Obstacle Rich Environment Research Articles

Generative Models for Grid-Based and Image-Based Pathfinding

Pathfinding is a challenging problem which generally asks to find a sequence of valid moves for an agent provided with a representation of the environment, i.e. a map, in which it operates. In this work, we consider pathfinding on binary grids and on image representations of the digital elevation models. In the former case, the transition costs are known, while in latter scenario, they are not. A widespread method to solve the first problem is to utilize a search algorithm that systematically explores the search space to obtain a solution. Ideally, the search should also be complemented with an informative heuristic to focus on the goal and prune the unpromising regions of the search space, thus decreasing the number of search iterations. Unfortunately, the widespread heuristic functions for grid-based pathfinding, such as Manhattan distance or Chebyshev distance, do not take the obstacles into account and in obstacle-rich environments demonstrate inefficient performance. As for pathfinding with image inputs, the heuristic search cannot be applied straightforwardly as the transition costs, i.e. the costs of moving from one pixel to the other, are not known. To tackle both challenges, we suggest utilizing modern deep neural networks to infer the instance-dependent heuristic functions at the pre-processing step and further use them for pathfinding with standard heuristic search algorithms. The principal heuristic function that we suggest learning is the path probability, which indicates how likely the grid cell (pixel) is lying on the shortest path (for binary grids with known transition costs, we also suggest another variant of the heuristic function that can speed up the search). Learning is performed in a supervised fashion (while we have also explored the possibilities of end-to-end learning that includes a planner in the learning pipeline). At the test time, path probability is used as the secondary heuristic for the Focal Search, a specific heuristic search algorithm that provides the theoretical guarantees on the cost bound of the resultant solution. Empirically, we show that the suggested approach significantly outperforms state-of-the-art competitors in a variety of different tasks (including out-of-the distribution instances).

Extremum Seeking-Based Radio Signal Strength Optimization Algorithm for Hoverable UAV Path Planning

For the safe autonomous operations of unmanned aerial vehicles (UAVs) and ground control stations (GCS), including autonomous battery replacement, wireless power transfer, and more, the precise landing of UAVs on GCS is essential. Accurate landing is only possible when the link capacity strength exceeds a certain threshold, but this is often disturbed due to complex terrain. To address this, we developed an extremum seeking (ES)-based radio signal strength optimization (RSSO) algorithm, ES-RSSO, designed to find the optimal positions of the UAV using radio communication signals. This ensures energy-efficient path planning while guaranteeing the minimum received signal strength indication (RSSI) capacity. This algorithm is particularly useful in obstacle-rich environments, where UAVs are limited in power resources. Simulation results demonstrate a 2.37% decrease in the mean, a 62.08% improvement in variance, and a 3.72% decrease in the integration strength of the link capacity when ES-RSSO is applied. These results confirm that the RADIO.rssi maintenance ability remains above a critical boundary level, supporting robust communication links and energy-efficient path planning. Throughout the study, we showed how, in many cases, simply moving the UAV a few meters can significantly improve the communication link.

Enhanced Multi-UAV Formation Control and Obstacle Avoidance Using IAAPF-SMC

In response to safety concerns pertaining to multi-UAV formation flights, a novel obstacle avoidance method based on an Improved Adaptive Artificial Potential field (IAAPF) is presented. This approach enhances UAV obstacle avoidance capabilities by utilizing segmented attraction potential fields refined with adaptive factors and augmented with virtual forces for inter-UAV collision avoidance. To further enhance the control and stability of multi-UAV formations, a Sliding Mode Control (SMC) method is integrated into the IAAPF-based obstacle avoidance framework. Renowned for its robustness and ability to handle system uncertainties and disturbances, the SMC method is combined with a feedback control system that utilizes inner and outer loops. The outer loop generates the desired path based on the leader’s state and control commands, while the inner loop tracks these trajectories and adjusts the follower UAVs’ motions. This design ensures that obstacle feedback is accounted for before the desired state information is received, enabling effective obstacle avoidance while maintaining formation integrity. Integrating leader-follower formation control techniques with SMC-based multi-UAV obstacle avoidance strategies ensures the effective convergence of the formation velocity and spacing to predetermined values, meeting the cooperative obstacle avoidance requirements of multi-UAV formations. Simulation results validate the efficacy of the proposed method in reaching otherwise unreachable destinations within obstacle-rich environments, while ensuring robust collision avoidance among UAVs.

