What visual information is used for navigation around obstacles in a cluttered environment?

Chapter 32 - Gaze fixation patterns during goal-directed locomotion while navigating around obstacles and a new route-selection model

Maintenance of equilibrium was tested in conditions when humans assume different leaning postures during upright standing. Subjects ( n=11) stood in 13 different body postures specified by visual center of pressure (COP) targets within their base of support (BOS). Different types of visual information were tested: continuous presentation of visual target, no vision after target presentation, and with simultaneous visual feedback of the COP. The following variables were used to describe the equilibrium maintenance: the mean of the COP position, the area of the ellipse covering the COP sway, and the resultant median frequency of the power spectral density of the COP displacement. The variability of the COP displacement, quantified by the COP area variable, increased when subjects occupied leaning postures, irrespective of the kind of visual information provided. This variability also increased when vision was removed in relation to when vision was present. Without vision, drifts in the COP data were observed which were larger for COP targets farther away from the neutral position. When COP feedback was given in addition to the visual target, the postural control system did not control stance better than in the condition with only visual information. These results indicate that the visual information is used by the postural control system at both short and long time scales.

A number of lines of evidence suggest that, in the macaque monkey, inferior parietal and inferotemporal cortices process different types of visual information. It has been suggested that visual information reaching these two subdivisions follows separate pathways from the striate cortex through the prestriate cortex. We examined directly this possibility by placing injections of the retrograde fluorescent tracers, fast blue and diamidino yellow, in inferior parietal and inferotemporal cortex and examining the spatial pattern of cortical areas containing labeled cells in two-dimensional reconstructions of the cortex. The results of injections in inferotemporal cortex show that TEO receives afferents from areas V2, ventral V3, V3A, central V4, V4t, and DPL in prestriate cortex and from areas IPa, PGa, and FST in the superior temporal sulcus (STS). Area TEp receives afferents only from V4 in prestriate cortex and from IPa, PGa, and FST in the anterior STS. Area TEa receives no prestriate input and is innervated by IPa, PGa, FST, and TPO in the anterior STS. The results of injections in inferior parietal cortex demonstrate that POa receives afferents from dorsal V3, V3A, peripheral V4, DPL, and PO in prestriate cortex, from MST and *VIP and from IPa, PGa, TPO, and FST in anterior STS. Area PGc (corresponding to 7a) is innervated by PO, MST, and by TPO in the anterior STS. Examination of the two-dimensional reconstructions of the pattern of labeling after combined injections of fast blue and diamidino yellow in areas POa and TEO revealed that these areas are principally innervated by different prestriate areas. Only a small region, centered on area V3A and extending into V4 and DPL, contained cells labeled by either injection as well as a small number of double-labeled cells. In contrast, areas POa and TEO receive afferents from extensive common regions in the anterior STS corresponding to areas IPa, PGa, and FST. These results directly demonstrate that visual information from the striate cortex reaches inferior parietal and inferotemporal cortices through largely separate prestriate cortical pathways. On the other hand, both parietal and inferotemporal cortices receive common inputs from extensive regions in the anterior STS which map play a role in linking the processing occurring in these two cortical subdivisions of the visual system.

OBJECTIVESTo investigate the roles of visual and tactile information in a dyspraxic patient with corticobasal degeneration (CBD) who showed dramatic facilitation in miming the use of a tool or object...

Experiments on rhesus macaques were used to study the relationship between the characteristics of delayed visual differentiation and stimulus properties in conditions of pharmacological treatment with the m-cholinoreceptor blocker amizil, with the aim of identifying how modification of cholinergic structures affects different types of information. Disturbances to short-term memory for all stimuli consisted of reductions in the duration of retention and increases in motor reaction times, but occurred at different doses of the blocker: amizil at a dose of 0.3 mg/kg significantly decreased the retention duration for information relating to spatial relationships. Delayed discrimination of shape, contrast, and size worsened after treatment with amizil at a dose of 0.45-0.50 mg/kg, while decreases in the duration of short-term storage of information relating to color started after amizil doses of 0.6-0.8 mg/kg. It is suggested that the short-term memory system includes a set of neurophysiological mechanisms in which the cholinergic structures are organized differently and whose specific properties result in differences in the characteristics of short-term storage of different types of visual information.

