With the onset of the COVID-19 pandemic, faculty are suddenly thrown into a world where they have to teach virtually, forcing them to innovate in their teaching practice without the ability to “chat with their colleagues” next door. Additionally, many faculty do not know how to create an inclusive classroom, which is crucial for students who have also lost their learning community and support structure. Many faculty are afraid to include issues of inclusion and diversity, feeling ill equipped and unsupported. As tensions rise across the US, there is a critical need for engineering students to be able to discuss issues involving inclusion in the classroom as well as apply their abilities as engineers to make a significant impact (both positively or negatively) on inclusion through their applications and innovations. Without the proper support for the faculty to innovate and foster inclusive classrooms, millions of students will suffer in their education. Additionally, without a trusting support group, most engineering faculty would not choose to assume the risk. In response, we developed a community of practice with six teaching fellows who shared, watched, commented on, and emulated each other's classroom recordings. The teaching fellows learned a significant amount from each other, courageously implemented activities focused on inclusion and awareness in their classrooms, observed greater awareness among their students on inclusion issues, and were able to mentor faculty and publish about the data driven best practices that they completed.

This Innovative Practice Full Paper presents a collaborative course design and delivery model that supports teamwork among faculty, tracks course alignment throughout the development process, and fosters a greater variety and distribution of student engagement opportunities, even when the collaborative relationship must be virtual. Multiple perspectives, when used effectively, enhance course design and delivery, but collaboration among faculty can be challenging without a structured process. In addition, establishing alignment between learning outcomes and course activities is increasingly difficult. We show the benefits of this approach through deployment in a collaboratively redesigned engineering course. The resulting course has improved alignment of learning objectives and activities across all modules, provides a wider set of student engagement opportunities, and gives a more consistent instructional voice among the different delivering faculty.

We present a new general framework for disassembly sequence planning. This framework is versatile allowing different types of search schemes (exhaustive vs. preemptive), various part separation techniques, and the ability to group parts, or not, into subassemblies to improve the solution efficiency and parallelism. This enables a truly hierarchical approach to disassembly sequence planning. We demonstrate two different search strategies using this framework that can either yield a single solution quickly or provide a spectrum of solutions from which an optimal may be selected. We also develop a method for subassembly identification based on collision information. Our results show improved performance over an iterative motion planning based method for finding a single solution and greater functionality through hierarchical planning and optimal solution search.

Nearest-neighbor finding is a major bottleneck for sampling-based motion planning algorithms. The cost of finding nearest neighbors grows with the size of the roadmap, leading to significant slowdowns for problems which require many configurations to find a solution. Prior work has investigated relieving this pressure with quicker computational techniques, such as kd-trees or locality-sensitive hashing. In this work, we investigate an alternative direction for expediting this process based on workspace connectivity. We present an algorithm called Topological Nearest-Neighbor Filtering, which employs a workspace decomposition to select a topologically relevant set of candidate neighbor configurations as a pre-processing step for a nearest-neighbor algorithm. We investigate the application of this filter to several varieties of RRT and demonstrate that the filter improves both nearest-neighbor time and overall planning performance.

Motion planning for manipulators with rotational joints is challenging because the actuation range for each link is constrained by the placement and orientation of other links. Thus, finding paths that avoid self-collision is non-trivial. However, rotational joints are often used in industrial robots. We develop a reparameterization of the planning problem called directed reachable volumes that provides an explicit representation of the workspace regions that the joints and end effectors can reach given the placement and orientation of other links. This formulation, while similar in spirit to prior reachable volume work, does not rely on the same restrictive assumptions that preclude prior work from handling rotational joints. We provide primitive planning operations that can be used in the context of state-of-the-art motion planning methods. We present experimental validation of directed reachable volumes by demonstrating a simulated pick-and-place scenario using realistic robots with rotational joints.

Sampling-based motion planning is the state-of-the-art technique for solving challenging motion planning problems in a wide variety of domains. While generally successful, their performance suffers from increasing problem complexity. In many cases, the full problem complexity is not needed for the entire solution. We present a hierarchical aggregation framework that groups and models sets of obstacles based on the currently needed level of detail. The hierarchy enables sampling to be performed using the simplest and most conservative representation of the environment possible in that region. Our results show that this scheme improves planner performance irrespective of the underlying sampling method and input problem. In many cases, improvement is significant, with running times often less than 60% of the original planning time.