Path planning for Multi-USV target coverage in complex environments

This research introduces a path planning methodology aimed at coordinating multiple unmanned surface vehicles (USVs) to accomplish target coverage task in obstacle-rich environments. Leveraging the locking sweeping method (LSM), our proposed method constructs task target point distance fields that integrates environmental constraints, alongside a task target point distance matrix. During the task allocation phase, we design a cost assessment function to assess the generated allocation solutions. And a greedy allocation strategy is employed to determine the optimal number of USVs required for mission completion, ensuring evenly distribution of target task points among them. Subsequently, we improve the ant colony optimization (ACO) method by redesigning the heuristic function and pheromone update rules, considering environmental constraints and task execution sequence constraints. This refinement facilitates the generation of optimized task execution sequences and safe navigation paths in obstacle environments. The effectiveness of the proposed method is validated through multiple sets of simulation experiments and compared with existing methods. The results demonstrate the practicality and efficacy of the method in addressing the challenges of USVs target coverage task in obstacle environments.

Autonomous robotic drone system for mapping forest interiors

Abstract. During the last decade, the use of drones in forest monitoring and remote sensing has become highly popular. While most of the monitoring tasks take place in high altitudes and open air, in the last few years drones have also gained interest in under-canopy data collection. However, flying under the forest canopy is a complex task since the drone can not use Global Navigation Satellite Systems (GNSS) for positioning and it has to continually avoid obstacles, such as trees, branches, and rocks, on its path. For that reason, drone-based data collection under the forest canopy is still mainly based on manual control by human pilots. Autonomous flying in GNSS-denied obstacle-rich environment has been an actively researched topic in the field of robotics during the last years and various open-sourced methods have been published in the literature. However, most of the research is done purely from the point-of-view of robotics and only a few studies have been published in the boundary of forest sciences and robotics aiming to take steps towards autonomous forest data collection. In this study, a prototype of an autonomous under-canopy drone is developed and implemented utilizing state-of-the-art open-source methods. The prototype is utilizing the EGO-Planner-v2 trajectory planner for autonomous obstacle avoidance and VINS-Fusion for Visual-inertial-odometry based GNSS-free pose estimation. The flying performance of the prototype is evaluated by performing multiple test flights with real hardware in two different boreal forest test plots with medium and difficult densities. Furthermore, the first results of the forest data collecting performance are obtained by post-processing the data collected with a low-cost stereo camera during one test flight to a 3D point cloud and by performing diameter breast at height (DBH) estimation. In the medium-density forest, all seven test flights were successful, but in the difficult test forest, one of eight test flights failed. The RMSE of the DBH estimation was 3.86 cm (12.98 %).

Path Planning for a Wheel-Foot Hybrid Parallel-Leg Walking Robot.

Mobile robots require the ability to plan collision-free paths. This paper introduces a wheel-foot hybrid parallel-leg walking robot based on the 6-Universal-Prismatic-Universal-Revolute and 3-Prismatic (6UPUR + 3P) parallel mechanism model. To enhance path planning efficiency and obstacle avoidance capabilities, an improved artificial potential field (IAPF) method is proposed. The IAPF functions are designed to address the collision problems and issues with goals being unreachable due to a nearby problem, local minima, and dynamic obstacle avoidance in path planning. Using this IAPF method, we conduct path planning and simulation analysis for the wheel-foot hybrid parallel-legged walking robot described in this paper, and compare it with the classic artificial potential field (APF) method. The results demonstrate that the IAPF method outperforms the classic APF method in handling obstacle-rich environments, effectively addresses collision problems, and the IAPF method helps to obtain goals previously unreachable due to nearby obstacles, local minima, and dynamic planning issues.

Distributed Swarm Trajectory Planning for Autonomous Surface Vehicles in Complex Sea Environments

In this paper, a swarm trajectory-planning method is proposed for multiple autonomous surface vehicles (ASVs) in an unknown and obstacle-rich environment. Specifically, based on the point cloud information of the surrounding environment obtained from local sensors, a kinodynamic path-searching method is used to generate a series of waypoints in the discretized control space at first. Next, after fitting B-spline curves to the obtained waypoints, a nonlinear optimization problem is formulated to optimize the B-spline curves based on gradient-based local planning. Finally, a numerical optimization method is used to solve the optimization problems in real time to obtain collision-free, smooth and dynamically feasible trajectories relying on a shared network. The simulation results demonstrate the effectiveness and efficiency of the proposed swarm trajectory-planning method for a network of ASVs.

