively low action-switching frequencies and shorter path lengths, and SG–RL can deal with small changes in environments. We further demonstrate that the design of reward functions and the types of training environments are important factors for learning feasible policies. [ABSTRACT FROM AUTHOR]

In this paper, we present a hierarchical path planning framework called SG–RL (subgoal graphs–reinforcement learning), to plan rational paths for agents maneuvering in continuous and uncertain environments. By “rational”, we mean (1) efficient path planning to eliminate first-move lags; (2) collision-free and smooth for agents with kinematic constraints satisfied. SG–RL works in a two-level manner. At the first level, SG–RL uses a geometric path-planning method, i.e., simple subgoal graphs (SSGs), to efficiently find optimal abstract paths, also called subgoal sequences. At the second level, SG–RL uses an RL method, i.e., least-squares policy iteration (LSPI), to learn near-optimal motion-planning policies which can generate kinematically feasible and collision-free trajectories between adjacent subgoals. The first advantage of the proposed method is that SSG can solve the limitations of sparse reward and local minima trap for RL agents; thus, LSPI can be used to generate paths in complex environments. The second advantage is that, when the environment changes slightly (i.e., unexpected obstacles appearing), SG–RL does not need to reconstruct subgoal graphs and replan subgoal sequences using SSGs, since LSPI can deal with uncertainties by exploiting its generalization ability to handle changes in environments. Simulation experiments in representative scenarios demonstrate that, compared with existing methods, SG–RL can work well on large-scale maps with relatively low action-switching frequencies and shorter path lengths, and SG–RL can deal with small changes in environments. We further demonstrate that the design of reward functions and the types of training environments are important factors for learning feasible policies.

In this paper, we propose a novel hierarchical path planning approach for mobile robot navigation in complex environments. The proposed approach has a two-level structure. In the first level, the A ⁎ algorithm based on grids is used to find a geometric path quickly and several path points are selected as subgoals for the next level. In the second level, an approximate policy iteration algorithm called least-squares policy iteration (LSPI) is used to learn a near-optimal local planning policy that can generate smooth trajectories under kinematic constraints of the robot. Using this near-optimal local planning policy, the mobile robot can find an optimized path by sequentially approaching the subgoals obtained in the first level. One advantage of the proposed approach is that the kinematic characteristics of the mobile robot can be incorporated into the LSPI-based path optimization procedure. The second advantage is that the LSPI-based local path optimizer uses an approximate policy iteration algorithm which has been proven to be data-efficient and stable. The training of the local path optimizer can use sample experiences collected randomly from any reasonable sampling distribution. Furthermore, the LSPI-based local path optimizer has the ability of dealing with uncertainties in the environment. For unknown obstacles, it just needs to replan the path in the second level rather than the whole planner. Simulations for path planning in various types of environments have been carried out and the results demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]