Finding high-quality solutions quickly is an important objective in motion planning. This is especially true for high-degree-of-freedom robots. Satisficing planners have traditionally found feasible solutions quickly but provide no guarantees on their optimality, while almost-surely asymptotically optimal (a.s.a.o.) planners have probabilistic guarantees on their convergence towards an optimal solution but are more computationally expensive. This paper uses the AO-x meta-algorithm to extend the satisficing RRT-Connect planner to optimal planning. The resulting Asymptotically Optimal RRT-Connect (AORRTC) finds initial solutions in similar times as RRT-Connect and uses any additional planning time to converge towards the optimal solution in an anytime manner. It is proven to be probabilistically complete and a.s.a.o. AORRTC was tested with the Panda (7 DoF) and Fetch (8 DoF) robotic arms on the MotionBenchMaker dataset. These experiments show that AORRTC finds initial solutions as fast as RRT-Connect and faster than the tested state-of-the-art a.s.a.o. algorithms while converging to better solutions faster. AORRTC finds solutions to difficult high-DoF planning problems in milliseconds where the other a.s.a.o. planners could not consistently find solutions in seconds. This performance was demonstrated both with and without single instruction/multiple data (SIMD) acceleration.

Improving the performance of motion planning algorithms for high-degree-of-freedom robots usually requires reducing the cost or frequency of computationally expensive operations. Traditionally, and especially for asymptotically optimal sampling-based motion planners, the most expensive operations are local motion validation and querying the nearest neighbours of a configuration. Recent advances have significantly reduced the cost of motion validation by using single instruction/multiple data (SIMD) parallelism to improve solution times for satisficing motion planning problems. These advances have not yet been applied to asymptotically optimal motion planning. This paper presents Fully Connected Informed Trees (FCIT*), the first fully connected, informed, anytime almost-surely asymptotically optimal (ASAO) algorithm. FCIT* exploits the radically reduced cost of edge evaluation via SIMD parallelism to build and search fully connected graphs. This removes the need for nearest-neighbours structures, which are a dominant cost for many sampling-based motion planners, and allows it to find initial solutions faster than state-of-the-art ASAO (VAMP, OMPL) and satisficing (OMPL) algorithms on the MotionBenchMaker dataset while converging towards optimal plans in an anytime manner.

Visual motion estimation is a well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation in highly dynamic environments. These environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion. Estimating third-party motions simultaneously with the sensor egomotion is difficult because an object’s observed motion consists of both its true motion and the sensor motion. Most previous works in multimotion estimation simplify this problem by relying on appearance-based object detection or application-specific motion constraints. These approaches are effective in specific applications and environments but do not generalize well to the full multimotion estimation problem (MEP). This paper presents Multimotion Visual Odometry (MVO), a multimotion estimation pipeline that estimates the full SE (3) trajectory of every motion in the scene, including the sensor egomotion, without relying on appearance-based information. MVO extends the traditional visual odometry (VO) pipeline with multimotion segmentation and tracking techniques. It uses physically founded motion priors to extrapolate motions through temporary occlusions and identify the reappearance of motions through motion closure. Evaluations on real-world data from the Oxford Multimotion Dataset (OMD) and the KITTI Vision Benchmark Suite demonstrate that MVO achieves good estimation accuracy compared to similar approaches and is applicable to a variety of multimotion estimation challenges.

Event-based cameras asynchronously capture individual visual changes in a scene. This makes them more robust than traditional frame-based cameras to highly dynamic motions and poor illumination. It also means that every measurement in a scene can occur at a unique time. Handling these different measurement times is a major challenge of using event-based cameras. It is often addressed in visual odometry (VO) pipelines by approximating temporally close measurements as occurring at one common time. This grouping simplifies the estimation problem but, absent additional sensors, sacrifices the inherent temporal resolution of event-based cameras. This paper instead presents a complete stereo VO pipeline that estimates directly with individual event-measurement times without requiring any grouping or approximation in the estimation state. It uses continuous-time trajectory estimation to maintain the temporal fidelity and asynchronous nature of event-based cameras through Gaussian process regression with a physically motivated prior. Its performance is evaluated on the MVSEC dataset, where it achieves 7.9e-3 and 5.9e-3 RMS relative error on two independent sequences, outperforming the existing publicly available event-based stereo VO pipeline by two and four times, respectively.

