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Multi-arm mobile manipulators can be represented as a combination of multiple robotic agents from the perspective of task-assignment and motion planning. Depending upon the task, agents might collaborate or work independently. Integrating motion planning with task-agent assignment is a computationally slow process as infeasible assignments can only be detected through expensive motion planning queries. We present three speed-up techniques for addressing this problem-(1) spatial constraint checking using conservative surrogates for motion planners, (2) instantiating symbolic conditions for pruning infeasible assignments, and (3) efficiently caching and reusing previously generated motion plans. We show that the developed method is useful for real-world operations that require complex interaction and coordination among high-DOF robotic agents.

We present a context-dependent bi-directional tree-search framework for point-to-point path planning for manipulators. Conceptually, our framework is composed of six modules: tree selection, focus selection, node selection, target selection, extend selection and connection type selection. Each module consists of a set of interchangeable strategies. By exploiting synergistic interaction between these strategies and selecting appropriate strategies based the contextual cues from the search state, we show an instance of our framework that computes high-quality solutions in a variety of complex scenarios with a low failure rate. We also show that some popular path planning methods in the literature can be easily represented in our framework. We compare our approach with these popular methods in a diverse set of test scenarios. We report a 15-fold reduction in failure rate coupled with at least a 26% drop in solution suboptimality when compared to the best of the alternative methods.

We present a two-layered architecture for task-agent assignment and motion planning of Bi-manual Mobile manipulators for executing complex tasks. We use search trees in temporal windows to determine feasible task assignments of agents using task and spatial constraint-based heuristics. We also introduce a caching scheme for moving between different trees so as to avoid re-planning of those portions of the robot motion that were successful. This greatly reduces the number of calls to the motion planner as compared to direct motion planning after task-agent assignment. We have shown our approach works on a set of complex tasks with significantly lower computation times.

Low production volume applications cannot use custom fixtures to reduce uncertainties in part locations. Traditional tool-path planners for area coverage applications may not perform satisfactorily when there is significant uncertainty in part locations because these planners do not account for the tool-part contact and impedance controller parameters during path execution. This may lead to tool collision with the part, incorrect force at the Tool Center Point (TCP), and loss of contact while performing area coverage operations. Moreover, the poorly designed tool-path and incorrectly estimated impedance control parameters also lead to poor task performance. To avoid such failures, we concurrently design a collision-free tool-paths, generate executable robot trajectory, and select the impedance compensation parameters so that it can take care of part uncertainties and provide the desired tool-part contact. We apply the designed algorithm for area coverage manufacturing applications. Finally, we evaluate the algorithm performance by conducting physical experiments.

Successfully completing a complex manufacturing task requires finding a feasible placement of the workpiece in the robot workspace. The workpiece placement should be such that the task surfaces on the workpiece are reachable by the robot, the robot can apply the required forces, and the end-effector/tool can move with the desired velocity. This paper formulates the problem of identifying a feasible placement as a non-linear optimization problem over the constraint violation functions. This is a computationally challenging problem. We show that this problem can be solved by successively searching for the solution by incrementally applying different constraints. We demonstrate the feasibility of our approach using several complex workpieces.

Extrusion-based additive manufacturing (AM), also known as fused deposition modeling (FDM) extrudes filaments through a heated nozzle and builds a part layer-by-layer. Using a smaller diameter nozzle can achieve better surface finish. However, there is a trade-off between surface finish and build times as using a small diameter nozzle leads to smaller layer thickness and long build times. Traditional FDM printers create a part with planar layers, and this restricts control over fiber orientations. This paper presents a robotic cell for multi-resolution AM. The cell consists of two 6 degrees of freedom (DOF) robot manipulators capable of printing non-planar and/or planar layers. We describe algorithms for decomposing parts into multi-resolution layers and generating collision-free trajectories for the robot manipulators. We validate our approach by printing five parts with multi-resolution.