Accurate path planning was the prerequisite for autonomous navigation of agricultural vehicles. An Unmanned Aerial Vehicle (UAV)-based coverage path planning was developed in this research for automating guidance of agricultural vehicles and reducing the operator maneuver in the creation of navigation maps. High-resolution orthophoto maps of the field were constructed by using low-altitude UAV photogrammetry to obtain spatial information. Travel paths and working paths were automatically generated from anchor points selected by the operator under the image coordinate domain. The navigation path for unmanned agricultural vehicles was generated by Mercator projection-based conversion for the anchor pixel coordinates into latitude and longitude geographic coordinates. A Graphical User Interface (GUI) was developed for path generation, visualization, and performance evaluation, through which the proposed path planning method was implemented for autonomous agricultural vehicle navigation. Calculation accuracy tests demonstrated the mean planar coordinate error was 2.23 cm and the maximum error was 3.37 cm for path planning. Field tests showed that lateral navigation errors remained within ±5.5 cm for the unmanned high-clearance sprayer, which indicated that the developed UAV-based coverage path planning method was feasible and featured high accuracy. It provided an effective solution for achieving fully autonomous agricultural vehicle operations.

Grasses give beauty to the environment especially when properly managed. There are challenges of and not limited to time consuming, labour intensiveness in carrying out these maintenance thus, the need for the system which can autonomously perform such task. Many researches on autonomous grass cutting robot have been limited to obstacle avoidance and path planning but lawn availability is still a major area ofconcern. This study is aimed at developing an autonomous solar grass cutting robot embedded with obstacle avoidance and path planning mechanisms. The autonomous robot utilizes energy from the sun via photovoltaic panel to charge the battery for continuous performance of the robot. The obstacle avoidance and path planning for area covering of the robot were achieved using infrared sensor and 2D laser scanner respectively. The efficiency evaluation of the machine was based on the primary set objectives of obstacle avoidance and path planning in terms of area coverage, machine lawn availability, energy consumption and machine intervention. The results show that the machine operates at an efficiency of almost 100 % in in term of area coverage. Percentage machine intervention and lawn availability are 5 and 95 %, respectively. Also, 6000 Wh average energy consumption was achieved. The development is very suitable for a semi-structured environment.

Aiming at the problem of multi-objective path planning (MOPP) and dynamic obstacle avoidance of manipulator in dynamic environment, this paper proposes a solution based on hierarchical reinforcement learning (HRL) framework. Traditional path planning methods have some problems in dynamic scenes, such as poor real-time performance, difficult to balance multi-objective conflicts and insufficient adaptability to environmental changes. Therefore, this paper designs a two-tier architecture including global path planning layer and local obstacle avoidance layer, which is trained by Proximal policy optimization (PPO) and Soft Actor-Critic (SAC) algorithms respectively, and achieves collaborative optimization through an efficient information exchange mechanism between layers. At the same time, a multi-objective reward function based on dynamic weight adjustment strategy is introduced. Fuzzy logic is used to adaptively balance the relationship among path length, obstacle avoidance safety and energy consumption according to environmental complexity. Combined with Long Short-Term Memory (LSTM), the trajectory of obstacles is predicted, and the potential field method is further introduced to modify the obstacle avoidance reward, which improves the real-time response ability and robustness of the algorithm in dynamic environment. The experimental results show that the HRL-SAC-PPO method proposed in this paper shows superior performance in both static and dynamic scenarios. In the static scene, the success rate of this method reaches 100%, the average path length is shortened to 2.13m, no collision occurs, and the energy consumption is reduced to 1.12 kJ, which shows a good multi-objective optimization effect. In the dynamic scene, the trajectory error of obstacles predicted by LSTM is only 4.2%, and the safe distance between the robot arm and obstacles is improved by 35%, which significantly enhances the reliability of obstacle avoidance. In addition, the average decision delay of this method is only 11.3ms, and the peak delay is 23ms, which is much lower than that of the contrast algorithm, showing stronger real-time response ability. The ablation experiment further verified the key role of LSTM trajectory prediction, dynamic weight adjustment and layered structure on the overall performance. In the welding task verification of the real UR5 manipulator, the success rate of the system in dynamic environment is 95.3%, the average path length is 3.41m, and the maximum joint acceleration is only 0.87 radian/s², which is far below the safety threshold, indicating that the algorithm has good stability and obstacle avoidance ability in practical application. The comprehensive performance comparison shows that this method performs well in different industrial scenarios, and has stronger environmental adaptability and comprehensive path planning ability.