Motivation: Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms such as Probabilistic Roadmap Methods (PRMs) have been successful in modeling the protein folding landscape. PRMs and variants contain several phases (i.e., sampling, connection, and path extraction). Global machine learning has been applied to the connection phase but is inefficient in situations with varying topology, such as those typical of folding landscapes. Results: We present a local learning algorithm that considers the past performance near the current connection attempt as a basis for learning. It is sensitive not only to different types of landscapes but also to differing regions in the landscape itself, removing the need to explicitly partition the landscape. We perform experiments on 23 proteins of varying secondary structure makeup with 52-114 residues. Our method models the landscape with better quality and comparable time to the best performing individual method and to global learning.

Reachable volumes are a new technique that allows one to efficiently restrict sampling to feasible/reachable regions of the planning space even for high degree of freedom and highly constrained problems. However, they have so far only been applied to graph-based sampling-based planners. In this paper we develop the methodology to apply reachable volumes to tree-based planners such as Rapidly-Exploring Random Trees (RRTs). In particular, we propose a reachable volume RRT called RVRRT that can solve high degree of freedom problems and problems with constraints. To do so, we develop a reachable volume stepping function, a reachable volume expand function, and a distance metric based on these operations. We also present a reachable volume local planner to ensure that local paths satisfy constraints for methods such as PRMs. We show experimentally that RVRRTs can solve constrained problems with as many as 64 degrees of freedom and unconstrained problems with as many as 134 degrees of freedom. RVRRTs can solve problems more efficiently than existing methods, requiring fewer nodes and collision detection calls. We also show that it is capable of solving difficult problems that existing methods cannot.

Probabilistic Roadmap Methods (PRMs) solve the motion planing problem by constructing a roadmap (or graph) that models the motion space when feasible local motions exist. PRMs and variants contain several phases during roadmap generation i.e., sampling, connection, and query. Some work has been done to apply machine learning to the connection phase to decide which variant to employ, but it uses a global learning approach that is inefficient in heterogeneous situations. We present an algorithm that instead uses local learning: it only considers the performance history in the vicinity of the current connection attempt and uses this information to select good candidates for connection. It thus removes any need to explicitly partition the environment which is burdensome and typically difficult to do. Our results show that our method learns and adapts in heterogeneous environments, including a KUKA youBot with a fixed and mobile base. It finds solution paths faster for single and multi-query scenarios and builds roadmaps with better coverage and connectivity given a fixed amount of time in a wide variety of input problems. In all cases, our method outperforms the previous adaptive connection method and is comparable or better than the best individual method.

Motion planning is a difficult and widely studied problem in robotics. Current research aims not only to find feasible paths, but to ensure paths have certain properties, e.g., shortest or safest paths. This is difficult for current state-of-the-art sampling-based techniques as they typically focus on simply finding any path. Despite this difficulty, sampling-based techniques have shown great success in planning for a wide range of applications. Among such planners, Rapidly-Exploring Random Trees (RRTs) search the planning space by biasing exploration toward unexplored regions. This paper introduces a novel RRT variant, Medial Axis RRT (MARRT), which biases tree exploration to the medial axis of free space by pushing all configurations from expansion steps towards the medial axis. We prove that this biasing increases the tree's clearance from obstacles. Improving obstacle clearance is useful where path safety is important, e.g., path planning for robots performing tasks in close proximity to the elderly. Finally, we experimentally analyze MARRT, emphasizing its ability to effectively map difficult passages while increasing obstacle clearance, and compare it to contemporary RRT techniques.

We introduce a new concept, reachable volumes, that denotes the set of points that the end effector of a chain or linkage can reach. We show that the reachable volume of a chain is equivalent to the Minkowski sum of the reachable volumes of its links, and give an efficient method for computing reachable volumes. We present a method for generating configurations using reachable volumes that is applicable to various types of robots including open and closed chain robots, tree-like robots, and complex robots including both loops and branches. We also describe how to apply constraints (both on end effectors and internal joints) using reachable volumes. Unlike previous methods, reachable volumes work for spherical and prismatic joints as well as planar joints. Visualizations of reachable volumes can allow an operator to see what positions the robot can reach and can guide robot design. We present visualizations of reachable volumes for representative robots including closed chains and graspers as well as for examples with joint and end effector constraints.

