Symbiotic Navigation in Multi-Robot Systems with Remote Obstacle Knowledge Sharing

Large scale operational areas often require multiple service robots for coverage and task parallelism. In such scenarios, each robot keeps its individual map of the environment and serves specific areas of the map at different times. We propose a knowledge sharing mechanism for multiple robots in which one robot can inform other robots about the changes in map, like path blockage, or new static obstacles, encountered at specific areas of the map. This symbiotic information sharing allows the robots to update remote areas of the map without having to explicitly navigate those areas, and plan efficient paths. A node representation of paths is presented for seamless sharing of blocked path information. The transience of obstacles is modeled to track obstacles which might have been removed. A lazy information update scheme is presented in which only relevant information affecting the current task is updated for efficiency. The advantages of the proposed method for path planning are discussed against traditional method with experimental results in both simulation and real environments

Multi-Modal Sensor Fusion-Based Semantic Segmentation for Snow Driving Scenarios

In recent years, autonomous vehicle driving technology and advanced driver assistance systems have played a key role in improving road safety. However, weather conditions such as snow pose severe challenges for autonomous driving and are an active research area. Thanks to their superior reliability, the resilience of detection, and improved accuracy, advances in computation and sensor technology have paved the way for deep learning and neural network-based techniques that can replace the classical approaches. In this research, we investigate the semantic segmentation of roads in snowy environments. We propose a multi-modal fused RGB-T semantic segmentation utilizing a color (RGB) image and thermal map (T) as inputs for the network. This paper introduces a novel fusion module that combines the feature map from both inputs. We evaluate the proposed model on a new snow dataset that we collected and on other publicly available datasets. The segmentation results show that the proposed fused RGB-T input can segregate human subjects in snowy environments better than an RGB-only input. The fusion module plays a vital role in improving the efficiency of multiple input neural networks for person detection. Our results show that the proposed network can generate a higher success rate than other state-of-the-art networks. The combination of our fused module and pyramid supervision path generated the best results in both mean accuracy and mean intersection over union in every dataset

Avoiding blind leading the blind: Uncertainty integration in virtual pheromone deposition by robots

Virtual pheromone trailing has successfully been demonstrated for navigation of multiple robots to achieve a collective goal. Many previous works use a pheromone deposition scheme that assumes perfect localization of the robot, in which, robots precisely know their location in the map. Therefore, pheromones are always assumed to be deposited at the desired place. However, it is difficult to achieve perfect localization of the robot due to errors in encoders and sensors attached to the robot and the dynamics of the environment in which the robot operates. In real-world scenarios, there is always some uncertainty associated in estimating the pose (i.e. position and orientation) of the mobile service robot. Failing to model this uncertainty would result in service robots depositing pheromones at wrong places. A leading robot in the multi-robot system might completely fail to localize itself in the environment and be lost. Other robots trailing its pheromones will end up being in entirely wrong areas of the map. This results in a "blind leading the blind'' scenario that reduces the efficiency of the multi-robot system. We propose a pheromone deposition algorithm, which models the uncertainty of the robot's pose. We demonstrate, through experiments in both simulated and real environments, that modeling the uncertainty in pheromone deposition is crucial, and that the proposed algorithm can model the uncertainty well

Hitchhiking Robots: A Collaborative Approach for Efficient Multi-Robot Navigation in Indoor Environments

Hitchhiking is a means of transportation gained by asking other people for a (free) ride. We developed a multi-robot system which is the first of its kind to incorporate hitchhiking in robotics, and discuss its advantages. Our method allows the hitchhiker robot to skip redundant computations in navigation like path planning, localization, obstacle avoidance, and map update by completely relying on the driver robot. This allows the hitchhiker robot, which performs only visual servoing, to save computation while navigating on the common path with the driver robot. The driver robot, in the proposed system performs all the heavy computations in navigation and updates the hitchhiker about the current localized positions and new obstacle positions in the map. The proposed system is robust to recover from 'driver-lost' scenario which occurs due to visual servoing failure. We demonstrate robot hitchhiking in real environments considering factors like service-time and task priority with different start and goal configurations of the driver and hitchhiker robots. We also discuss the admissible characteristics of the hitchhiker, when hitchhiking should be allowed and when not, through experimental results

SHP: Smooth Hypocycloidal Paths with Collision-Free and Decoupled Multi-Robot Path Planning