To address issues such as insufficient real-time performance, poor obstacle avoidance flexibility, and slow emergency stop response in path planning for transport robots operating within complex, dynamic hospital environments, this paper proposes a hybrid path planning strategy that integrates an improved A* algorithm with the Dynamic Window Algorithm (DWA). This strategy is implemented within the ROS2 framework. First, the traditional $\mathbf{A}^{*}$ algorithm is optimized by introducing a dynamic weighted Manhattan distance <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> as the heuristic function. This incorporates path smoothness and safety distance penalty terms, enhancing the rationality and efficiency of the global path. Second, at the local planning layer, a multi-modal perception fusion dynamic window algorithm is designed. This method dynamically embeds real-time evaluations of LiDAR, visual semantic information, and UWB positioning data into the evaluation function, enabling adaptive obstacle avoidance for diverse obstacles such as pedestrian flows, IV stands, and mobile hospital beds. Experiments demonstrate that the improved algorithm reduces planning time by approximately 35% while maintaining near-optimal path length. Emergency stop response time is $\boldsymbol{\leqslant} 180 \mathrm{~ms}$, and dynamic obstacle avoidance success rate increases to 98.7%, effectively supporting high-frequency, high-priority medication transport tasks in medical settings.

The downhole method is one of the most widely used in situ seismic tests to determine shear wave velocity (VS) profiles for site characterization and site response analysis. Although easy to perform in the field, it has always been a tedious task to accurately interpret the downhole data based on the arrival times of shear waves at the borehole receiver. Commonly used methods are the direct, interval, and Snell’s Raypath methods. Snell’s Raypath method has been known to provide the best results for downhole processing at multilayered subsurface sites as it considers the actual travel path of waves considering the refraction along the wave travel path. The direct method is considered better when the soil layers are uniform with reduced chances of refraction. In this study, VS profiles are estimated for two boreholes 100 m deep at a deep silty deposit underlain by sandstone rocks. The Snell’s Raypath method was observed to be more suitable for shallow subsurface to capture a greater variation in properties and need for a higher resolution in shallow subsurface. The direct method was found suitable for deeper layers where dense soil and rock layers are located, which show more uniform layering with depth.

From an ecological perspective, a basic form of navigating is wayfinding which involves the control of travel through perceiving temporally-structured visual information. This information consists of an optical flow of perspective structure generated by a perceiver moving along a path of travel. The generation of visual information through action, which in turn is controlled by that information, is indicative of the on-going, reciprocal interaction between the perceiver and the environment. It is suggested that the perspective structure consists of a sequence of transitions between successive vistas which uniquely specifies a route to a destination. Also, like the information specifying other types of events, this information can be described as a nested hierarchical structure that unfolds over time. A series of experiments are reviewed that employ dynamic displays of paths in order to examine this approach to way- finding. In addition, it is proposed that in the process of traveling paths through the environment, invariant information specifying the overall layout of the environment is revealed to a perceiver. In this way, the panorama of the environment is apprehended. The role of the affordances of places in navigational processes is also briefly considered. Overall, this ecological analysis suggests a need to reexamine our standard assumptions about the nature of perceiving and its role in navigation.

Abstract. Urban traffic congestion, obstacle avoidance, and driving efficiency are the challenges faced by autonomous-vehicle path-planning technology in cities. The traditional artificial potential field (APF) algorithm is insufficient to meet the requirements of efficiency and safety in path planning, as it easily gets trapped in local optima when dealing with complex environments. Therefore, this paper proposes a novel AS-IAPF path-planning algorithm to more efficiently enhance the target reachability of autonomous vehicles in complex traffic environments. Firstly, this paper analyzes and elucidates the macroscopic traffic model, achieving effective modeling of dynamic traffic flow stability based on Lyapunov stability theorem and a classical 1D flow model. Thus, the threshold discriminant formula for traffic element stability is obtained. Secondly, based on the aforementioned threshold discriminant formula, a new AS-IAPF algorithm is proposed. The algorithm mainly includes two aspects: firstly, by pre-generating initial paths and introducing a Gaussian oscillation coefficient of force fields, it avoids the algorithm falling into local optima; secondly, by using the aforementioned driving stability threshold discriminant formula as a dimensional adjustment for adaptively improving and adjusting the strength coefficient of the AS-APF repulsive field, the algorithm further improves the efficiency of path planning. Finally, the algorithm is subjected to joint simulations of 2D and 3D scenarios of different types. The research results show that the AS-IAPF algorithm outperforms other algorithms of the same type with respect to comprehensive performance based on multiple 2D scenario simulation experiments. In the 3D simulation experiments of three different typical traffic scenarios, the proposed algorithm can drive autonomous vehicles to effectively perform corresponding obstacle avoidance actions based on the actual traffic scenarios ahead, ultimately achieving safe obstacle avoidance. The path-planning method proposed in this paper can enhance driving efficiency while considering the safety and stability of vehicles, providing a promising approach and reference for the path planning of autonomous vehicles.