Interacting with Obstacles Using a Bio-Inspired, Flexible, Underactuated Multilink Manipulator.

With the increasing demand for robotic manipulators to operate in complex environments, it is important to develop designs that work in obstacle-rich environments and can navigate around obstacles. This paper aims to demonstrate the capabilities of a bio-inspired, underactuated multilink manipulator in environments with fixed and/or movable obstacles. To simplify the system design, a single rotational actuator is used at the base of the manipulator. We present a modeling method for flexible, multilink underactuated manipulators, including their interaction with obstacles. We also demonstrate how to plan a trajectory for the manipulator in environments with fixed obstacles. The robustness of the manipulator is examined by analyzing the effects of uncertainty in its initial state and the position of obstacles. Next, we demonstrate the performance of the manipulator in environments with movable obstacles and show the advantages of controlling the obstacles' radii and positions. Lastly, we showcase the process of picking up an object in workspaces with obstacles. All the findings are supported by simulations as well as hardware experiments.

A Compact Set of Closed-form Equations for the Set of Dubins and Reeds-Shepp Paths

As the demand for robots and vehicles capable of autonomously navigating through obstacle-rich environments increases, so will the need for software capable of achieving such functionality. To find a collision-free trajectory through an obstacle-rich environment, the use of motion planning software may be necessary. When using the kinematic model of a car, the trajectories found by such software would ideally be composed of Dubins or Reeds-Shepp paths, depending on whether reversing is allowed or not. However, current methods for generating these paths tend to be rather verbose. This paper presents a compact set of closed-form equations that describe the set of Dubins and Reeds-Shepp paths. This set is compact in that the equations describe the set of Dubins and Reeds-Shepp path families as opposed to the set of Dubins and Reeds-Shepp path types, as defined in this paper, reducing the complexity of creating motion planning software when using the kinematic model of a car. These equations are derived by modelling the car as an underactuated system on the special Euclidean group in dimension 2 and then solving an associated set of inverse kinematics problems.

PRM Path Smoothening by Circular Arc Fillet Method for Mobile Robot Navigation

The problem of motion planning and navigation for mobile robots in complex environments has been a central issue in robotics. Navigating these environments requires sophisticated algorithms that handle obstacles and provide smooth, efficient paths. The Probabilistic Roadmap (PRM) method is a widespread technique in robotics for constructing paths for mobile robot navigation. In this study, we propose a novel path-smoothing method using arc fillets for path planning, building on PRM's foundation in the presence of obstacles. Our method operates in two primary stages to improve path efficiency and quality. The first stage generates the shortest path between the initial and goal states in an obstacle-rich environment using PRM, constructing a straight-line, collision-free route. The second stage smooths corners caused by nodes with arc fillets, ensuring smooth turns and minimizing abrupt changes in direction, resulting in more natural and efficient robot motion. We conducted simulations and tests using various PRM features to evaluate the proposed method. The results indicate that the built route offers a smooth turning motion and quicker, more compact movement while evading obstacles. This study contributes to mobile robot navigation by offering a practical approach to improving pathway quality in challenging environments.

TransPath: Learning Heuristics for Grid-Based Pathfinding via Transformers

Heuristic search algorithms, e.g. A*, are the commonly used tools for pathfinding on grids, i.e. graphs of regular structure that are widely employed to represent environments in robotics, video games, etc. Instance-independent heuristics for grid graphs, e.g. Manhattan distance, do not take the obstacles into account, and thus the search led by such heuristics performs poorly in obstacle-rich environments. To this end, we suggest learning the instance-dependent heuristic proxies that are supposed to notably increase the efficiency of the search. The first heuristic proxy we suggest to learn is the correction factor, i.e. the ratio between the instance-independent cost-to-go estimate and the perfect one (computed offline at the training phase). Unlike learning the absolute values of the cost-to-go heuristic function, which was known before, learning the correction factor utilizes the knowledge of the instance-independent heuristic. The second heuristic proxy is the path probability, which indicates how likely the grid cell is lying on the shortest path. This heuristic can be employed in the Focal Search framework as the secondary heuristic, allowing us to preserve the guarantees on the bounded sub-optimality of the solution. We learn both suggested heuristics in a supervised fashion with the state-of-the-art neural networks containing attention blocks (transformers). We conduct a thorough empirical evaluation on a comprehensive dataset of planning tasks, showing that the suggested techniques i) reduce the computational effort of the A* up to a factor of 4x while producing the solutions, whose costs exceed those of the optimal solutions by less than 0.3% on average; ii) outperform the competitors, which include the conventional techniques from the heuristic search, i.e. weighted A*, as well as the state-of-the-art learnable planners. The project web-page is: https://airi-institute.github.io/TransPath/.