Maintaining clearance, or distance from obstacles, is a vital component of successful motion planning algorithms. Maintaining high clearance often creates safer paths for robots. Contemporary sampling-based planning algorithms that utilize the medial axis, or the set of all points equidistant to two or more obstacles, produce higher clearance paths. However, they are biased heavily toward certain portions of the medial axis, sometimes ignoring parts critical to planning, e.g., specific types of narrow passages. We introduce Uniform Medial Axis Probabilistic RoadMap (UMAPRM), a novel planning variant that generates samples uniformly on the medial axis of the free portion of Cspace. We theoretically analyze the distribution generated by UMAPRM and show its uniformity. Our results show that UMAPRM's distribution of samples along the medial axis is not only uniform but also preferable to other medial axis samplers in certain planning problems. We demonstrate that UMAPRM has negligible computational overhead over other sampling techniques and can solve problems the others could not, e.g., a bug trap. Finally, we demonstrate UMAPRM successfully generates higher clearance paths in the examples.

Reachable volumes are a geometric representation of the regions the joints of a robot can reach. They can be used to generate constraint satisfying samples for problems including complicated linkage robots (e.g. closed chains and graspers). They can also be used to assist robot operators and to help in robot design.We show that reachable volumes have an O(1) complexity in unconstrained problems as well as in many constrained problems. We also show that reachable volumes can be computed in linear time and that reachable volume samples can be generated in linear time in problems without constraints. We experimentally validate reachable volume sampling, both with and without constraints on end effectors and/or internal joints. We show that reachable volume samples are less likely to be invalid due to self-collisions, making reachable volume sampling significantly more efficient for higher dimensional problems. We also show that these samples are easier to connect than others, resulting in better connected roadmaps. We demonstrate that our method can be applied to 262-dof, multi-loop, and tree-like linkages including combinations of planar, prismatic and spherical joints. In contrast, existing methods either cannot be used for these problems or do not produce good quality solutions.

Rapidly-exploring Random Tree (RRT), like other sampling-based motion planning methods, has been very successful in solving motion planning problems. Even so, sampling-based planners cannot solve all problems of interest efficiently, so attention is increasingly turning to parallelizing them. However, one challenge in parallelizing RRT is the global computation and communication overhead of nearest neighbor search, a key operation in RRTs. This is a critical issue as it limits the scalability of previous algorithms. We present two parallel algorithms to address this problem. The first algorithm extends existing work by introducing a parameter that adjusts how much local computation is done before a global update. The second algorithm radially subdivides the configuration space into regions, constructs a portion of the tree in each region in parallel, and connects the subtrees,i removing cycles if they exist. By subdividing the space, we increase computation locality enabling a scalable result. We show that our approaches are scalable. We present results demonstrating almost linear scaling to hundreds of processors on a Linux cluster and a Cray XE6 machine.

Probabilistic Roadmap Methods (PRMs) are widely used motion planning methods that sample robot configurations (nodes) and connect them to form a graph (roadmap) containing feasible trajectories. Many PRM variants propose different strategies for each of the steps and choosing among them is problem dependent. Planning in heterogeneous environments and/or on parallel machines necessitates dividing the problem into regions where these choices have to be made for each one. Hand-selecting the best method for each region becomes infeasible. In particular, there are many ways to select connection candidates, and choosing the appropriate strategy is input dependent. In this paper, we present a general connection framework that adaptively selects a neighbor finding strategy from a candidate set of options. Our framework learns which strategy to use by examining their success rates and costs. It frees the user of the burden of selecting the best strategy and allows the selection to change over time. We perform experiments on rigid bodies of varying geometry and articulated linkages up to 37 degrees of freedom. Our results show that strategy performance is indeed problem/region dependent, and our adaptive method harnesses their strengths. Over all problems studied, our method differs the least from manual selection of the best method, and if one were to manually select a single method across all problems, the performance can be quite poor. Our method is able to adapt to changing sampling density and learns different strategies for each region when the problem is partitioned for parallelism.