High-quality observations of the real world are crucial for a variety of applications, including producing 3D printed replicas of small-scale scenes and conducting inspections of large-scale infrastructure. These 3D observations are commonly obtained by combining multiple sensor measurements from different views. Guiding the selection of suitable views is known as the Next Best View (NBV) planning problem. Most NBV approaches reason about measurements using rigid data structures (e.g., surface meshes or voxel grids). This simplifies next best view selection but can be computationally expensive, reduces real-world fidelity and couples the selection of a next best view with the final data processing. This paper presents the Surface Edge Explorer (SEE), a NBV approach that selects new observations directly from previous sensor measurements without requiring rigid data structures. SEE uses measurement density to propose next best views that increase coverage of insufficiently observed surfaces while avoiding potential occlusions. Statistical results from simulated experiments show that SEE can attain similar or better surface coverage with less observation time and travel distance than evaluated volumetric approaches on both small- and large-scale scenes. Real-world experiments demonstrate SEE autonomously observing a deer statue using a 3D sensor affixed to a robotic arm.

Aerial mapping systems are important for many surveying applications (e.g., industrial inspection or agricultural monitoring). Aerial platforms that can fly GPS-guided preplanned missions semi-autonomously are already widely available but fully autonomous systems can significantly improve efficiency. Autonomously mapping complex 3D structures requires a system that performs online mapping and mission planning. This paper presents Osprey, an autonomous aerial mapping system with state-of-the-art multi-session LiDAR-based mapping capabilities. It enables a non-expert operator to specify a bounded target area that the aerial platform can then map autonomously over multiple flights. Field experiments with Osprey demonstrate that this system can achieve greater map coverage of large industrial sites than manual surveys with a pilot-flown aerial platform or a terrestrial laser scanner (TLS). Three sites, with a total ground coverage of 2528 m^2 and a maximum height of 27 m, were mapped in separate missions using 112 minutes of autonomous flight time. True colour maps were created from images captured by Osprey using pointcloud and NeRF reconstruction methods. These maps provide useful data for structural inspection tasks.

Multiquery planning algorithms find paths between various different starts and goals in a single search space. They are designed to do so efficiently by reusing information across planning queries. This information may be computed before or during the search and often includes knowledge of valid paths. Using known valid paths to solve an individual planning query takes less computational effort than finding a completely new solution. This allows multiquery algorithms, such as PRM*, to outperform single-query algorithms, such as RRT*, on many problems but their relative performance depends on how much information is reused. Despite this, few multiquery planners explicitly seek to maximize path reuse and, as a result, many do not consistently outperform single-query alternatives. This paper presents Effort Informed Roadmaps (EIRM*), an almost-surely asymptotically optimal multiquery planning algorithm that explicitly prioritizes reusing computational effort. EIRM* uses an asymmetric bidirectional search to identify existing paths that may help solve an individual planning query and then uses this information to order its search and reduce computational effort. This allows it to find initial solutions up to an order-of-magnitude faster than state-of-the-art planning algorithms on the tested abstract and robotic multiquery planning problems.

Due to ligament laxity, bearing dislocation occurs in 1–6% of Oxford Domed Lateral (ODL) replacements with most dislocations occurring medially. Dislocations were studied using a previously built mechanical rig, however testing using the rig was inefficient. The aim of this study was to develop a better tool that was more reliable and efficient. An established robotics software package, the Open Motion Planning Library, was modified to accept the ODL components. Using a robotics path planning algorithm, the mobile bearing was allowed to find a way out from between the femoral and tibial components i.e. to dislocate. Testing assessed a range of clinically relevant positions of the femoral component relative to the tibial component. Dislocations were labelled as medial, lateral, anterior or posterior depending on the dislocation direction. The Distraction to Dislocation (DD) measured the minimum vertical distraction of the femoral component from the tibial component for a dislocation to occur. Results were validated against the mechanical rig. Statistical analysis of medial dislocation showed excellent agreement with an intraclass correlation value of 0.993 (95% CI 0.982–0.998). All DDs from the dislocation analysis tool were within 1 mm of the mechanical rig DDs with results sharing a remarkably similar trend. The robotics dislocation analysis tool output DDs which were marginally higher than the manual mechanical rig: 0.50 mm anteriorly, 0.25 mm posteriorly and 0.50 mm laterally. Medially, the computational DD differed on average by 0.09 mm (stand deviation: 0.2026 mm). Our study describes the development and validation of a novel robotics dislocation analysis tool, which allows mobile bearing dislocation risk quantification. The tool may also be used to improve surgical implantation parameters and to assess new implant designs that aim to reduce the medial dislocation risk to an acceptable level.

Navigating unknown structures (e.g., buildings or caves) can be a dangerous and challenging task for emergency responders. This risk can be reduced by capturing detailed 3D maps of unseen environments with a robotic sensor platform and using them to assess and visualise the unknown structures. Many existing approaches require offline processing or human oversight to produce high-fidelity maps, which precludes their use in real-time scenarios with limited communication. This paper investigates the use of an autonomous aerial mapping solution that could capture detailed 3D maps of unknown structures in real-time without requiring human intervention.