Differentiable Learning of Scalable Multi-Agent Navigation Policies

We present an end-to-end differentiable learning algorithm for multi-agent navigation policies. Compared with prior model-free learning algorithms, our method leads to a significant speedup via the gradient information. Our key innovation lies in a novel differentiability analysis of the optimization-based crowd simulation algorithm via the implicit function theorem. Inspired by continuum multi-agent modeling techniques, we further propose a kernel-based policy parameterization, allowing our learned policy to scale up to an arbitrary number of agents without re-training. We evaluate our algorithm on two tasks in obstacle-rich environments, partially labeled navigation and evacuation, for which loss functions can be defined making the entire task learnable in an end-to-end manner. The results show that our method can achieve more than one order of magnitude speedup over model-free baselines and readily scale to unseen target configurations and agent sizes.

Hybrid Path Planning Using a Bionic-Inspired Optimization Algorithm for Autonomous Underwater Vehicles

This research presents a hybrid approach for path planning of autonomous underwater vehicles (AUVs). During path planning, static obstacles affect the desired path and path distance which result in collision penalties. In this study, the merits of grey wolf optimization (GWO) and genetic algorithm (GA) of bionic-inspired algorithms are integrated to implement a hybrid grey wolf optimization (HGWO) algorithm which allows AUVs to reach their destination safely in an obstacle rich environment. The proposed hybrid path planner is employed for path planning of a single AUV based on collision avoidance. It uses the GA as an initialization generator to overcome the random initialization problem of GWO. In this research, the total cost is considered to be a function of path distance and collision penalties. Further, the application of the proposed hybrid path planner is extended for cooperative path planning of AUVs while avoiding collision using communication consensus. Simulation results are obtained for both a single AUV and multiple AUV path planning in a 3D obstacle rich environment using a proportional-derivative controller. The Kruskal–Wallis test is employed for a non-parametric statistical analysis, where the independence of the results given by the algorithms is demonstrated.

3D self-deployment of jumping robot sensor nodes for improving network performance in obstacle dense environment

Wireless sensor node deployment in unconstructed environments affects the network’s connectivity. This paper presents the 3D self-deployment of jumping robot sensor nodes (JRSNs), which can jump over and onto obstacles to enhance network performance. The effects of JRSNs’ 3D deployment on network connectivity and energy consumption are simulated first. Then, we propose a localization method by the fusion of ultra-wideband (UWB) technology, inertial navigation system (INS), and jumping error model for JRSNs’ precise locomotion during deployment. Moreover, we propose path planning and deployment algorithms considering the JRSNs’ locomotion pattern and obstacles. Finally, we conducted experiments on the localization and deployment of JRSNs in an outdoor environment. The results verified the feasibility of the proposed method and algorithms. The JRSNs’ deployment error was smaller than 25 cm. The network connectivity was improved significantly by 57.70% with the 3D deployment. The JRSNs can deploy autonomously for various applications in obstacle-rich environments.

Joint-Space CPG for Safe Foothold Planning and Body Pose Control During Locomotion and Climbing

From insects to larger mammals, legged animals can be seen easily traversing a wide variety of challenging environments, by carefully selecting, reaching, and exploiting high-quality contacts with the terrain. In contrast, existing robotic foothold planning methods remain computationally expensive, often relying on exhaustive search and/or (often offline) optimization methods, thus limiting their adoption for real-life robotic deployments. In this work, we propose a low-cost, bio-inspired foothold planning method for legged robots, which replicates the mechanism of the central nervous system of legged mammals. We develop a low-level joint-space CPG model along with a high-level vision-based controller that can inexpensively predict future foothold locations and locally optimize them based on a potential field based approach. Specifically, by reasoning about the quality of ground contacts and the robot's stability through the high-level vision-based controller, our CPG model smoothly and iteratively updates relevant locomotion parameters to both optimize foothold locations and body pose, directly in the joint space of the robot for easier implementation. We experimentally validate our control model on a modular hexapod on various locomotion tasks in obstacle-rich environments as well as on stair climbing. Our results show that our method enables stabler and steadier locomotion than an open-loop CPG controller, yielding higher-quality feedback from onboard sensors by minimizing the effect of slippage and unexpected impacts.