We pose the problem of path-constrained trajectory generation for the synchronous motion of multi-robot systems as a non-linear optimization problem. Our method determines appropriate parametric representation for the configuration variables, generates an approximate solution as a starting point for the optimization method, and uses successive refinement techniques to solve the problem in a computationally efficient manner. We have demonstrated the effectiveness of the proposed method on challenging simulation and physical experiments with high degrees of freedom robotic systems.

Many practical operations like search and rescue operations are time-sensitive missions. In this paper, we present a wave-aware deliberative planner that avoids collisions with traffic vessels and also opportunistically traverses wavefields generated by these vessels while minimizing the execution time and the risk of failure. The planner performs a search over a 4D pose-time lattice to generate a collision-free, minimum-risk sequence of motion goals. It addresses motion uncertainty, failure risk and perception uncertainty. We also present heuristics and adaptive motion goals to speed-up computation. We present simulation results showing that our wave-aware planner produces plans that execute faster compared to the typical reactive planning scheme. We also present results from physical experiments showing the feasibility of the proposed approach.

This paper presents a new trajectory planning algorithm based on context-dependent search strategy switching for manipulators working in confined workspaces. This problem has an inherently complex and large search space. Finding good solutions to this problem requires developing an efficient search strategy using appropriately-designed motion primitives and heuristic functions to guide the search. The algorithm presented in this paper monitors the search progress and uses different search strategies in different parts of the search space. This allows it to solve manipulator trajectory planning problems for highly-confined workspaces. It is also able to seek input from the human user when search heuristics are unable to provide guidance to the search process. We present results on challenging example parts and compare them with the previously reported approaches.

Warehouses and factories are beginning to deploy mobile manipulators for transporting parts between machines and work stations. Minimizing the number of these robots on the shop floor requires that each robot should complete the assigned task in a time-optimal manner and therefore maximize the robot's capacity. In this paper we present a search-based algorithm for generating time-optimal trajectories for picking and transporting parts using a mobile manipulator. The approach is based on a hierarchical search that uses discrete search for planning of the mobile base. The time cost due to the motion of the manipulator for picking up the part is evaluated and integrated with the search state space at the appropriate times. The trajectories generated result in picking up of the part with the manipulator while the mobile base is in motion. The performance of the algorithm is studied for different factory sizes and obstacle densities. This approach enables the mobile manipulator to perform pick and transport operation in a smaller time duration compared to the operation where it stops to pick up the object.

We propose a hybrid human-robot cell for reducing labor in multi-layer prepreg composite sheet layup process. The human expert performs initial experiments to assess the process sequence, and the direction of the layup. The expert knowledge is then transferred to the system in the form of high level commands which are converted into low-level collision free trajectories for the robots in the cell. This paper demonstrates the feasibility of the composite sheet layup using the robotic manipulators along with the design of end-effector tools that are used during the layup process. We give a comparison between manual and hybrid cell for execution times in different steps involved. Finally, we give an overview of process consistency benefits of the hybrid cell.

We present a model-predictive trajectory planning algorithm for following a target boat by an autonomous unmanned surface vehicle (USV) in an environment with static obstacle regions and civilian boats. The planner developed in this work is capable of making a balanced trade-off among the following, possibly conflicting criteria: the risk of losing the target boat, trajectory length, risk of collision with obstacles, violation of the Coast Guard Collision Regulations (COLREGs), also known as “rules of the road”, and execution of avoidance maneuvers against vessels that do not follow the rules. The planner addresses these criteria by combining a search for a dynamically feasible trajectory to a suitable pose behind the target boat in 4D state space, forming a time-extended lattice, and reactive planning that tracks this trajectory using control actions that respect the USV dynamics and are compliant with COLREGs. The reactive part of the planner represents a generalization of the velocity obstacles paradigm by computing obstacles in the control space using a system-identified, dynamic model of the USV as well as worst-case and probabilistic predictive motion models of other vessels. We present simulation and experimental results using an autonomous unmanned surface vehicle platform and a human-driven vessel to demonstrate that the planner is capable of fulfilling the above mentioned criteria.