Rapidly-Exploring Random Trees (RRTs) have been successful at finding feasible solutions for many types of problems. With motion planning becoming more computationally demanding, we turn to parallel motion planning for efficient solutions. Existing work on distributed RRTs has been limited by the overhead that global communication requires. A recent approach, Radial RRT, demonstrated a scalable algorithm that subdivides the space into regions to increase the computation locality. However, if an obstacle completely blocks RRT growth in a region, the planning space is not covered and is thus not probabilistically complete. We present a new algorithm, Blind RRT, which ignores obstacles during initial growth to efficiently explore the entire space. Because obstacles are ignored, free components of the tree become disconnected and fragmented. Blind RRT merges parts of the tree that have become disconnected from the root. We show how this algorithm can be applied to the Radial RRT framework allowing both scalability and effectiveness in motion planning. This method is a probabilistically complete approach to parallel RRTs. We show that our method not only scales but also overcomes the motion planning limitations that Radial RRT has in a series of difficult motion planning tasks.

This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine.

This paper presents a new sampling method for motion planning that can generate configurations more uniformly distributed on C-obstacle surfaces than prior approaches. Here, roadmap nodes are generated from the intersections between C-obstacles and a set of uniformly distributed fixed-length segments in C-space. The results show that this new sampling method yields samples that are more uniformly distributed than previous obstacle-based methods such as OBPRM, Gaussian sampling, and Bridge test sampling. UOBPRM is shown to have nodes more uniformly distributed near C-obstacle surfaces and also requires the fewest nodes and edges to solve challenging motion planning problems with varying narrow passages.

Since planning environments are complex and no single planner exists that is best for all problems, much work has been done to explore methods for selecting where and when to apply particular planners. However, these two questions have been difficult to answer, even when adaptive methods meant to facilitate a solution are applied. For example, adaptive solutions such as setting learning rates, hand-classifying spaces, and defining parameters for a library of planners have all been proposed. We demonstrate a strategy based on unsupervised learning methods that makes adaptive planning more practical. The unsupervised strategies require less user intervention, model the topology of the problem in a reasonable and efficient manner, can adapt the sampler depending on characteristics of the problem, and can easily accept new samplers as they become available. Through a series of experiments, we demonstrate that in a wide variety of environments, the regions automatically identified by our technique represent the planning space well both in number and placement. We also show that our technique has little overhead and that it out-performs two existing adaptive methods in all complex cases studied.

Motion planning for closed-chain systems is particularly difficult due to additional closure constraints placed on the system. In fact, the probability of randomly selecting a set of joint angles that satisfy the closure constraints is zero. We propose planning with reachable distance (PRD) to overcome this challenge by first precomputing the subspace satisfying the closure constraints, then directly sampling in it. To do so, we represent the chain as a hierarchy of sub-chains. Then we calculate the "closure" sub-space as appropriate reachable distance ranges of sub-chains satisfying the closure constraints. This provides two distinct advantages over traditional approaches: (1) configurations are quickly sampled and converted to joint angles using basic trigonometry functions instead of more expensive inverse kinematics solvers, and (2) configurations are guaranteed to be closed. In this paper, we describe this hierarchical chain representation and give a sampling algorithm with complexity linear in the number of links. We provide the necessary motion planning primitives for most sampling-based motion planners. Our experimental results show our method is fast, making sampling closed configurations comparable to sampling open chain configurations that ignore closure constraints. Our method is general, easy to implement, and also extends to other distance-related constraints besides the ones demonstrated here

With the success of randomized sampling-based motion planners such as probabilistic roadmap methods, much work has been done to design new sampling techniques and distributions. To date, there is no sampling technique that outperforms all other techniques for all motion planning problems. Instead, each proposed technique has different strengths and weaknesses. However, little work has been done to combine these techniques to create new distributions. In this paper, we propose to bias one sampling distribution with another such that the resulting distribution out-performs either of its parent distributions. We present a general framework for biasing samplers that is easily extendable to new distributions and can handle an arbitrary number of parent distributions by chaining them together. Our experimental results show that by combining distributions, we can out-perform existing planners. Our results also indicate that not one single distribution combination performs the best in all problems, and we identify which perform better for the specific application domains studied