Introduction Ligament laxity in the lateral knee enables dislocation of the mobile bearing in 1–6% of Oxford Domed Lateral (ODL) Unicompartmental Knee Replacements (UKRs). Although dislocations can occur anteriorly or posteriorly, they usually occur medially with the bearing sitting above the wall of the tibial component. Dislocations were previously studied using a custom built mechanical rig, however, testing using the rig was inefficient. The aim of this study was to develop a more reliable and efficient dislocation analysis tool. Methods The established robotics software package, the Open Motion Planning Library (OMPL) was modified to accept the ODL components. Starting with the components positioned with the bearing flush against the tibial wall, the femoral component was distracted away from the tibial component: vertically (2–8 mm) and mediolaterally (ML) (0–6 mm) in 0.25 mm increments (425 configurations). Using a robotics path planning algorithm called Rapidly-Exploring Random Trees (RRT) the mobile bearing was allowed to find a way out from between the femoral and tibial components i.e. to dislocate. Dislocations were labelled as medial, lateral, anterior or posterior depending on the dislocation direction. For each ML distance, the vertical distraction required for dislocation (VDD) was recorded. To improve the tool’s efficiency, a convergence test was run to identify the optimal search time, no. of searches. The tool was also made more agile by implementing directional searching and selectively increasing the search capacity. To validate the tool, the VDD results were compared to measurements taken using a custom-built mechanical rig. Medial VDD results were statistically compared using a Intraclass Correlation Coefficient: mean rating (k=2), two way random effects model with type consistency and 95% Confidence Interval (CI). Results Section Using the RRT algorithm, the tool successfully identified dislocations medially, laterally, anteriorly and posteriorly. Convergence testing found that at least 10 search attempts and 210 seconds was required to identify all solutions. However, within 10 search attempts and 45 seconds search time, most of the dislocation solutions had been found. Based on this, for each configuration, the RRT algorithm was allowed to run for 270 seconds and 10 searches. If no solution was found in this time, the search was repeated for the same configuration but with the time and search attempts extended to 405 seconds and 25 searches, respectively. For medial VDD, as ML translation increased from 0 mm to 6 mm, the VDD medially of the mobile bearing reduces: 5.5 mm to 2.75 mm for the mechanical rig and from 5.5 mm to 3.25 mm for the robotics tool. For lateral, anterior and posterior dislocations, the robotics dislocation analysis tool output VDDs which were marginally higher than the manual mechanical rig: 3.5 mm, 6.25 mm and 6.25 mm versus 2.75 mm, 5.75 mm and 6 mm respectively. The ICC for medial VDD was 0.993 (95%CI: 0.982–0.998). Discussion Our study demontrates a novel and successful application of a robotics path planning algorithm, RRT, to the clinical problem of mobile bearing dislocation, to develop a reliable and efficient robotics dislocation analysis tool. The results from the tool reveal that the amount of distraction for a medial dislocation (0–6 mm ML: 5.5–3.25 mm) was much smaller than that for an anterior or posterior dislocation (6.25 mm). This explains why medial dislocations are more common clinically. Even though the amount of distraction required for lateral dislocation was even less than that required for a medial dislocation, when the lateral compartment is distracted the lateral ligament and other soft tissues are tight, preventing a lateral dislocation. Anterior and posterior bearing dislocation rarely occur and this dislocation rate is clinically acceptable. Future work will use the tool to test whether modifications to the design of the implant e.g. increasing the height of the tibial component wall successfully reduce the risk of medial dislocations. More broadly, the tool may be used to inform the implant design to reduce medial dislocation risk to match that of anterior/posterior dislocation, which would reduce the dislocation risk to an acceptable level. Significance/Clinical Relevance Using the novel dislocation analysis tool developed in this study, medial dislocation required less distraction than either anterior or posterior dislocation, possibly explaining why medial dislocations are most common clinically. If the VDD medially could be reduced to match the VDD anteriorly or posteriorly, the dislocation risk would likely be acceptable.