Learning Selective Communication for Multi-Agent Path Finding

Learning communication via deep reinforcement learning (RL) or imitation learning (IL) has recently been shown to be an effective way to solve Multi-Agent Path Finding (MAPF). However, existing communication based MAPF solvers focus on broadcast communication, where an agent broadcasts its message to all other or predefined agents. It is not only impractical but also leads to redundant information that could even impair the multi-agent cooperation. A succinct communication scheme should learn <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">which</i> information is relevant and influential to each agent’s decision making process. To address this problem, we consider a request-reply scenario and propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Decision Causal Communication</i> (DCC), a simple yet efficient model to enable agents to select neighbors to conduct communication during both training and execution. Specifically, a neighbor is determined as relevant and influential only when the presence of this neighbor causes the decision adjustment on the central agent. This judgment is learned only based on agent’s local observation and thus suitable for decentralized execution to handle large scale problems. Empirical evaluation in obstacle-rich environment indicates the high success rate with low communication overhead of our method.

A Robust, Multiple Control Barrier Function Framework for Input Constrained Systems

We propose a novel (Type-II) zeroing control barrier function (ZCBF) for safety-critical control, which generalizes the original ZCBF approach. Our method allows for applications to a larger class of systems (e.g. passivity-based) while still ensuring robustness, for which the construction of conventional ZCBFs is difficult. We also propose a locally Lipschitz continuous control law that handles multiple ZCBFs, while respecting input constraints, which is not currently possible with existing ZCBF methods. We apply the proposed concept for unicycle navigation in an obstacle-rich environment.

Secure Visible Light Communication System via Cooperative Attack Detecting Techniques

With the recent development of fourth industrial technology, the need for a broadband short-range wireless communication system to realize an ultraconnected, ultra-low latency, and ultra-realistic intelligent information society has emerged. Among the next-generation communication network technologies that can fulfill the technical demands, visible light communication (VLC) is a promising technology that can use illuminated light as a communication light source, which is convenient and environmentally friendly and has high energy and frequency efficiency. However, although VLC has a high level of security owing to the straightness and transparency of visible light, if some of the VLC nodes in a dense mesh network environment are hacked by external attacks, there can be critical performance degradation by jamming attacks. Although several studies have suggested the possibility of VLC jamming attacks, only a few have studied how to effectively detect and respond to these attacks. This study proposes a method to collaboratively detect and respond to jamming attacks in smart LED-based VLC systems. According to the experimental results of this study, the proposed cooperative method showed 91% attack detection accuracy and 1.82 times better than the k-random method. The proposed method showed a minimum detection rate of 84% even in an obstacle-rich environment, proving outstanding attack detection performance 1.68 times better than the k-random method.

CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees

This letter addresses the 3D coverage path planning (CPP) problem for terrain reconstruction of unknown obstacle rich environments. Due to sensing limitations, the proposed method, called CT-CPP, performs layered scanning of the 3D region to collect terrain data, where the traveling sequence is optimized using the concept of a coverage tree (CT) with a TSP-inspired tree traversal strategy. The CT-CPP method is validated on a high-fidelity underwater simulator and the results are compared to an existing terrain following CPP method. The results show that CT-CPP yields significant reduction in trajectory length, energy consumption, and reconstruction error.

Receding-horizon RRT-Infotaxis for autonomous source search in urban environments

In emergency situations such as hazardous gas leak, search and estimation for identifying source information, known as source term estimation (STE), in a timely and accurate manner is of significant importance. In real world situations, obstacles such as buildings or barriers not only block the path for search but also interfere the flow of the gas source. For autonomous source search and estimation using a mobile sensor in such obstacle-rich environments, this paper proposes an information-theoretic STE approach by combining a widely-used Infotaxis with the rapidly-exploring random trees (RRT). In particular, the proposed strategy utilizes the receding-horizon RRT concept with a newly designed utility function for determining the next maneuver of a mobile agent to get the best information of the source while avoiding obstacles in urban environments. Numerical simulations in various environments show the superior performance of the proposed approach compared with the original Infotaxis method.