Abstract The protein‐folding problem is a study of how a protein dynamically folds to its so‐called native state—an energetically stable, three‐dimensional conformation. Understanding this process is of great practical importance since some devastating diseases such as Alzheimer's and bovine spongiform encephalopathy (Mad Cow) are associated with the misfolding of proteins. We have developed a new computational technique for studying protein folding that is based on probabilistic roadmap methods for motion planning. Our technique yields an approximate map of a protein's potential energy landscape that contains thousands of feasible folding pathways. We have validated our method against known experimental results. Other simulation techniques, such as molecular dynamics or Monte Carlo methods, require many orders of magnitude more time to produce a single, partial trajectory. In this paper we report on our experiences parallelizing our method using STAPL (Standard Template Adaptive Parallel Library) that is being developed in the Parasol Lab at Texas A&M. An efficient parallel version will enable us to study larger proteins with increased accuracy. We demonstrate how STAPL enables portable efficiency across multiple platforms, ranging from small Linux clusters to massively parallel machines such as IBM's BlueGene/L, without user code modification. Copyright © 2005 John Wiley & Sons, Ltd.

Summary form only given. The protein folding problem is to study how a protein dynamically folds to its so-called native state - an energetically stable, three-dimensional configuration. Understanding this process is of great practical importance since some devastating diseases such as Alzheimer's and bovine spongiform encephalopathy (Mad Cow) are associated with the misfolding of proteins. In our group, we have developed a new computational technique for studying protein folding that is based on probabilistic roadmap methods for motion planning. Our technique yields an approximate map of a protein's potential energy landscape that contains thousands of feasible folding pathways. We have validated our method against known experimental results. Other simulation techniques, such as molecular dynamics or Monte Carlo methods, require many orders of magnitude more time to produce a single, partial, trajectory. We report on our experiences parallelizing our method using STAPL (the standard template adaptive parallel library), that is being developed in the Parasol Lab at Texas A&M. An efficient parallel version enables us to study larger proteins with increased accuracy. We demonstrate how STAPL enables portable efficiency across multiple platforms without user code modification. We show performance gains on two systems: a dedicated Linux cluster and an extremely heterogeneous multiuser Linux cluster.

We propose a novel, motion planning based approach to approximately map the energy landscape of an RNA molecule. Our method is based on the successful probabilistic roadmap motion planners that we have previously successfully applied to protein folding. The key advantage of our method is that it provides a sparse map that captures the main features of the landscape and which can be analyzed to compute folding kinetics. In this paper, we provide evidence that this approach is also well suited to RNA. We compute population kinetics and transition rates on our roadmaps using the master equation for a few moderately sized RNA and show that our results compare favorably with results of other existing methods.

We propose a general framework for sampling the configuration space in which randomly generated configurations, free or not, are retracted onto the medial axis of the free space. Generalizing our previous work, this framework provides a template encompassing all possible retraction approaches. It also removes the requirement of exactly computing distance metrics thereby enabling application to more realistic high dimensional problems. In particular, our framework supports methods that retract a given configuration exactly or approximately onto the medial axis. As in our previous work, exact methods provide fast and accurate retraction in low (2 or 3) dimensional space. We also propose new approximate methods that can be applied to high dimensional problems, such as many DOF articulated robots. Theoretical and experimental results show improved performance on problems requiring traversal of narrow passages. We also study tradeoffs between accuracy and efficiency for different levels of approximation, and how the level of approximation effects the quality of the resulting roadmap.

An important property of PRM roadmaps is that they provide a good approximation of the connectivity of the free C-space. We present a general framework for building and querying probabilistic roadmaps that includes all previous PRM variants as special cases. In particular, it supports no, complete, or partial node and edge validation and various evaluation schedules for path validation, and it enables path customization for variable, adaptive query requirements. While each of the above features is present in some PRM variant, the general framework proposed here is the only one to include them all. Our framework enables users to choose the best approximation level for their problem. Our experimental evidence shows this can result in significant performance gains.