Optimal path planning is the problem of finding a valid sequence of states between a start and goal that optimizes an objective. Informed path planning algorithms order their search with problem-specific knowledge expressed as heuristics and can be orders of magnitude more efficient than uninformed algorithms. Heuristics are most effective when they are both accurate and computationally inexpensive to evaluate, but these are often conflicting characteristics. This makes the selection of appropriate heuristics difficult for many problems. This paper presents two almost-surely asymptotically optimal sampling-based path planning algorithms to address this challenge, Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*). These algorithms use an asymmetric bidirectional search in which both searches continuously inform each other. This allows AIT* and EIT* to improve planning performance by simultaneously calculating and exploiting increasingly accurate, problem-specific heuristics. The benefits of AIT* and EIT* relative to other sampling-based algorithms are demonstrated on 12 problems in abstract, robotic, and biomedical domains optimizing path length and obstacle clearance. The experiments show that AIT* and EIT* outperform other algorithms on problems optimizing obstacle clearance, where a priori cost heuristics are often ineffective, and still perform well on problems minimizing path length, where such heuristics are often effective.

High-level autonomy requires discrete and continuous reasoning to decide both what actions to take and how to execute them. Integrated Task and Motion Planning (TMP) algorithms solve these hybrid problems jointly to consider constraints between the discrete symbolic actions (i.e., the task plan) and their continuous geometric realization (i.e., motion plans). This joint approach solves more difficult problems than approaches that address the task and motion subproblems independently. TMP algorithms combine and extend results from both task and motion planning. TMP has mainly focused on computational performance and completeness and less on solution optimality. Optimal TMP is difficult because the independent optima of the subproblems may not be the optimal integrated solution, which can only be found by jointly optimizing both plans. This letter presents Task and Motion Informed Trees (TMIT*), an optimal TMP algorithm that combines results from makespan-optimal task planning and almost-surely asymptotically optimal motion planning. TMIT* interleaves asymmetric forward and reverse searches to delay computationally expensive operations until necessary and perform an efficient informed search directly in the problem's hybrid state space. This allows it to solve problems quickly and then converge towards the optimal solution with additional computational time, as demonstrated on the evaluated robotic-manipulation benchmark problems.

Due to lateral ligament laxity, bearing dislocation occurs in 1%-6% of Oxford Domed Lateral replacements. Most dislocations are medial but they do rarely occur anteriorly or posteriorly. The aim was to decrease the risk of dislocation. For a bearing to dislocate the femoral component has to be distracted from the tibial component. A robotic-path-planning-algorithm was used with a computer model of the implant in different configurations to determine the Vertical Distraction needed for Dislocation (VDD). With current components, VDD anteriorly/posteriorly was 5.5 to 6.5 mm and medially was 3.5 to 5.75 mm. A thicker bearing increased VDD medially and decreased VDD anteriorly/posteriorly (0.1 mm/1 mm thickness increase). VDD medially increased with the bearing closer to the tibial wall (0.5 mm/1 mm closer), or by increasing the tibial wall height (1 mm/1 mm height increase). VDD anteriorly/posteriorly was not influenced by bearing position or wall height. To prevent collision between the femoral and tibial components an increase in wall height must be accompanied by a similar increase in minimum bearing thickness. Increasing the wall height and minimum bearing thickness by 2 mm and ensuring the bearing is 4 mm or less from the wall increased the minimum VDD medially to 5.5 mm. The lower VDD medially than anteriorly/posteriorly explains why medial dislocation is more common. If the wall height is increased by 2 mm, the minimum bearing thickness is 5 mm and the surgeon ensured the bearing is 4 mm or less from the wall, the medial dislocation rate should be similar to the anterior/posterior dislocation rate, which should be acceptable.

Background: The Oxford Domed Lateral (ODL) Unicompartmental Knee Replacement (UKR) has some advantages over other lateral UKRs, but the mobile bearing dislocation rate is high (1–6%). Medial dislocations, with the bearing lodged on the tibial component wall, are most common. Anterior/posterior dislocations are rare. For a dislocation to occur distraction of the joint is required. We have developed and validated a dislocation analysis tool based on a computer model of the ODL with a robotics path-planning algorithm to determine the Vertical Distraction required for a Dislocation (VDD), which is inversely related to the risk of dislocation. Objectives: To modify the ODL design so the risk of medial dislocation decreases to that of an anterior/posterior dislocation. Methods: The components were modified using Solidworks. For each modification the dislocation analysis tool was used to determine the VDD for medial dislocation (with bearing 0–6mm from the tibial wall). This was compared with the original implant to identify the modifications that were most effective at reducing the dislocation risk. These modifications were combined into a final design, which was assessed. Results: Modifying the tibial component plateau, changing the femoral component width and making the bearing wider medially had little effect on VDD. Shifting the femoral sphere centre medially decreased VDD. Shifting the femoral sphere laterally, increasing tibial wall height and increasing bearing width laterally increased VDD. A modified implant with a femoral sphere centre 3mm lateral, wall 2.8mm higher, and bearing 2mm wider laterally, implanted so the bearing is l4mm from the tibial wall with a bearing thickness g4mm had a minimum VDD for medial dislocation of 5.75mm, which is larger than the minimum VDD for anterior/posterior dislocation of 5.5mm. Conclusions: A modified ODL design should decrease the dislocation rate to an acceptable level, however, further testing in cadavers is required.

