Trauma caused by grasping during laparoscopic surgery is something which will never be fully eradicated however efforts should be taken to reduce the potential to cause trauma by grasping. Tissue is often grasped with excessive forces for long periods of time during surgeries such as cholecystectomies and colectomies. This along with failed grasping actions and the occurrence of slip has been shown to damage the tissue. Design features often employed within graspers such as profiling and the occlusion mechanism of the instrument cause areas of high, uneven distribution of pressures on the tissue which can result in perforation or tissue tearing. By investigating these contributing factors, development of graspers with a low risk to cause damage this combined with actuating the grasping force should reduce the incidence of grasping trauma, currently at estimated at one incidence per procedure. These trauma events can lead to conversion to open surgery, peritonitis and even death. Development of an autonomous grasping instrument to detect and prevent slip by actuating the grasping force is reported. Piezoelectric sensors are used to detect incipient slip and slip events. A closed loop control system then reacts to these perceived slip events to prevent slip occurring by actuating the applied force by small increments to increase or decrease grasping force. This leads to a system in which only the required amount of force necessary to overcome pull force is applied to the tissue. Other areas of investigation to reduce tissue trauma are presented. In chapter 3 design features such as surface profiling and fenestrations are evaluated to determine the potential to cause damage. A variety of profiles and fenestrations are studied and each is reported by representing the applied force to retention force ratio which indicates how good the profile is at retaining tissue against a pull force. The aim of this study was to develop surface profiling which had a high retention force but a reduced number of high stress areas which can lead to tissue damage. Three new parallel action grasping designs are presented and evaluated using finite element analysis. Parallel action grasping is important in reducing tissue trauma as it distributes pressure evenly across the active grasping area as opposed to more conventional pivot style graspers which have high stress concentration areas in the proximal opening. Each area of study within the thesis addresses areas of concern which have been shown to cause tissue trauma and postulates viable solutions to reduce the incidences of tissue trauma during laparoscopic surgery with the ultimate aim of developing a deployable and autonomous grasping device which will detect and prevent slip.

Since the advent of machine learning and machine vision methods, progress has been made in tackling the long-standing research question of autonomous grasping of arbitrary objects using robotic end-effectors. Building on these efforts, we focus on a subset of the general grasping problem concerning the automation of a forwarder. This forestry vehicle collects and transports felled and cut tree logs in a forest environment to a nearby roadside landing. The forwarder must safely and energy-efficiently grip logs to minimize fuel consumption and reduce loading times. In this thesis project, we develop a data-driven model for predicting the expected outcome of grasping attempts made by the forwarder's crane. For a given pile of logs, such a model can estimate the optimal horizontal location and angle for applying the claw grapple, enabling effective grasp planning. We utilize physics-based simulations to create a ground truth dataset of 12 500 000 simulated grasps distributed across 5000 randomly generated log piles. Our semi-generative, supervised model is a fully convolutional network that inputs the orthographic depth image of a pile and returns images predicting the corresponding grasps' initial grapple angle and outcome metrics as a function of position. Over five folds of cross-validation, our model predicted the number of grasped logs and the initial grapple angle with a normalized root mean squared error of 15.77(2)% and 2.64(4)%, respectively. The grasps' energy efficiency and energy waste were similarly predicted with a relative error of 14.43(2)% and 21.06(3)%. / Sedan tillkomsten av maskininlärnings- och maskinseendebaserade metoder har betydande framsteg gjorts inom forskningsområdet för autonom greppning av godtyckliga objekt med en robotisk sluteffektor. Vi bygger vidare på dessa resultat och fokuserar på en del av det generella greppningsproblemet gällande automatisering av en skotare. Denna skogsmaskin samlar in och transporterar fällda och kapade trädstammar från avverkningsplats till upplag intill närliggande skogsbilväg. Skotaren måste greppa och lyfta stockarna på ett säkert och energieffektivt sätt för att minimera bränsleförbrukningen samt minska lastningstiderna. I detta examensarbete utvecklar vi en datadriven modell för att förutsäga det förväntade resultatet av gripförsök utförda av skotarens kran. För en given timmerstockshög kan en sådan modell uppskatta den optimala positionen och vinkeln för att applicera skotarens gripklo, vilket möjliggör effektiv planering av lastningen. Vi använder fysikbaserade simuleringar för att skapa ett dataset med 12 500 000 simulerade gripförsök fördelade över 5000 slumpmässigt genererade timmerhögar. Vår semi-generativa, övervakade modell är ett djupt faltningsnätverk utan helt sammankopplade neuronlager som tar in en ortografisk djupbild av en timmerhög och returnerar bilder som predikterar de motsvarande gripförsökens initiala gripvinkel och resultatmått som en funktion av position. Vid en femfaldig korsvalidering förutsåg vår modell antalet greppade stockar och den initiala gripvinkeln med ett normaliserat rotmedelkvadratfel på 15.77(2)% respektive 2.64(4)%. Gripförsökens energieffektivitet och energiförlust predikterades på liknande sätt med ett relativt fel på 14.43(2)% och 21.06(3)%.