Multi-robot grasp planning for sequential assembly operations

This paper addresses the problem of finding robot configurations to grasp assembly parts during a sequence of collaborative assembly operations. We formulate the search for such configurations as a constraint satisfaction problem (CSP).Collision constraints in an operation and transfer constraints between operations determine the sets of feasible robot configurations. We show that solving the connected constraint graph with off-the-shelf CSP algorithms can quickly become infeasible even fora few sequential assembly operations. We present an algorithm which, through the assumption of feasible regrasps, divides the CSP into independent smaller problems that can be solved exponentially faster. The algorithm then uses local search techniques to improve this solution by removing a gradually increasing number of regrasps from the plan. The algorithm enables the user to stop the planner anytime and use the current best plan if the cost of removing regrasps from the plan exceeds the cost of executing those regrasps. We present simulation experiments to compare our algorithm’s performance toa naive algorithm which directly solves the connected constraint graph. We also present a physical robot system which uses the output of our planner to grasp and bring parts together in assembly configurations

Real-Time Terminal Area Trajectory Planning for Runway Independent Aircraft

The increasing demand for commercial air transportation results in delays due to traffic queues that form bottlenecks along final approach and departure corridors. In urban areas, it is often infeasible to build new runways, and regardless of automation upgrades traffic must remain separated to avoid the wakes of previous aircraft. Vertical or short takeoff and landing aircraft as Runway Independent Aircraft (RIA) can increase passenger throughput at major urban airports via the use of vertiports or stub runways. The concept of simultaneous non-interfering (SNI) operations has been proposed to reduce traffic delays by creating approach and departure corridors that do not intersect existing fixed-wing routes. However, SNI trajectories open new routes that may overfly noise-sensitive areas, and RIA may generate more noise than traditional jet aircraft, particularly on approach. In this dissertation, we develop efficient SNI noise abatement procedures applicable to RIA. First, we introduce a methodology based on modified approximated cell-decomposition and Dijkstra's search algorithm to optimize longitudinal plane (2-D) RIA trajectories over a cost function that minimizes noise, time, and fuel use. Then, we extend the trajectory optimization model to 3-D with a k-ary tree as the discrete search space. We incorporate geography information system (GIS) data, specifically population, into our objective function, and focus on a practical case study: the design of SNI RIA approach procedures to Baltimore-Washington International airport. Because solutions were represented as trim state sequences, we incorporated smooth transition between segments to enable more realistic cost estimates. Due to the significant computational complexity, we investigated alternative more efficient optimization techniques applicable to our nonlinear, non-convex, heavily constrained, and discontinuous objective function. Comparing genetic algorithm (GA) and adaptive simulated annealing (ASA) with our original Dijkstra's algorithm, ASA is identified as the most efficient algorithm for terminal area trajectory optimization. The effects of design parameter discretization are analyzed, with results indicating a SNI procedure with 3-4 segments effectively balances simplicity with cost minimization. Finally, pilot control commands were implemented and generated via optimization-base inverse simulation to validate execution of the optimal approach trajectories

On learning task-directed motion plans

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Includes bibliographical references (p. 119-129).Robotic motion planning is a hard problem for robots with more than just a few degrees of freedom. Modern probabilistic planners are able to solve many problems very quickly, but for difficult problems, they are still unacceptably slow for many applications. This thesis concerns the use of previous planning experience to allow the agent to generate motion plans very quickly when faced with new but related problems. We first investigate a technique for learning from previous experience by simply remembering past solutions and applying them where relevant to new problems. We find that this approach is useful in environments with very low variability in obstacle placement and task endpoints, and that it is important to keep the set of stored plans small to improve performance. However, we would like to be able to better generalize our previous experience so we next investigate a technique for learning parameterized motion plans. A parameterized motion plan is a function from planning problem parameters to a motion plan. In our approach, we learn a set of parameterized subpaths, which we can use as suggestions for a probabilistic planner, leading to substantially reduced planning times. We find that this technique is successful in several standard motion planning domains. However, as the domains get more complex, the technique produces less of an advantage. We discover that the learning problem as we have posed it is likely to be intractible, and that the complexity of the problem is due to the redundancy of the robotics platform. We suggest several possible approaches for addressing this problem as future work.by Sarah J. Finney.Ph.D

Learning-Assisted Multi-Step Planning

Probabilistic sampling-based motion planners are unable to detect when no feasible path exists. A common heuristic is to declare a query infeasible if a path is not found in a fixed amount of time. In applications where many queries must be processed -- for instance, robotic manipulation, multilimbed locomotion, and contact motion -- a critical question arises: what should this time limit be? This paper presents a machine-learning approach to deal with this question. In an off-line learning phase, a classifier is trained to quickly predict the feasibility of a query. Then, an improved multi-step motion planning algorithm uses this classifier to avoid wasting time on infeasible queries. This approach has been successfully demonstrated in simulation on a four-limbed, free-climbing robot