Optimal path planning is the problem of finding a valid sequence of states between a start and goal that optimizes an objective. Informed path planning algorithms order their search with problem-specific knowledge expressed as heuristics and can be orders of magnitude more efficient than uninformed algorithms. Heuristics are most effective when they are both accurate and computationally inexpensive to evaluate, but these are often conflicting characteristics. This makes the selection of appropriate heuristics difficult for many problems. This paper presents two almost-surely asymptotically optimal sampling-based path planning algorithms to address this challenge, Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*). These algorithms use an asymmetric bidirectional search in which both searches continuously inform each other. This allows AIT* and EIT* to improve planning performance by simultaneously calculating and exploiting increasingly accurate, problem-specific heuristics. The benefits of AIT* and EIT* relative to other sampling-based algorithms are demonstrated on twelve problems in abstract, robotic, and biomedical domains optimizing path length and obstacle clearance. The experiments show that AIT* and EIT* outperform other algorithms on problems optimizing obstacle clearance, where a priori cost heuristics are often ineffective, and still perform well on problems minimizing path length, where such heuristics are often effective.

Obstacle clearance in state space is an important optimization objective in path planning because it can result in safe paths. This technical report presents admissible solution- and path-cost heuristics for this objective, which can be used to improve the performance of informed path planning algorithms.

Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal pathquality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This article summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.

Path planning in robotics often requires finding high-quality solutions to continuously valued and/or high-dimensional problems. These problems are challenging and most planning algorithms instead solve simplified approximations. Popular approximations include graphs and random samples, as used by informed graph-based searches and anytime sampling-based planners, respectively. Informed graph-based searches, such as A * , traditionally use heuristics to search a priori graphs in order of potential solution quality. This makes their search efficient, but leaves their performance dependent on the chosen approximation. If the resolution of the chosen approximation is too low, then they may not find a (suitable) solution, but if it is too high, then they may take a prohibitively long time to do so. Anytime sampling-based planners, such as RRT * , traditionally use random sampling to approximate the problem domain incrementally. This allows them to increase resolution until a suitable solution is found, but makes their search dependent on the order of approximation. Arbitrary sequences of random samples approximate the problem domain in every direction simultaneously, but may be prohibitively inefficient at containing a solution. This article unifies and extends these two approaches to develop Batch Informed Trees (BIT*), an informed, anytime sampling-based planner. BIT * solves continuous path planning problems efficiently by using sampling and heuristics to alternately approximate and search the problem domain. Its search is ordered by potential solution quality, as in A * , and its approximation improves indefinitely with additional computational time, as in RRT * . It is shown analytically to be almost-surely asymptotically optimal and experimentally to outperform existing sampling-based planners, especially on high-dimensional planning problems.

Visual motion estimation is an integral and well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation, which is especially challenging in highly dynamic environments. Such environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion. Previous work in object tracking focuses on maintaining the integrity of object tracks but usually relies on specific appearance-based descriptors or constrained motion models. These approaches are very effective in specific applications but do not generalize to the full multimotion estimation problem. This paper presents a pipeline for estimating multiple motions, including the camera egomotion, in the presence of occlusions. This approach uses an expressive motion prior to estimate the SE(3) trajectory of every motion in the scene, even during temporary occlusions, and identify the reappearance of motions through motion closure. The performance of this occlusion-robust multimotion visual odometry (MVO) pipeline is evaluated on real-world data and the Oxford Multimotion Dataset.

Informed sampling-based planning algorithms exploit problem knowledge for better search performance. This knowledge is often expressed as heuristic estimates of solution cost and used to order the search. The practical improvement of this informed search depends on the accuracy of the heuristic.Selecting an appropriate heuristic is difficult. Heuristics applicable to an entire problem domain are often simple to define and inexpensive to evaluate but may not be beneficial for a specific problem instance. Heuristics specific to a problem instance are often difficult to define or expensive to evaluate but can make the search itself trivial.This paper presents Adaptively Informed Trees (AIT*), an almost-surely asymptotically optimal sampling-based planner based on BIT*. AIT* adapts its search to each problem instance by using an asymmetric bidirectional search to simultaneously estimate and exploit a problem-specific heuristic. This allows it to quickly find initial solutions and converge towards the optimum. AIT* solves the tested problems as fast as RRT-Connect while also converging towards the optimum.