Visual working memory (VWM) is used to maintain visual information available for subsequent goal-directed behavior. The content of VWM has been shown to affect the behavioral response to concurrent visual input, suggesting that visual representations originating from VWM and from sensory input draw upon a shared neural substrate (i.e., a sensory recruitment stance on VWM storage). Here, we hypothesized that visual information maintained in VWM would enhance the neural response to concurrent visual input that matches the content of VWM. To test this hypothesis, we measured fMRI BOLD responses to task-irrelevant stimuli acquired from 15 human participants (three males) performing a concurrent delayed match-to-sample task. In this task, observers were sequentially presented with two shape stimuli and a retro-cue indicating which of the two shapes should be memorized for subsequent recognition. During the retention interval, a task-irrelevant shape (the probe) was briefly presented in the peripheral visual field, which could either match or mismatch the shape category of the memorized stimulus. We show that this probe stimulus elicited a stronger BOLD response, and allowed for increased shape-classification performance, when it matched rather than mismatched the concurrently memorized content, despite identical visual stimulation. Our results demonstrate that VWM enhances the neural response to concurrent visual input in a content-specific way. This finding is consistent with the view that neural populations involved in sensory processing are recruited for VWM storage, and it provides a common explanation for a plethora of behavioral studies in which VWM-matching visual input elicits a stronger behavioral and perceptual response.SIGNIFICANCE STATEMENT Humans heavily rely on visual information to interact with their environment and frequently must memorize such information for later use. Visual working memory allows for maintaining such visual information in the mind's eye after termination of its retinal input. It is hypothesized that information maintained in visual working memory relies on the same neural populations that process visual input. Accordingly, the content of visual working memory is known to affect our conscious perception of concurrent visual input. Here, we demonstrate for the first time that visual input elicits an enhanced neural response when it matches the content of visual working memory, both in terms of signal strength and information content.

With the development and widely use of unmanned aerial vehicles (UAVs) in recent years, the development of efficient path planning methods for automation has become crucial. Obstacle avoidance and path planning are the key components of UAV path planning. This article provides an overview of obstacle avoidance and path planning techniques for UAVs based on the artificial potential field method (APF method). This article begins with the explaining the principles of artificial potential field on this basis discusses its advantages and limitations. The article then summarizes the improvement strategies proposed by previous researchers to address issues like local minimum values and unreachable targets, such as introducing a new repulsive potential energy function, combining APF with other planning methods, and utilizing flow functions. Furthermore, it presents examples of the application and the performance of usage of these techniques in both static and dynamic environments. Based on this, the prospects and developing trend of UAV obstacle avoidance methods based on artificial potential field are foresee, such as combined with DRL and deep learning.

Most coastal trash comes from land. To prevent and control ocean pollution, it should be handled using sources that can maintain a clean ocean and improve the marine ecological environment. The proposed system can be used to inspect riverbanks and identify garbage on riverbanks. This waste can then be cleaned up before flowing into the sea. In this study, we utilized an unmanned aerial vehicle (UAV) to inspect riverbanks and applied path planning and obstacle avoidance to enhance the efficiency of UAV performance and ensure good adaptability in a complicated environment. Since most rivers in the middle and upper sections of the study area are rough and meandering, path planning was first addressed so that the drone could use the shortest path and less energy to perform the inspection task. Branches frequently protrude from the riverbank on both sides. Therefore, an instant obstacle avoidance algorithm was added to avoid various obstacles. Path planning was based on an Improved Particle Swarm Optimization (IPSO). A fuzzy system was added to the IPSO to adjust the parameters that could shorten the planned path. The Artificial Potential Field (APF) was applied for real-time dynamic obstacle avoidance. The proposed UAV system could be used to perform riverbank inspection successfully.