Generating smooth and continuous paths for robots with collision avoidance, which avoid sharp turns, is an important problem in the context of autonomous robot navigation. This paper presents novel smooth hypocycloidal paths (SHP) for robot motion. It is integrated with collision-free and decoupled multi-robot path planning. An SHP diffuses (i.e., moves points along segments) the points of sharp turns in the global path of the map into nodes, which are used to generate smooth hypocycloidal curves that maintain a safe clearance in relation to the obstacles. These nodes are also used as safe points of retreat to avoid collision with other robots. The novel contributions of this work are as follows: (1) The proposed work is the first use of hypocycloid geometry to produce smooth and continuous paths for robot motion. A mathematical analysis of SHP generation in various scenarios is discussed. (2) The proposed work is also the first to consider the case of smooth and collision-free path generation for a load carrying robot. (3) Traditionally, path smoothing and collision avoidance have been addressed as separate problems. This work proposes integrated and decoupled collision-free multi-robot path planning. ‵Node caching‵ is proposed to improve efficiency. A decoupled approach with local communication enables the paths of robots to be dynamically changed. (4) A novel ‵multi-robot map update‵ in case of dynamic obstacles in the map is proposed, such that robots update other robots about the positions of dynamic obstacles in the map. A timestamp feature ensures that all the robots have the most updated map. Comparison between SHP and other path smoothing techniques and experimental results in real environments confirm that SHP can generate smooth paths for robots and avoid collision with other robots through local communication

On a bio-inspired hybrid pheromone signalling for efficient map exploration of multiple mobile service robots

This paper presents a novel bio-inspired hybrid communication framework that incorporates the repelling behaviour of anti-aphrodisiac pheromones and attractive behaviour of pheromones for efficient map exploration of multiple mobile service robots. The proposed communication framework presents a scheme for robots to efficiently serve large areas of map, while cooperating with each other through proper pheromone deposition. This eliminates the need of explicitly programming each service robot to serve particular areas of the map. The paths taken by robots are represented as nodes across which pheromones are deposited. This reduces the search space for tracking pheromones and reduces data size to be communicated between robots. A novel pheromone deposition model is presented which takes into account the uncertainty in the robot's position. This eliminates robots to deposit pheromones at wrong places when localization fails. The framework also integrates the pheromone signalling mechanism in landmark-based Extended Kalman Filter (EKF) localization and allows the robots to capture areas or sub-areas of the map, to improve the localization. A scheme to resolve conflicts through local communication is presented. We discuss, through experimental and simulation results, two cases of floor cleaning task, and surveillance task, performed by multiple robots. Results show that the proposed scheme enables multiple service robots to perform cooperative tasks intelligently without any explicit programming

Bio-Inspired Structure and Behavior of Self-Recovery Quadruped Robot with a Limited Number of Functional Legs

In this study, the authors focus on the structural design of and recovery methods for a damaged quadruped robot with a limited number of functional legs. Because the pre-designed controller cannot be executed when the robot is damaged, a control strategy to avoid task failures in such a scenario should be developed. Not only the control method but also the shape and structure of the robot itself are significant for the robot to be able to move again after damage. We present a caterpillar-inspired quadruped robot (CIQR) and a self-learning mudskipper inspired crawling (SLMIC) algorithm in this research. The CIQR is realized by imitating the prolegs of caterpillars and by using a numerical optimization technique. A reinforcement learning method called Q-learning is employed to improve the adaptability of locomotion based on the crawling behavior of mudskipper. The results show that the proposed robotic platform and recovery method can improve the moving ability of the damaged quadruped robot with a few active legs in both simulations and experiments. Moreover, we obtained satisfactory results showing that a damaged multi-legged robot with at least one leg could travel properly along the required direction. Furthermore, the presented algorithm can successfully be employed in a damaged quadruped robot with fewer than four legs

On a Hopping-Points SVD and Hough Transform-Based Line Detection Algorithm for Robot Localization and Mapping

Line detection is an important problem in computer vision, graphics and autonomous robot navigation. Lines detected using a laser range sensor (LRS) mounted on a robot can be used as features to build a map of the environment, and later to localize the robot in the map, in a process known as Simultaneous Localization and Mapping (SLAM). We propose an efficient algorithm for line detection from LRS data using a novel hopping-points Singular Value Decomposition (SVD) and Hough transform-based algorithm, in which SVD is applied to intermittent LRS points to accelerate the algorithm. A reverse-hop mechanism ensures that the end points of the line segments are accurately extracted. Line segments extracted from the proposed algorithm are used to form a map and, subsequently, LRS data points are matched with the line segments to localize the robot. The proposed algorithm eliminates the drawbacks of point-based matching algorithms like the Iterative Closest Points (ICP) algorithm, the performance of which degrades with an increasing number of points. We tested the proposed algorithm for mapping and localization in both simulated and real environments, and found it to detect lines accurately and build maps with good self-localization